KR102601998B1 - Kit for diagnosing acute tumor response of cervical cancer - Google Patents
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Abstract
본 발명은 자궁경부암의 급성 종양 반응 진단용 바이오 마커 및 이의 용도에 관한 것으로, 보다 구체적으로 항암방사선 치료를 받는 자궁경부암 환자들로부터 치료 전/후의 혈액에서 분리한 엑소좀 내에 포함된 microRNA를 분석한 결과, 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 관련된 마이크로 RNA들로 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 마이크로 RNA 3960(hsa-miR-3960), 마이크로 RNA 202-5p(hsa-miR-202-5p), 마이크로 RNA 3928-5p(hsa-miR-3928-5p), 및 마이크로 RNA 574-3p(hsa-miR-574-3p)를 발굴하고, DIABLO(Diablo homolog)의 mRNA, UBC(Ubiquitin C)의 mRNA, 및 SIN3A(SIN3 transcription regulator family member A)의 mRNA와 관련됨을 확인하였는 바, 상기 마이크로 RNA들 및 mRNA를 포함하는 자궁경부암의 급성 종양 반응 진단용 바이오 마커 조성물, 자궁경부암의 급성 종양 반응 진단용 조성물, 자궁경부암 급성 종양 반응 진단용 키트, 자궁경부암의 급성 종양 반응 진단을 위한 정보 제공 방법을 제공한다.The present invention relates to a biomarker for diagnosing acute tumor response in cervical cancer and its use. More specifically, the results of analyzing microRNA contained in exosomes isolated from the blood of cervical cancer patients receiving anti-cancer radiation treatment before and after treatment. , micro RNAs related to acute tumor response (AR) in cervical cancer include micro RNA 92a-1-5p (hsa-miR-92a-1-5p), micro RNA 3960 (hsa-miR-3960), Discovered micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), and micro RNA 574-3p (hsa-miR-574-3p), DIABLO (Diablo homolog) mRNA, UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA were confirmed to be used for diagnosing acute tumor response in cervical cancer containing the above-mentioned micro RNAs and mRNA. Provides a biomarker composition, a composition for diagnosing acute tumor response in cervical cancer, a kit for diagnosing acute tumor response in cervical cancer, and a method of providing information for diagnosing acute tumor response in cervical cancer.
Description
본 발명은 자궁경부암의 급성 종양 반응 진단용 바이오 마커 및 이의 용도에 관한 것이다.The present invention relates to a biomarker for diagnosing acute tumor response in cervical cancer and its use.
자궁경부암(cervical cancer)은 여성에게 빈번하게 발생되는 악성 종양으로, 매년 전세계적으로 35만 명 이상의 환자가 자궁경부암으로 사망하고 있다. 자궁경부암은 0기에서 4기까지 나뉘고 0기 암을 상피내암이라고도 하며, 1기부터 4기 암을 침윤성 자궁경부 암이라고 한다. 상피내암은 침윤성 자궁경부암보다 10년 정도 빠르게 35~40세 정도에서 발병하며 침윤성 자궁경부암은 30세 이후부터 증가하기 시작하여 50대에 정점에 달한 후 급격히 감소하는 경향을 보이는 것으로 알려져 있다. 국내의 경우 자궁경부암의 유병률 수준은 10만 명당 31명 정도이고 사망률은 10만 명당 6.8명 수준으로 지난 10년간 큰 변화를 보이지 않았다. 발생률과 유병률의 빈도를 연령별로 보면 50대, 60대, 40대의 순이며, 발생률은 미국의 3배, 일본의 2.5배, 브라질의 1/3 수준이며 자궁경부암으로 인한 사망률이 암 사망률에서 차지하는 비율이 6%로 미국, 일본, 스위스 등의 2% 내외에 비하여 높은 실정이다. 현재 자궁경부암 발생에 관해서는 성적 접촉성 감염질환 모델이 가장 널리 인정되고 있으며 조기에 시작된 성적 활동, 다수의 성교 상대자, 남성 요인, 인유두종 바이러스(human papillomavirus, HPV) 감염, 에이즈 바이러스(human immunodeficiency virus) 감염 등이 자궁경부암 발생의 위험 요인으로 알려져 있다.Cervical cancer is a malignant tumor that frequently occurs in women, and more than 350,000 patients worldwide die from cervical cancer every year. Cervical cancer is divided into stages 0 to 4. Stage 0 cancer is also called intraepithelial cancer, and stage 1 to 4 cancer is called invasive cervical cancer. Intraepithelial cancer develops around the age of 35 to 40, about 10 years earlier than invasive cervical cancer, and invasive cervical cancer is known to start increasing after the age of 30, peak in the 50s, and then show a sharp decline. In Korea, the prevalence level of cervical cancer is about 31 per 100,000 people and the mortality rate is about 6.8 per 100,000 people, showing no significant changes over the past 10 years. Looking at the frequency of incidence and prevalence by age, it is in the order of those in their 50s, 60s, and 40s. The incidence rate is 3 times that of the United States, 2.5 times that of Japan, and 1/3 that of Brazil. Mortality due to cervical cancer accounts for a proportion of cancer mortality. This 6% is higher than the around 2% in the United States, Japan, and Switzerland. Currently, the sexually transmitted infectious disease model is most widely accepted for the development of cervical cancer, with early onset sexual activity, multiple sexual partners, male factors, human papillomavirus (HPV) infection, and human immunodeficiency virus. Infections are known to be risk factors for cervical cancer.
자궁경부암의 치료 후 재발률은 1년 내 50%, 2년 내 25%, 3년 내 15% 정도로서, 약 85%의 환자가 3년내 재발을 한다. 특히 자궁경부암의 방사선 치료 후의 재발율 또한 55%가 넘는다. 재발한 자궁경부암의 치료는 기존에 받았던 치료의 종류와 환자의 상태에 따라 다르게 결정되는데, 일반적으로 1차 치료로 수술을 했다면 재발시에는 방사선 치료를, 1차 치료로 방사선 치료를 했다면 수술을 고려하며, 두 가지 치료가 모두 불가능할 경우 항암화학요법을 시행하게 된다. 그러나 표준적인 치료법은 없으며 재발부위에 따라 각각에 대한 적절한 치료를 실시하는데, 일반적으로 병을 고치는 것보다 증상을 경감시기는 것을 목적으로 하는 치료법이다.The recurrence rate of cervical cancer after treatment is approximately 50% within 1 year, 25% within 2 years, and 15% within 3 years, with approximately 85% of patients experiencing recurrence within 3 years. In particular, the recurrence rate after radiation treatment for cervical cancer also exceeds 55%. Treatment for recurrent cervical cancer is determined differently depending on the type of treatment previously received and the patient's condition. In general, if surgery was the primary treatment, radiation therapy is considered in case of recurrence, and if radiation therapy was the primary treatment, surgery is considered. If both treatments are not possible, chemotherapy is performed. However, there is no standard treatment, and appropriate treatment is administered depending on the site of recurrence. In general, the treatment is aimed at alleviating symptoms rather than curing the disease.
현재 자궁경부암에서 급성 치료 반응의 평가를 영상도구에 많이 의존하고 있으며 보조적으로 SCC ag, Cyfra21-1 과 같은 종양표지자를 사용하고 있지만 방사선 치료 반응을 예측하기에는 부족하다. 이를 보완할 수 있다면 추가적인 방사선 선량의 증가 및 감소 등의 효과적인 치료전략의 수립에 기여할 수 있을 것이다. 이러한 임상적 문제를 해결하기 위해서는 방사선 치료 반응 예측을 위한 정확한 도구가 필요하다. 임상결과들은 단순하지만 그 이면에는 복잡한 생물학적인 의미를 내포하고 있으므로 좀 더 유의미한 상위레벨의 접근방법이 필요하다.Currently, the evaluation of acute treatment response in cervical cancer relies heavily on imaging tools, and tumor markers such as SCC ag and Cyfra21-1 are used as auxiliary methods, but they are insufficient to predict radiotherapy response. If this can be complemented, it will be possible to contribute to the establishment of effective treatment strategies such as increasing or decreasing additional radiation dose. To solve these clinical problems, accurate tools for predicting radiotherapy response are needed. Although clinical results are simple, they have complex biological meaning behind them, so a more meaningful, high-level approach is needed.
한편, 엑소좀(Exosome)은 암세포 혹은 다른 정상세포에 분비되는 40-150 nm의 세포외 소포(extracellular vesicles)로써, 세포와 세포간의 신호전달을 통한 여러 생리적 기능을 수행하며 암의 발생, 전이, 면역력, 염증 그리고 스트레스 반응의 조절에 관여하는 것으로 알려져 있다. 이들은 특이 non coding/coding RNA들을 포함하고 있으며 여러 coding RNA를 조절하는 miRNA가 이러한 기능들을 조절하는 핵심역할을 한다. 따라서 exosome miRNA는 현 시점에서 생각해 볼 수 있는 상위 레벨의 조절인자로서 고려될 수 있다. 특히 혈액 안에는 암세포뿐만 아니라 정상세포에서 기원한 exosome들도 풍부하기 때문에 암환자들의 신체적 반응과 암세포 간의 관련성을 알기 용이하며 동시에 miRNA와 coding gene 사이의 연관성을 살펴보는 것도 가능하다.Meanwhile, exosomes are extracellular vesicles of 40-150 nm that are secreted by cancer cells or other normal cells. They perform various physiological functions through signal transmission between cells and play a role in the development, metastasis, and development of cancer. It is known to be involved in the regulation of immunity, inflammation, and stress response. They contain specific non-coding/coding RNAs, and miRNAs that regulate several coding RNAs play a key role in regulating these functions. Therefore, exosome miRNAs can be considered as high-level regulators that can be considered at the present time. In particular, because blood is rich in exosomes originating from not only cancer cells but also normal cells, it is easy to understand the relationship between the physical responses of cancer patients and cancer cells, and at the same time, it is possible to examine the relationship between miRNAs and coding genes.
본 발명은 자궁경부암의 급성 종양 반응 진단용 바이오 마커 및 이의 용도에 관한 것으로, 본 발명자들은 항암방사선 치료를 받는 자궁경부암 환자들 중에서 치료 전/후로 혈액 샘플을 채취하고, 이로부터 혈장 엑소좀(exosome) RNA를 분리한 후, NGS 분석을 통해 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 관련된 마이크로 RNA들을 검출하고, 이들을 자궁경부암의 급성 종양 반응 진단용 바이오 마커로 제공하는 것이다.The present invention relates to a biomarker for diagnosing acute tumor response in cervical cancer and its use. The present inventors collect blood samples from cervical cancer patients receiving anti-cancer radiation treatment before and after treatment, and extract plasma exosomes from these. After separating RNA, micro RNAs related to acute tumor response (AR) of cervical cancer are detected through NGS analysis, and these are provided as biomarkers for diagnosing acute tumor response of cervical cancer.
본 발명은 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 및 DIABLO(Diablo homolog)의 mRNA로 이루어진 군에서 선택된 하나 이상을 포함하는 자궁경부암의 급성 종양 반응 진단용 바이오 마커 조성물을 제공한다.The present invention provides a biomarker composition for diagnosing acute tumor response in cervical cancer comprising at least one selected from the group consisting of micro RNA 92a-1-5p (hsa-miR-92a-1-5p) and mRNA of DIABLO (Diablo homolog). provides.
또한, 본 발명은 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 및 DIABLO(Diablo homolog)의 mRNA로 이루어진 군에서 선택된 하나 이상의 RNA의 발현 수준을 측정하는 제제를 포함하는 자궁경부암의 급성 종양 반응 진단용 조성물을 제공한다.In addition, the present invention includes an agent for measuring the expression level of one or more RNAs selected from the group consisting of mRNA of micro RNA 92a-1-5p (hsa-miR-92a-1-5p) and DIABLO (Diablo homolog). A composition for diagnosing acute tumor response in cervical cancer is provided.
또한, 본 발명은 상기 급성 종양 반응 진단용 조성물을 포함하는 자궁경부암 급성 종양 반응 진단용 키트를 제공한다.Additionally, the present invention provides a kit for diagnosing acute tumor response in cervical cancer, including the composition for diagnosing acute tumor response.
또한, 본 발명은 대상체 유래의 생물학적 시료에서 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 또는 DIABLO(Diablo homolog)의 mRNA의 발현 수준을 측정하는 단계를 포함하는 자궁경부암의 급성 종양 반응 진단을 위한 정보 제공 방법을 제공한다.In addition, the present invention provides a method for treating cervical cancer, comprising measuring the expression level of mRNA of microRNA 92a-1-5p (hsa-miR-92a-1-5p) or DIABLO (Diablo homolog) in a biological sample derived from a subject. Provides an informational method for diagnosing acute tumor reactions.
본 발명에 따르면, 항암방사선 치료를 받는 자궁경부암 환자들로부터 치료 전/후의 혈액에서 분리한 엑소좀 내에 포함된 microRNA를 분석한 결과, 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 관련된 마이크로 RNA들로 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 마이크로 RNA 3960(hsa-miR-3960), 마이크로 RNA 202-5p(hsa-miR-202-5p), 마이크로 RNA 3928-5p(hsa-miR-3928-5p), 및 마이크로 RNA 574-3p(hsa-miR-574-3p)를 발굴하고, DIABLO(Diablo homolog)의 mRNA, UBC(Ubiquitin C)의 mRNA, 및 SIN3A(SIN3 transcription regulator family member A)의 mRNA와 관련됨을 확인하였는 바, 상기 마이크로 RNA들 및 mRNA를 자궁경부암의 급성 종양 반응(acute tumor response, AR)을 진단하는 수단으로 활용될 수 있으며, 치료 및 수술 후의 예후를 예측하는데 유용한 바이오 마커로써 임상에서 효율적으로 이용될 것으로 기대된다.According to the present invention, as a result of analyzing microRNA contained in exosomes isolated from the blood before and after treatment of cervical cancer patients receiving anticancer radiation therapy, microRNAs related to acute tumor response (AR) of cervical cancer were found. RNAs include micro RNA 92a-1-5p (hsa-miR-92a-1-5p), micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), and micro RNA 574-3p (hsa-miR-574-3p) were discovered, mRNA of DIABLO (Diablo homolog), mRNA of UBC (Ubiquitin C), and SIN3A. As it was confirmed to be related to the mRNA of (SIN3 transcription regulator family member A), the micro RNAs and mRNA can be used as a means of diagnosing acute tumor response (AR) in cervical cancer, and can be used for treatment and surgery. It is expected to be used efficiently in clinical practice as a useful biomarker for predicting future prognosis.
도 1은 자궁경부암의 급성 종양 반응(acute tumor response, AR)에 관련된 마이크로 RNA들 및 mRNA의 메커니즘을 나타내는 그림이다.
도 2은 항암 방사선 치료를 시행한 자궁경부암 환자들에 대한 치료과정, 혈액의 채취 시점, MRI 촬영 시점, 및 추적 관찰 과정을 설명한 모식도이다.
도 3는 종양에 대한 방사선 치료 반응을 측정하기 위한 원발 부위 MRI 촬영 사진이다.
도 4은 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 유의한 연관성을 가진 miRNA(log2FC)들의 서브그룹(Subgroup) 분석을 통한 miRNA의 선별 결과이다.
도 5는 상기 도 4에서 선별된 miRNA의 상관관계를 분석한 결과이다.
도 6는 선별된 miRNA 군 및 그들과 연관된 miRNA들을 네트워크(network)로 분석한 결과이다.
도 7은 선별된 miRNA 군과 연관 mRNA들 간의 구조를 파악한 network 분석한 결과이다.
도 8은 각 임상 요소와 높은 관련성이 있는 mRNA (A) 및 네트워크(network) 분석을 통해 얻어진 miRNA 및 mRNA(B)를 포함하여 관련 유전자들을 선별한 결과다.
도 9은 자궁경부암의 급성 종양 반응(acute tumor response, AR)에 관련된 miRNA와 mRNA들을 IPA(Ingenuity pathway analysis) 분석한 결과이다.
도 10는 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 관련된 4개의 함수 범주(function category)들의 벤다이어그램을 그린 결과이다.
도 11은 상기 도 10에서 재선별된 mRNA 및 miRNA들을 네트워크(network)로 분석한 결과이다.
도 12은 상기 도 11에 포함된 RNA들의 log2FC 값들을 IPA database로 분석하여 자궁경부암의 급성 종양 반응(acute tumor response, AR) 여부에 따라 통계적으로 유의한 차이를 나타내는 범주들을 분석한 결과이다.
도 13는 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 관련된 주 miRNA에 대하여 선별된 범주들의 상대적 비율을 그래프로 나타낸 결과이다.
도 14은 자궁경부암의 급성 종양 반응(acute tumor response, AR)과 관련된 주 miRNA의 변화에 따라 유의미하게 변화된 mRNA 및 miRNA들을 그래프로 나타낸 결과이다.
도 15는 도 11의 mRNA들을 대상으로 자궁경부암의 급성 종양 반응(acute tumor response, AR)에 대한 서브그룹을 분석한 결과이다.
도 16는 상기 도 16에서 선별된 miRNA의 상관관계를 분석한 결과이다.Figure 1 is a diagram showing the mechanisms of microRNAs and mRNA involved in the acute tumor response (AR) of cervical cancer.
Figure 2 is a schematic diagram explaining the treatment process, blood collection time, MRI imaging time, and follow-up process for cervical cancer patients who underwent anti-cancer radiation therapy.
Figure 3 is an MRI image of the primary site for measuring the radiation treatment response to the tumor.
Figure 4 shows the results of selection of miRNAs through subgroup analysis of miRNAs (log2FC) with significant correlation with acute tumor response (AR) of cervical cancer.
Figure 5 shows the results of analyzing the correlation between the selected miRNAs in Figure 4.
Figure 6 shows the results of network analysis of selected miRNA groups and their associated miRNAs.
Figure 7 shows the results of network analysis identifying the structure between the selected group of miRNAs and related mRNAs.
Figure 8 shows the results of selecting related genes, including mRNA (A), which is highly related to each clinical factor, and miRNA and mRNA (B) obtained through network analysis.
Figure 9 shows the results of IPA (Ingenuity pathway analysis) analysis of miRNAs and mRNAs related to acute tumor response (AR) in cervical cancer.
Figure 10 is the result of drawing a Venn diagram of four function categories related to acute tumor response (AR) of cervical cancer.
Figure 11 shows the results of network analysis of the mRNAs and miRNAs reselected in Figure 10.
Figure 12 shows the results of analyzing the log2FC values of the RNAs included in Figure 11 using the IPA database to analyze categories showing statistically significant differences depending on whether there was an acute tumor response (AR) in cervical cancer.
Figure 13 is a graphical representation of the relative proportions of selected categories for major miRNAs related to acute tumor response (AR) in cervical cancer.
Figure 14 is a graphical representation of significantly changed mRNA and miRNA according to changes in major miRNAs related to acute tumor response (AR) in cervical cancer.
Figure 15 shows the results of subgroup analysis of acute tumor response (AR) in cervical cancer targeting the mRNAs in Figure 11.
Figure 16 shows the results of analyzing the correlation between the selected miRNAs in Figure 16.
본 명세서에서 사용되는 용어는 본 발명에서의 기능을 고려하면서 가능한 현재 널리 사용되는 일반적인 용어들을 선택하였으나, 이는 당 분야에 종사하는 기술자의 의도 또는 판례, 새로운 기술의 출현 등에 따라 달라질 수 있다. 또한, 특정한 경우는 출원인이 임의로 선정한 용어도 있으며, 이 경우 해당되는 발명의 설명 부분에서 상세히 그 의미를 기재할 것이다. 따라서 본 발명에서 사용되는 용어는 단순한 용어의 명칭이 아닌, 그 용어가 가지는 의미와 본 발명의 전반에 걸친 내용을 토대로 정의되어야 한다.The terms used in this specification are general terms that are currently widely used as much as possible while considering the function in the present invention, but this may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than simply the name of the term.
다르게 정의되지 않는 한, 기술적이거나 과학적인 용어를 포함해서 여기서 사용되는 모든 용어들은 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자에 의해 일반적으로 이해되는 것과 동일한 의미를 가지고 있다. 일반적으로 사용되는 사전에 정의되어 있는 것과 같은 용어들은 관련 기술의 문맥상 가지는 의미와 일치하는 의미를 가지는 것으로 해석되어야 하며, 본 출원에서 명백하게 정의하지 않는 한, 이상적이거나 과도하게 형식적인 의미로 해석되지 않는다.Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless clearly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.
수치 범위는 상기 범위에 정의된 수치를 포함한다. 본 명세서에 걸쳐 주어진 모든 최대의 수치 제한은 낮은 수치 제한이 명확히 쓰여져 있는 것처럼 모든 더 낮은 수치 제한을 포함한다. 본 명세서에 걸쳐 주어진 모든 최소의 수치 제한은 더 높은 수치 제한이 명확히 쓰여져 있는 것처럼 모든 더 높은 수치 제한을 포함한다. 본 명세서에 걸쳐 주어진 모든 수치 제한은 더 좁은 수치 제한이 명확히 쓰여져 있는 것처럼, 더 넓은 수치 범위 내의 더 좋은 모든 수치 범위를 포함할 것이다.The numerical range includes the values defined in the range above. Every maximum numerical limit given throughout this specification includes all lower numerical limits as if the lower numerical limit were explicitly written out. Every minimum numerical limit given throughout this specification includes every higher numerical limit as if such higher numerical limit was clearly written. All numerical limits given throughout this specification will include all better numerical ranges within the broader numerical range, as if the narrower numerical limits were clearly written.
이하, 본 발명을 보다 상세하게 설명한다.Hereinafter, the present invention will be described in more detail.
본 발명자들은 혈장 엑소좀 miRNA가 항암방사선 치료를 시행한 환자들에게서 나타나는 자궁경부암의 급성 종양 반응(acute tumor response, AR)을 예측할 수 있는 바이오 마커인지 확인하기 위해, 방사선 치료 전/후(2주)간의 혈장 엑소좀 miRNA를 분석하여 fold change를 통해 임상 변수가 나타나는 miRNA를 검출하였다.To confirm that plasma exosomal miRNA is a biomarker that can predict acute tumor response (AR) of cervical cancer in patients who underwent anticancer radiation therapy, the present inventors examined the plasma exosomes before and after radiation therapy (2 weeks). ) Liver plasma exosomal miRNAs were analyzed to detect miRNAs that showed clinical variables through fold change.
본 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 및 DIABLO(Diablo homolog)의 mRNA로 이루어진 군에서 선택된 하나 이상을 포함하는 자궁경부암의 급성 종양 반응 진단용 바이오 마커 조성물을 제공한다.Provides a biomarker composition for diagnosing acute tumor response in cervical cancer containing at least one selected from the group consisting of this micro RNA 92a-1-5p (hsa-miR-92a-1-5p) and mRNA of DIABLO (Diablo homolog) do.
상기 자궁경부암의 급성 종양 반응(acute tumor response, AR)은 방사선 치료에 따라 종양이 빠르게 감소하는 정도로 그 값이 높을 수록 나쁜 치료 반응을 낮을 수로 좋은 치료 반응을 의미한다. 치료반응 여부를 예측할 수 있다면 환자별로 조사하고자 하는 방사선량을 조절하여 방사선 치료로 인한 정상조직의 손상을 최소화하면서 종양을 조절할 수 있는 치료 효율을 높일 수 있을 것이며, 방사선 저항성을 가지는 종양의 치료 반응을 향상시키기 위한 치료제의 개발을 위한 실마리를 제공할 수 있다.The acute tumor response (AR) of cervical cancer refers to the degree to which the tumor rapidly decreases following radiation treatment. The higher the value, the lower the bad treatment response, and the better the treatment response. If we can predict the response to treatment, we will be able to control the radiation dose for each patient and increase the treatment efficiency to control the tumor while minimizing the damage to normal tissue caused by radiation treatment. It can provide clues for the development of treatments to improve
상기 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p)는 서열번호 1로 표시되는 염기서열로 이루어지고, 상기 DIABLO(Diablo homolog)의 mRNA는 서열번호 2으로 표시되는 염기서열로 이루어진다.The micro RNA 92a-1-5p (hsa-miR-92a-1-5p) consists of a base sequence shown in SEQ ID NO: 1, and the mRNA of the DIABLO (Diablo homolog) consists of a base sequence shown in SEQ ID NO: 2. It comes true.
상기 바이오 마커 조성물은 마이크로 RNA 3960(hsa-miR-3960), 마이크로 RNA 202-5p(hsa-miR-202-5p), 마이크로 RNA 3928-5p(hsa-miR-3928-5p), 마이크로 RNA 574-3p(hsa-miR-574-3p), UBC(Ubiquitin C)의 mRNA, 및 SIN3A(SIN3 transcription regulator family member A)의 mRNA로 이루어진 군에서 선택된 하나 이상을 추가로 포함할 수 있다.The biomarker composition includes micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), micro RNA 574- It may further include one or more selected from the group consisting of 3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA.
상기 마이크로 RNA 3960(hsa-miR-3960)는 서열번호 3으로 표시되는 염기서열로 이루어지고, 상기 마이크로 RNA 202-5p(hsa-miR-202-5p)는 서열번호 4로 표시되는 염기서열로 이루어지고, 상기 마이크로 RNA 3928-5p(hsa-miR-3928-5p)는 서열번호 5로 표시되는 염기서열로 이루어지고, 상기 마이크로 RNA 574-3p(hsa-miR-574-3p)는 서열번호 6으로 표시되는 염기서열로 이루어지고, 상기 UBC(Ubiquitin C)의 mRNA는 서열번호 7로 표시되는 염기서열로 이루어지고, 상기 SIN3A(SIN3 transcription regulator family member A)의 mRNA는 서열번호 8로 표시되는 염기서열로 이루어진다.The micro RNA 3960 (hsa-miR-3960) consists of the base sequence shown in SEQ ID NO: 3, and the micro RNA 202-5p (hsa-miR-202-5p) consists of the base sequence shown in SEQ ID NO: 4. In addition, the micro RNA 3928-5p (hsa-miR-3928-5p) consists of the base sequence shown in SEQ ID NO: 5, and the micro RNA 574-3p (hsa-miR-574-3p) has SEQ ID NO: 6. It consists of the base sequence shown, and the UBC (Ubiquitin C) mRNA consists of the base sequence shown in SEQ ID NO: 7, and the SIN3A (SIN3 transcription regulator family member A) mRNA consists of the base sequence shown in SEQ ID NO: 8. It consists of
또한, 본 발명은 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 및 DIABLO(Diablo homolog)의 mRNA로 이루어진 군에서 선택된 하나 이상의 RNA의 발현 수준을 측정하는 제제를 포함하는 자궁경부암의 급성 종양 반응 진단용 조성물을 제공한다.In addition, the present invention includes an agent for measuring the expression level of one or more RNAs selected from the group consisting of mRNA of micro RNA 92a-1-5p (hsa-miR-92a-1-5p) and DIABLO (Diablo homolog). A composition for diagnosing acute tumor response in cervical cancer is provided.
상기 발현 수준을 측정하는 제제는 상기 마이크로 RNA에 상보적으로 결합하는 센스 및 안티센스 프라이머, 또는 프로브일 수 있다.Agents for measuring the expression level may be sense and antisense primers or probes that bind complementary to the micro RNA.
상기 프라이머는 DNA 합성의 기시점이 되는 짧은 유전자 서열로써, 진단, DNA 시퀀싱 등에 이용할 목적으로 합성된 올리고뉴클레오티드를 의미한다. 상기 프라이머들은 통상적으로 15 내지 30 염기쌍의 길이로 합성하여 사용할 수 있으나, 사용 목적에 따라 달라질 수 있으며, 공지된 방법으로 메틸화, 캡화 등으로 변형시킬 수 있다.The primer is a short gene sequence that serves as the starting point for DNA synthesis, and refers to an oligonucleotide synthesized for use in diagnosis, DNA sequencing, etc. The primers can generally be synthesized and used in a length of 15 to 30 base pairs, but may vary depending on the purpose of use, and can be modified by methylation, capping, etc. by known methods.
상기 프로브는 효소 화학적인 분리정제 또는 합성과정을 거쳐 제작된 수 염기 내지 수백 염기길이의 mRNA와 특이적으로 결합할 수 있는 핵산을 의미한다. 방사성 동위원소나 효소 등을 표지하여 mRNA의 존재 유무를 확인할 수 있으며, 공지된 방법으로 디자인하고 변형시켜 사용할 수 있다.The probe refers to a nucleic acid that can specifically bind to mRNA of a few bases to hundreds of bases in length, produced through enzyme-chemical separation purification or synthesis. The presence or absence of mRNA can be confirmed by labeling it with a radioactive isotope or enzyme, and it can be designed, modified and used by known methods.
상기 진단용 조성물은 마이크로 RNA 3960(hsa-miR-3960), 마이크로 RNA 202-5p(hsa-miR-202-5p), 마이크로 RNA 3928-5p(hsa-miR-3928-5p), 마이크로 RNA 574-3p(hsa-miR-574-3p), UBC(Ubiquitin C)의 mRNA, 및 SIN3A(SIN3 transcription regulator family member A)의 mRNA로 이루어진 군에서 선택된 하나 이상의 RNA의 발현 수준을 측정하는 제제를 추가로 포함할 수 있다.The diagnostic composition includes micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), and micro RNA 574-3p. (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA. You can.
또한, 본 발명은 상기 급성 종양 반응 진단용 조성물을 포함하는 자궁경부암 급성 종양 반응 진단용 키트를 제공한다.Additionally, the present invention provides a kit for diagnosing acute tumor response in cervical cancer, including the composition for diagnosing acute tumor response.
상기 키트는 분석 방법에 적합한 한 종류 또는 그 이상의 다른 구성성분 조성물, 용액 또는 장치로 구성된다. 예컨대, 상기 키트는 PCR을 수행하기 위해, 분석하고자 하는 시료로부터 유래된 게놈 DNA, 본 발명의 마커 유전자에 대해 특이적인 프라이머 세트, 적당량의 DNA 중합 효소, dNTP 혼합물, PCR 완충용액 및 물을 포함하는 키트일 수 있다. 상기 PCR 완충용액은 KCl, Tris-HCl 및 MgCl2를 함유할 수 있다. 이외에 PCR 산물의 증폭 여부를 확인할 수 있는 전기영동 수행에 필요한 구성 성분들이 키트에 추가로 포함될 수 있다.The kit consists of one or more different component compositions, solutions or devices suitable for the analytical method. For example, in order to perform PCR, the kit includes genomic DNA derived from the sample to be analyzed, a primer set specific for the marker gene of the present invention, an appropriate amount of DNA polymerase, dNTP mixture, PCR buffer, and water. It could be a kit. The PCR buffer solution may contain KCl, Tris-HCl, and MgCl2. In addition, the kit may additionally include components required to perform electrophoresis to check whether amplification of the PCR product has occurred.
또한, 상기 키트는 RT-PCR을 수행하기 위해 필요한 필수 요소를 포함하는 키트일 수 있다. RT-PCR 키트는 마커 유전자에 대한 특이적인 각각의 프라이머 쌍 외에도 테스트 튜브 또는 다른 적절한 컨테이너, 반응 완충액, 데옥시뉴클레오티드(dNTPs), Taq-폴리머레이즈 및 역전사 효소와 같은 효소, DNase, RNase 억제제, DEPC-수(DEPC-water), 멸균수 등을 포함할 수 있다. 또한 정량 대조군으로 사용되는 유전자에 특이적인 프라이머 쌍을 포함할 수 있다Additionally, the kit may be a kit containing essential elements required to perform RT-PCR. In addition to each primer pair specific for the marker gene, the RT-PCR kit contains test tubes or other suitable containers, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNase inhibitors, and DEPC. -Can include DEPC-water, sterilized water, etc. It may also include a pair of primers specific for the gene used as a quantitative control.
또한, 본 발명은 대상체 유래의 생물학적 시료에서 마이크로 RNA 92a-1-5p(hsa-miR-92a-1-5p), 또는 DIABLO(Diablo homolog)의 mRNA의 발현 수준을 측정하는 단계를 포함하는 자궁경부암의 급성 종양 반응 진단을 위한 정보 제공 방법을 제공한다.In addition, the present invention provides a method for treating cervical cancer, comprising measuring the expression level of mRNA of microRNA 92a-1-5p (hsa-miR-92a-1-5p) or DIABLO (Diablo homolog) in a biological sample derived from a subject. Provides an informational method for diagnosing acute tumor reactions.
상기 생물학적 시료는 혈액 또는 혈장 유래 엑소좀이다.The biological sample is blood or plasma derived exosomes.
상기 발현 수준은 차세대 염기서열 분석(Next generation sequencing; NGS), 중합효소연쇄반응(PCR), 역전사 중합효소연쇄반응(RT-PCR), 실시간 중합효소연쇄반응(Real-time PCR), RNase 보호 분석법(RNase protection assay; RPA), 마이크로어레이(microarray), 및 노던 블롯팅(northern blotting)으로 이루어진 군으로부터 선택되는 1종 이상의 방법을 통해 측정될 수 있다.The expression level was determined by next generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), and RNase protection assay. It can be measured using one or more methods selected from the group consisting of (RNase protection assay (RPA)), microarray, and northern blotting.
상기 제공 방법은 대상체 유래의 생물학적 시료에서 마이크로 RNA 3960(hsa-miR-3960), 마이크로 RNA 202-5p(hsa-miR-202-5p), 마이크로 RNA 3928-5p(hsa-miR-3928-5p), 마이크로 RNA 574-3p(hsa-miR-574-3p), UBC(Ubiquitin C)의 mRNA, 및 SIN3A(SIN3 transcription regulator family member A)의 mRNA로 이루어진 군에서 선택된 하나 이상의 RNA의 발현 수준을 측정하는 단계;를 추가로 포함할 수 있다.The above provision method is to obtain micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), and micro RNA 3928-5p (hsa-miR-3928-5p) from a biological sample derived from a subject. , measuring the expression level of one or more RNAs selected from the group consisting of micro RNA 574-3p (hsa-miR-574-3p), mRNA of UBC (Ubiquitin C), and mRNA of SIN3A (SIN3 transcription regulator family member A). Step; may additionally be included.
상기 대상체는 자궁경부암을 치료하기 위한 방사선 치료를 받는 환자일 수 있다.The subject may be a patient receiving radiation therapy to treat cervical cancer.
상기 진단은 넓은 의미로는 환자의 병의 실태를 모든 면에 걸쳐서 판단하는 것을 의미한다. 판단의 내용은 병명, 병인, 병형, 경중, 병상의 상세한 양태, 및 합병증의 유무 등이다. 상기 예후는 병세의 진행, 회복에 관한 예측을 의미하는 것으로, 전망 내지는 예비적 평가를 말한다.In a broad sense, the above diagnosis means judging the actual condition of the patient's disease in all aspects. The contents of the judgment include the name of the disease, etiology, type, severity, detailed conditions of the disease, and the presence or absence of complications. The prognosis refers to a prediction regarding the progression and recovery of the disease, and refers to an outlook or preliminary evaluation.
상기 진단을 위한 정보 제공 방법은 진단 또는 예후 예측을 위한 예비적 단계로써 필요한 객관적인 기초정보를 제공하는 것이며 의사의 임상학적 판단 또는 소견은 제외된다.The above method of providing information for diagnosis provides objective basic information necessary as a preliminary step for diagnosis or prognosis prediction, and excludes the doctor's clinical judgment or opinion.
이하, 실시예를 통하여 본 발명을 더욱 상세히 설명하고자 한다. 이들 실시예는 오로지 본 발명을 예시하기 위한 것으로서, 본 발명의 범위가 이들 실시예에 의해 제한되는 것으로 해석되지 않는 것은 당업계에서 통상의 지식을 가진 자에게 있어서 자명할 것이다.Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those skilled in the art that the scope of the present invention is not to be construed as limited by these examples.
[실시예][Example]
1. 환자 및 검체1. Patients and specimens
자궁경부암으로 진단된 후 항암 방사선 치료를 받은 환자들을 대상으로 하였다. 아주 대학교 병원 연구위원회 (Institutional Review Board)는 기증자로부터 정보제공에 대한 동의를 받고, 이 연구를 승인하였다.Patients who received chemotherapy and radiation therapy after being diagnosed with cervical cancer were eligible. The Institutional Review Board of Ajou University Hospital obtained informed consent from donors and approved this study.
급성 치료 반응은 암 진단시에 촬영한 영상검사상 종양의 크기를 방사선 치료 시작 4주차에 촬영한 영상검사상 종양의 크기로 나눈 값으로 정의하였다. 이를 임상 종료점으로 (Clinical end point) 하였다.종양의 크기를 평가하는 범위는 골반과 복부림프절에 한정하였다. 자궁경부의 종양의 크기는 T2 weighted MRI image 또는 Diffusion weighted MRI image를 대상으로 radiotherapy treatment planning system (eclipse)를 사용하여 그 부피를 측정하였다. 림프절의 크기는 CT 혹은 T2 weighted MRI image를 통하여 측정되었다. 모든 측정은 치료 전과 항암방사선 치료 시작 4주차의 image를 이용하여 이루어졌으며 치료 전과 치료 중에 사용된 image의 종류는 같았다.Acute treatment response was defined as the size of the tumor on imaging tests taken at the time of cancer diagnosis divided by the size of the tumor on imaging tests taken 4 weeks after starting radiation treatment. This was used as the clinical end point. The scope of evaluating the size of the tumor was limited to the pelvic and abdominal lymph nodes. The size of the cervical tumor was measured using a radiotherapy treatment planning system (eclipse) using a T2 weighted MRI image or a diffusion weighted MRI image. The size of lymph nodes was measured using CT or T2 weighted MRI images. All measurements were made using images taken before treatment and 4 weeks after starting chemotherapy, and the types of images used before and during treatment were the same.
도 2에 나타난 바와 같이, 항암 방사선 치료를 시행한 28명의 자궁경부암 환자들에 대한 치료과정, 혈액의 채취 시점, MRI 촬영 시점, 및 추적 관찰 과정을 실시하였다.As shown in Figure 2, the treatment process, blood collection time, MRI imaging time, and follow-up process were conducted for 28 cervical cancer patients who underwent anti-cancer radiation therapy.
도 3에 나타난 바와 같이, 종양에 대한 방사선 치료 반응을 측정하기 위해 MRI를 이용하여 원발 부위의 종양의 부피를 측정하였고, 림프절 전이 병변은 CT 혹은 MRI로 측정하였다. 종양의 부피를 측정하는 범위는 골반과 복부림프절에 국한되었다. 환자들의 임상 결과는 하기 표 1과 같다.As shown in Figure 3, to measure the radiotherapy response to the tumor, the volume of the tumor at the primary site was measured using MRI, and lymph node metastasis was measured using CT or MRI. The scope of measuring tumor volume was limited to the pelvic and abdominal lymph nodes. The clinical results of the patients are shown in Table 1 below.
2. 엑소좀 miRNA 및 mRNA에 대한 dataset 구성2. Construction of dataset for exosomal miRNA and mRNA
방사선 치료 전과 방사선 치료 2주차일 때, 환자들로부터 혈액을 5-10cc 채집하였다. 채집된 혈액으로부터 혈장을 분리하고, 추가적으로 인체 유래물 보관소에 보관되어 있던 29명의 혈장 샘플 3-5cc를 분양 받아 실험에 사용하였다. 혈장 샘플들로부터 엑소좀(exosome)을 분리하고, NGS(Next Generation Sequencing) 분석을 시행하였다. 혈장으로부터 발현량이 낮은 샘플은 분석에서 제외하였다.Before radiation treatment and during the second week of radiation treatment, 5-10 cc of blood was collected from patients. Plasma was separated from the collected blood, and additionally, 3-5 cc of plasma samples from 29 people stored in the human derivatives storage were distributed and used in experiments. Exosomes were isolated from plasma samples and NGS (Next Generation Sequencing) analysis was performed. Samples with low expression levels from plasma were excluded from the analysis.
2-1. Small RNA 라이브러리 구축 및 시퀀싱2-1. Small RNA library construction and sequencing
혈장을 Exo2D RNA 용액(Exosomeplus)과 혼합하여 엑소좀을 분리하였다. 제조사의 지침에 따라 miRNeasy Serum / Plasma Kit (Qiagen, Valencia, CA)를 사용하여 혈장 유래 엑소좀의 엑소좀 RNA를 추출하였다. 추출된 RNA의 농도는 Quant-IT RiboGreen (Invitrogen)에 의해 계산되었다. RNA의 크기는 Agilent 2100 Bioanalyzer(Agilent Technologies, Boblingen, Germany)에서 Agilent RNA 6000 Pico Kit 및 Small RNA Kit를 사용하여 확인하였다.Exosomes were isolated by mixing plasma with Exo2D RNA solution (Exosomeplus). Exosomal RNA from plasma-derived exosomes was extracted using the miRNeasy Serum/Plasma Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The concentration of extracted RNA was calculated by Quant-IT RiboGreen (Invitrogen). The size of RNA was confirmed using the Agilent RNA 6000 Pico Kit and Small RNA Kit in an Agilent 2100 Bioanalyzer (Agilent Technologies, Boblingen, Germany).
각 샘플에서 분리된 10ng의 RNA는 제조업체의 프로토콜에 따라 Illumina 용 SMARTer smRNA-Seq 키트로 시퀀싱 라이브러리를 구성하는 데 사용되었다. 입력 RNA는 올리고 (dT) 프라이머에 대한 프라이밍 서열을 제공하기 위해 먼저 폴리아데닐화되었다. cDNA 합성은 각 RNA 주형의 5 '끝에 어댑터 서열을 통합하는 3'smRNA dT Primer에 의해 프라이밍되며, LNA(locked nucleic acid) 기술로 강화된 SMRT smRNA Oligo에 의해 결합된 비주형 뉴클레오티드를 추가되었다. 템플릿 전환 단계에서 PrimeScript RT는 SMART smRNA Oligo를 각 첫 번째 가닥 cDNA 분자의 3 '말단에 두 번째 어댑터 시퀀스를 추가하기위한 템플릿으로 사용하였다. 이 후, PCR 증폭 중에 샘플 멀티플렉싱용 인덱스 시퀀스를 포함하는 전체 길이 Illumina 어댑터를 추가하였다. Forward PCR Primer는 SMART smRNA Oligo에 의해 추가된 서열에 결합하는 반면 Reverse PCR Primer는 3’smRNA dT Primer에 의해 추가된 서열에 결합하였다.10 ng of RNA isolated from each sample was used to construct a sequencing library with the SMARTer smRNA-Seq kit for Illumina according to the manufacturer's protocol. Input RNA was first polyadenylated to provide a priming sequence for oligo (dT) primers. cDNA synthesis was primed by a 3'smRNA dT Primer that incorporated an adapter sequence at the 5' end of each RNA template, followed by the addition of non-template nucleotides bound by SMRT smRNA Oligo enhanced by locked nucleic acid (LNA) technology. In the template conversion step, PrimeScript RT used SMART smRNA Oligo as a template to add a second adapter sequence to the 3' end of each first-strand cDNA molecule. After this, full-length Illumina adapters containing the index sequence for sample multiplexing during PCR amplification were added. Forward PCR Primer bound to the sequence added by SMART smRNA Oligo, while Reverse PCR Primer bound to the sequence added by 3’smRNA dT Primer.
증폭된 라이브러리를 6% Novex TBE-PAGE 겔(Thermo Fisher, MA)에서 정제하여 138bp(cDNA 18bp 이상 + 어댑터 120bp 이상) 이상의 분획을 절제하였다. 결과 라이브러리 cDNA 분자에는 Illumina 플로우 셀의 클러스터링에 필요한 시퀀스를 포함시켰다.The amplified library was purified on a 6% Novex TBE-PAGE gel (Thermo Fisher, MA), and a fraction of more than 138 bp (cDNA 18 bp or more + adapter 120 bp or more) was excised. The resulting library cDNA molecules contained the sequences required for clustering on an Illumina flow cell.
라이브러리는 겔 정제되었으며 Agilent Bioanalyzer에서 크기, 순도 및 농도를 확인하여 검증되었다. 라이브러리는 qPCR Quantification Protocol Guide (Illumina Sequecing 플랫폼 용 KAPA Library Quantificatoin 키트)에 따라 qPCR을 사용하여 정량화되었고, TapeStation D1000 ScreenTape (Agilent Technologies, Waldbronn, Germany)를 사용하여 검증되었다. 라이브러리를 등 몰량으로 풀링하고 Illumina HiSeq 2500 (Illumina, San Diego, USA) 기기에서 시퀀싱하여 51 개의 염기 읽기를 생성하였다. 이미지 분해 및 품질 값 계산은 Illumina 파이프 라인의 모듈을 사용하여 수행되었다.The library was gel purified and verified for size, purity, and concentration on an Agilent Bioanalyzer. Libraries were quantified using qPCR according to the qPCR Quantification Protocol Guide (KAPA Library Quantificatoin Kit for Illumina Sequecing Platforms) and verified using TapeStation D1000 ScreenTape (Agilent Technologies, Waldbronn, Germany). Libraries were pooled in equimolar amounts and sequenced on an Illumina HiSeq 2500 (Illumina, San Diego, USA) instrument, generating 51 base reads. Image decomposition and quality value calculation were performed using modules of the Illumina pipeline.
2-2. 어댑터 트리밍(Adapter trimming)2-2. Adapter trimming
서로 다른 실험 샘플에서 얻은 small RNA의 raw 서열 read은 miRDeep2로 전처리하고 분석되었다. 어댑터 트리밍은 cutadapt 프로그램을 사용하여 smRNA 라이브러리 구축 과정에서 miRNA이 부착된 어댑터 서열이 존재하면 제거하였다. 모든 read의 첫 번째 3nt는 SMART template-switching 활동 프로세스 중에 삽입된 추가 염기를 제거하기 위해 트리밍되었다. 어댑터 서열과 어댑터의 모든 3’도 제거되었다. 읽기가 3’어댑터 서열의 처음 5 bp 이상 일치하는 경우, 서열이 실제로 어댑터 서열인 것으로 간주한 후, read에서 트리밍되었다. 분석을 위해, 트리밍된 read이 최소 18bp 이상인 경우만 신뢰하는 것으로 간주하였다. 나머지는 어댑터 서열은 비 어댑터 읽기로 분류하였다. 이 분석에서 트리밍 및 비 어댑터 읽기가 결합되고 다운 스트림 분석을 위해 처리된 read으로 간주되었다.Raw sequence reads of small RNAs obtained from different experimental samples were preprocessed with miRDeep2 and analyzed. Adapter trimming used the cutadapt program to remove adapter sequences to which miRNAs were attached during the construction of the smRNA library. The first 3 nt of all reads were trimmed to remove additional bases inserted during the SMART template-switching activity process. The adapter sequence and all 3' sides of the adapter were also removed. If a read matched more than the first 5 bp of the 3' adapter sequence, the sequence was considered to actually be an adapter sequence and was then trimmed from the read. For analysis, only trimmed reads of at least 18bp were considered reliable. The remaining adapter sequences were classified as non-adapter reads. In this analysis, trimmed and non-adapter reads were combined and considered processed reads for downstream analysis.
2-3. 클러스터링(Clustering)2-3. Clustering
서열 특이성(sequence uniqueness) 및 계산 강도를 최소화하기 위해 어댑터 서열로 처리된 read을 수집하여 클러스터를 형성하였다. 이 클러스터에는 서열 ID 및 read 길이와 100% 일치하는 read을 포함하며, 임시 클러스터 ID 및 보유 read 수가 제공되었다.To minimize sequence uniqueness and computational intensity, reads processed with adapter sequences were collected to form clusters. This cluster contains reads with 100% match by sequence ID and read length, and a provisional cluster ID and number of retained reads were provided.
2-4. 리보솜 RNA 필터링2-4. Ribosomal RNA filtering
대부분의 RNA 구성은 rRNA로 알려져 있다. 다량의 rRNA의 효과를 제거하기 위해 읽기를 Homo sapiens의 45S pre-rRNA 및 미토콘드리아 rRNA에 정렬하고 일치시켰다.Most RNA components are known as rRNA. To eliminate the effect of large amounts of rRNA, reads were aligned and matched to the 45S pre-rRNA and mitochondrial rRNA of Homo sapiens .
2-5. miRNA read2-5. miRNA read
miRDeep2 소프트웨어 알고리즘을 사용하여 microRNA의 서열 정렬 및 검출을 수행하였다. 서열 정렬을 수행하기 전에 Homo sapiens reference genome release hg19를 UCSC 게놈 브라우저에서 검색하고 시퀀싱 read을 참조 서열에 정렬하기 위한 Bowtie (1.1.2)를 사용하여 인덱싱하였다. miRBase v21에서 얻은 전구체 miRNA을 참조 서열과 정렬하였다. miRDeep2 알고리즘은 miRNA biogenesis 모델을 기반으로 하며, Dicer 처리와 일치하는 방식으로 잠재적인 헤어핀 구조에 읽기를 정렬하고 헤어핀이 진정한 miRNA 전구체일 가능성을 나타내는 점수를 할당하였다.Sequence alignment and detection of microRNAs were performed using the miRDeep2 software algorithm. Before performing sequence alignment, the Homo sapiens reference genome release hg19 was searched in the UCSC genome browser and indexed using Bowtie (1.1.2) to align sequencing reads to the reference sequence. Precursor miRNAs obtained from miRBase v21 were aligned with the reference sequence. The miRDeep2 algorithm is based on the miRNA biogenesis model and aligned reads to potential hairpin structures in a manner consistent with Dicer processing and assigned a score indicating the likelihood that the hairpin is a true miRNA precursor.
2-6. miRNA 및 기타 RNA 범주 비율2-6. Proportion of miRNA and other RNA categories
클러스터링 된 읽기는 알려진 miRNA 및 기타 유형의 RNA를 식별하기 위해, 참조 게놈, miRBase v21 및 비 코딩 RNA 데이터베이스 RNAcentral release 10.0에 순차적으로 정렬되었다.Clustered reads were sequentially aligned to the reference genome, miRBase v21, and the non-coding RNA database RNAcentral release 10.0, to identify known miRNAs and other types of RNA.
모든 데이터 및 시각화는 R 4.0.2 version(www.r-project.org)을 이용하여 수행되었으며 모든 비교 분석은 비모수를 가정하였다. 상기 NGS data는 small RNA, non- mRNA, 및 mRNA을 포함하고 있었으며, 이 중 miRNA 및 mRNA을 분석에 이용하였다. 이 중 14명이상에서 read count가 0인 경우는 분석에서 제외되었다. 분석에 이용된 miRNA는 586건, mRNA은 15324 건으로 통계되었다. 모든 전사체 데이터의 값들은 치료 전의 혈장 엑소좀의 read count값들 대비 방사선 치료 시작 2주 후의 read count 값들의 log2 fold change (log2FC) 값을 사용하였다. 28명의 환자들에 각 각에 대해 방사선 치료 전의 read count 값을 control로 사용하였고, 방사선 치료 후 2주의 read count 값의 변화를 log2 fold change (log2FC) 값으로 계산하였다. edgeR을 사용하여 TMM normalization 하고 log2FC 값을 구하였다.All data and visualizations were performed using R 4.0.2 version (www.r-project.org), and all comparative analyzes were assumed to be non-parametric. The NGS data included small RNA, non-mRNA, and mRNA, and among these, miRNA and mRNA were used for analysis. Among these, cases where the read count was 0 in more than 14 patients were excluded from the analysis. There were 586 cases of miRNA and 15,324 cases of mRNA used in the analysis. The values of all transcriptome data used the log2 fold change (log2FC) value of the read count values 2 weeks after the start of radiation treatment compared to the read count values of plasma exosomes before treatment. For each of the 28 patients, the read count value before radiation treatment was used as a control, and the change in read count value 2 weeks after radiation treatment was calculated as the log2 fold change (log2FC) value. TMM normalization was performed using edgeR and log2FC values were obtained.
3. 분석과정 및 결과3. Analysis process and results
바이오마커를 선별하기 위한 분석 과정 및 결과는 다음의 단계로 진행되었다.The analysis process and results for selecting biomarkers proceeded in the following steps.
I 단계Stage I
임상 종료점(Clinical end point)을 예측할 수 있는 miRNA들을 선별하고,이들의 상관관계를 분석하였다(도 4 및 4). 상관 관계(Correlation) 분석은 다음 3단계로 이루어졌다.We selected miRNAs that could predict clinical end points, and analyzed their correlation (Figures 4 and 4). Correlation analysis consisted of the following three steps.
1 단계: Hmisc package - rcorr function - pearson’s correlationStep 1: Hmisc package - rcorr function - pearson’s correlation
2 단계: Leaps package - regsubsets function - exhaustive algorithm (최대 변수 설정 10 또는 7)Step 2: Leaps package - regsubsets function - exhaustive algorithm (set max variable 10 or 7)
3 단계: Mean comparison (Wincoxon rank sum test or Kruskal-Wallis test) and boxplotsStep 3: Mean comparison (Wincoxon rank sum test or Kruskal-Wallis test) and boxplots
도 4에 나타난 바와 같이, 자궁경부암의 급성 종양 반응(acute tumor response, AR)와 유의한 연관성(|R| >0.4)을 가진 miRNA(log2FC)들의 서브그룹(Subgroup) 분석을 통해 miRNA을 선별하였다. Adjusted R 값을 기준으로 가능한 모든 경우의 조합을 연산했을 때 지속적으로 포함되는 miRNA들을 선별하였다.As shown in Figure 4, miRNAs were selected through subgroup analysis of miRNAs (log2FC) with significant correlation (|R| >0.4) with acute tumor response (AR) of cervical cancer. . When all possible combinations of cases were calculated based on the Adjusted R value, consistently included miRNAs were selected.
도 5에 나타난 바와 같이, 선별된 miRNA 변수들의 다중선형회귀 조합으로 방향성을 얻은 후 단순히 각 miRNA (log2FC) 값들을 miR-3928-3p+miR-92a-1-5p+miR-574-3p+miR-3960-miR-202-5p 순으로 더하고 빼서 값을 구했다. 그 값이 클 수록 급성 종양 반응(acute tumor response)과 관련이 높은 것으로 나타났다.As shown in Figure 5, after obtaining directionality through multiple linear regression combination of selected miRNA variables, each miRNA (log2FC) value was simply divided into miR-3928-3p+miR-92a-1-5p+miR-574-3p+miR The value was obtained by adding and subtracting in the order of -3960-miR-202-5p. The larger the value, the higher the correlation with acute tumor response.
II 단계Stage II
주 miRNA 군 및 그들과 연관된 miRNA들을 네트워크 서브그룹(network subgroup)으로 나누어 네트워크(network) 분석하였다. 임상 종료점(Clinical end point)에 따라 주 miRNA 군의 및 서브그룹의 영향력을 기술하였다.The main miRNA groups and their associated miRNAs were divided into network subgroups and network analyzed. The influence of major miRNA families and subgroups was described according to clinical end points.
Network 분석은 Igraph package - prim algorithm을 사용하고, 모든 network에서 붉은 색 edge는 양의 방향, 푸른색 edge는 음의 방향을 의미하도록 시각화 하였다.Network analysis used the Igraph package - prim algorithm, and in all networks, red edges indicated positive directions and blue edges indicated negative directions.
miRNA 그룹 층화는 A group은 주 miRNA 군을 의미하고, B group은 여러 주 miRNA 군과 연관된 miRNA 군을 의미한다.For miRNA group stratification, A group refers to the main miRNA group, and B group refers to the miRNA group associated with several major miRNA groups.
도 6에 나타난 바와 같이, 급성 종양 반응(acute tumor response, AR)에 관한 miRNA들을 서브그룹(network subgroup)으로 나누어 네트워크(network)로 분석하였다. 주 miRNA가 포함된 community 마다 good AR (<0.2) 및 pool AR (≥ 0.2)을 표시하고, 각 miRNA의 수준이 증가한 개수 및 감소한 갯수를 비교하였다 (증가: log2FC>1.5, 감소: log2FC <-1.5). miR-574-3p를 제외한 A level은 도 5와 같은 방향성으로 유의미하게 증가 혹은 감소하였으며 이는 level A의 miRNA들이 각각 독립적으로 AR에 기여하는 것으로 나타났다. miR-574-3p는 상대적으로 영향력이 적었다고 볼 수 있다. AR 그룹에 따른 B level miRNA들의 차이는 없었다. 따라서 현재의 network에 포함되는 B level miRNA들은 A level과의 관련성이 적다고 볼 수 있다.As shown in Figure 6, miRNAs related to acute tumor response (AR) were divided into subgroups and analyzed as a network. Good AR (<0.2) and pool AR (≥ 0.2) were displayed for each community containing the main miRNA, and the number of increased and decreased levels of each miRNA was compared (increase: log2FC>1.5, decrease: log2FC <-1.5) ). Excluding miR-574-3p, the A level significantly increased or decreased in the same direction as shown in Figure 5, indicating that level A miRNAs each independently contribute to AR. It can be seen that miR-574-3p had relatively little influence. There was no difference in B level miRNAs according to AR group. Therefore, the B level miRNAs included in the current network can be considered to have little relationship with the A level.
III 단계Stage III
network 분석을 통해 주 miRNA 군, 연관 mRNA들 및 II 단계에 포함된 miRNA를 포함하여 전체적인 miRNA-mRNA 간의 구조를 파악하였다. 주 miRNA 간을 연결하는 가장 짧은 pathway를 기술하고, II 단계의 network 상에서 임상적 연관성이 있는 subgroup miRNA들의 위치를 확인하였다. Network 분석은 상기 II 단계와 동일한 조건으로 시각화하였다.Through network analysis, the overall structure of the miRNA-mRNA was identified, including the main miRNA group, related mRNAs, and miRNAs included in stage II. The shortest pathway connecting major miRNAs was described, and the locations of clinically relevant subgroup miRNAs were identified in the level II network. Network analysis was visualized under the same conditions as in step II above.
도 7에 나타난 바와 같이, A level의 miRNA와 연관된 (|R| >0.6) mRNA들과 선별된 miRNA들을 합쳐 network를 구성하였으며, A level miRNA들 (화살표) 상이의 최단 거리는 붉은 선으로 표시되었다. 25개이상의 긴 거리를 가졌으며 직선으로 이어져 유기적인 연결 구조도 아니었으며 B level의 miRNA는 현 network에서 발견되지 않아 현재의 network는 level A를 연결하는 miRNA-mRNA 구조로 적절하지 않다.As shown in Figure 7, a network was constructed by combining (|R| >0.6) mRNAs associated with A level miRNAs and selected miRNAs, and the shortest distance between A level miRNAs (arrows) is indicated by a red line. It had a long distance of more than 25 and was not an organic connection structure as it was connected in a straight line, and level B miRNAs were not found in the current network, so the current network is not appropriate as a miRNA-mRNA structure connecting level A.
IV 단계Stage IV
임상 종료점(Clinical end point)과 관련성 있는 mRNA들과 III 단계의 network에 포함된 miRNA 및 mRNA들이 포함되도록 선별(도 8)하고, 기존 IPA (Ingenuity Pathway Analysis) database로부터 유의한 질환(disease)/ 함수 범주(function category) 및 하위 범주(subcategories) 들을 추출(도 9)하였다.mRNAs related to clinical end points and miRNAs and mRNAs included in the stage III network were selected (Figure 8), and significant diseases/functions were identified from the existing IPA (Ingenuity Pathway Analysis) database. Function categories and subcategories were extracted (Figure 9).
도 8에 나타난 바와 같이, 각의 임상 요소와 높은 관련성이 있는 mRNA들 (A)및 네트워크(network) 분석을 통해 얻어진 miRNA 및 mRNA(B)를 포함하여 관련 유전자들을 선별하였다.As shown in Figure 8, related genes were selected, including mRNAs (A) highly related to each clinical element and miRNAs and mRNAs (B) obtained through network analysis.
도 9에 나타난 바와 같이, 급성 종양 반응(acute tumor response, AR)에 관련된 miRNA와 mRNA들로 IPA(Ingenuity pathway analysis) 분석하여 유의한 함수 범주(function category) 30개를 선별하였다. 30개 중 연구의 가정과 관련된 영역들로 RNA 손상/복구(RNA damage and repair), 암(cancer), 세포 사멸/생존(cell death and survival), 및 DNA 복제/제조합/복구(DNA Replication, Recombination, and Repair)를 선택하였다. 상기 항목들의 구체적 하위 범주(subcategories)로써 DNA 대사(Metabolism of DNA), DNA 분해(Degradation of DNA), DNA 복구(DNA replication), DNA 단편화(Fragmentation of DNA), 종양 세포주의 세포사멸(Apoptosis of tumor cell lines), 및 DNA 복구(Repair of DNA)를 선택하였다.As shown in Figure 9, 30 significant function categories were selected through IPA (Ingenuity pathway analysis) analysis of miRNAs and mRNAs related to acute tumor response (AR). Among the 30, areas related to research assumptions include RNA damage and repair, cancer, cell death and survival, and DNA Replication/Recombination. , and Repair) was selected. Specific subcategories of the above items include Metabolism of DNA, Degradation of DNA, DNA replication, Fragmentation of DNA, and Apoptosis of tumor cell lines. cell lines), and DNA repair (Repair of DNA) were selected.
V 단계Stage V
유의성 정도 및 임상 종료점(Clinical end point)과의 연관 가능성이 있는 질환(disease)/ 함수 범주(function category)를 선별하고, 각 그룹에 해당하는 mRNA 및 miRNA들을 이용 벤다어그램을 도시하였다(도 10). 도시된 벤다이어그램을 통해 암과 특정기능에 공통적으로 연관된 mRNA 및 miRNA들을 재선별 하였다.Diseases/function categories with a degree of significance and possible association with clinical end points were selected, and a vendiagram was drawn using the mRNA and miRNA corresponding to each group (Figure 10 ). Using the Venn diagram shown, mRNAs and miRNAs commonly associated with cancer and specific functions were re-selected.
도 10에 나타난 바와 같이, 급성 종양 반응(acute tumor response, AR)과 관련되어 선별된 4개의 함수 범주(function category)들의 벤다이어그램을 그리고 cancer 영역과 중복된 항목들의 mRNA 및 miRNA를 선택하였다.As shown in Figure 10, a Venn diagram of four function categories selected in relation to acute tumor response (AR) was drawn and mRNA and miRNA of items overlapping with the cancer region were selected.
VI 단계Stage VI
도 7의 주 miRNA의 short pathway를 이용하여 재선별된 mRNA 및 miRNA들을 network로 형성하였다. Network 분석은 상기 II 단계와 동일한 조건으로 시각화하였다.The re-selected mRNAs and miRNAs were formed into a network using the short pathway of the main miRNA in Figure 7. Network analysis was visualized under the same conditions as in step II above.
도 11에 나타난 바와 같이, 도 10에서 선별된 miRNA 및 mRNA들과 주 miRNA들을 이용하여 network를 형성하였다. 위의 그림은 edge사이의 correlation이 0.4가 되지 않는 경우를 제거한 network이고 아래의 그림은 correlation이 0.6이 되지 않는 경우를 제거한 network이다. DIABLO를 중심으로 모여있는 miR-92a-1-5p, miR-3928-3p, miR-564-3p를 제외하고는 모두 miR-202-5p 및 miR-3960의 변화에 따라 유의미하게 변화하는 mRNA들이었다. miR-202-5p 및 miR-3960를 중심으로 한 그룹과 DIABLO는 miR-574-3p를 매개로 EGF와 양으로 연결되어 있다. miR-92a-1-5p의 변화 만이 DIABLO를 유의하게 변화시켰다.As shown in Figure 11, a network was formed using the selected miRNAs and mRNAs in Figure 10 and the main miRNAs. The picture above is a network from which cases where the correlation between edges does not reach 0.4 are removed, and the picture below is a network from which cases where the correlation between edges does not reach 0.6 are removed. Except for miR-92a-1-5p, miR-3928-3p, and miR-564-3p, which are clustered around DIABLO, all mRNAs were significantly changed according to changes in miR-202-5p and miR-3960. . A group centered on miR-202-5p and miR-3960 and DIABLO are positively linked to EGF mediated by miR-574-3p. Only changes in miR-92a-1-5p significantly altered DIABLO.
VII 단계Stage VII
VI 단계의 network를 구성하는 RNA들의 log2FC 값들을 28명 별로 IPA database에 올리고, 유의미한 질환(disease)/ 함수 범주(function category)의 z-score를 구하였다. 모든 환자들에서 유의미한 z score가 구해진 항목만을 선별하여 clinical end point에 따라 통계적으로 유의하게 차이가 있었던 항목들을 분석하였다.The log2FC values of RNAs constituting the network of stage VI were uploaded to the IPA database for each 28 people, and the z-score of the significant disease/function category was obtained. Only the items for which significant z scores were obtained in all patients were selected, and items that showed statistically significant differences according to clinical end point were analyzed.
도 12에 나타난 바와 같이, 도 11(윗쪽)의 network에 포함된 RNA들의 log2FC 값들을 모두 각 환자 별로 IPA database에서 분석하여 카테고리 별로 z score를 얻은 후 Acute tumor response 0.2를 기준으로 poor 및 good 반응으로 나누었을 때 통계적으로 유의한 차이를 보이는 항목들을 기술한 결과 DNA 단편화(DNA fragmentation)를 동반하는 세포사멸(apoptosis) 및 세포 성장에 따른 DNA 대사 증가가 good AR과 연관되었다.As shown in Figure 12, all log2FC values of RNAs included in the network of Figure 11 (top) were analyzed in the IPA database for each patient to obtain z scores for each category, and then classified into poor and good responses based on Acute tumor response 0.2. As a result of describing the items that showed statistically significant differences when divided, apoptosis accompanied by DNA fragmentation and increased DNA metabolism due to cell growth were associated with good AR.
VIII 단계Stage VIII
모든 network에서 유의미하게 변한 RNA 그룹에 대하여 주 miRNA에 대한 상기 선별된 하위 범주(subcategories)들에 대한 RNA들의 비율 변화를 분석하였다.For the RNA groups that changed significantly in all networks, we analyzed the change in the ratio of RNAs for the selected subcategories to the main miRNA.
도 13에 나타난 바와 같이, 도 11(윗쪽)의 network는 약 10%에서 DNA 단편화(DNA fragmentation)와 연관된 세포사멸(apoptosis)였으며, 60-65%는 RNA 손상/복구(RNA damage/repair) 및 DNA 대사(DNA metabolism)와 연관되었으며, 그 외 25-30%는 다른 종류의 세포 사멸/생존(cell death and survival)이었다. 이들의 구성은 모두 miR-202-5p 및 miR-3690의 변화에 따라 움직이는 mRNA들의 비중과 같았으며 miR-92a-1-5p와 연관된 DIABLO는 DNA 단편화(DNA fragmentation)와 연관된 세포사멸(apoptosis)의 역할을 하는 것으로 보인다.As shown in Figure 13, the network in Figure 11 (top) was apoptosis associated with DNA fragmentation in about 10%, and RNA damage/repair and RNA damage/repair in 60-65%. It was associated with DNA metabolism, and the remaining 25-30% were other types of cell death and survival. Their composition was all the same as the proportion of mRNAs that move according to changes in miR-202-5p and miR-3690, and DIABLO, which is associated with miR-92a-1-5p, is responsible for apoptosis associated with DNA fragmentation. It appears to be playing a role.
IX 단계Stage IX
주 miRNA의 변화에 따라 유의미하게 변화된 mRNA 및 miRNA들을 분석하였다.Significantly changed mRNAs and miRNAs were analyzed according to changes in the main miRNA.
도 14에 나타난 바와 같이, miR-202-5p 및 miR-3690의 변화에 따른 mRNA의 변화는 두 miRNA에 의해 모두 유의하게 변화한 mRNA와 UBC (miR-202-5p), SIN3A(miR-3960)를 포함시켰다. 같은 방향의 변화는 붉은 색으로 반대방향의 변화는 푸른 색으로 p 값의 변화를 표시하였으며1.5 log2FC를 넘어서는 변화는 음영으로 표시하였다. 각각의 라벨은 DRR은 DNA 복제, 재조합 및 복구(DNA Replication, Recombination, and Repair)를 의미하고, RDR은 RNA 손상 및 복구(RNA damage and repair), DNA(r)은 DNA 복제(DNA replication), DNA (d)는 DNA 분해(DNA degradation), DRR(o)는 DNA 복제, 재조합 및 수리-기타(DNA Replication, Recombination, and Repair-others), Apop(f)는 세포사멸-DNA 단편화(Apoptosis - DNA fragmentation), Apop(o)는 세포사멸-기타(Apoptosis - others), non-Apop는 비-세포사멸(Non-apoptosis)을 의미한다.As shown in Figure 14, changes in mRNA according to changes in miR-202-5p and miR-3690 were significantly changed by both mRNAs, UBC (miR-202-5p), and SIN3A (miR-3960). included. Changes in the same direction are indicated in red, changes in the opposite direction are indicated in blue, and changes exceeding 1.5 log2FC are indicated in shading. Each label stands for DRR, which stands for DNA Replication, Recombination, and Repair, RDR, which stands for RNA damage and repair, and DNA(r), which stands for DNA replication. DNA (d) is DNA degradation, DRR(o) is DNA Replication, Recombination, and Repair-others, and Apop(f) is apoptosis-DNA fragmentation. DNA fragmentation), Apop(o) means apoptosis - others, and non-Apop means non-apoptosis.
X 단계X level
도 11에서 얻어진 mRNAㄷ르을 이용하여 임상 종료점(Clinical end point)을 예측할 수 있는 mRNA들을 추출하고, 이들과 주 miRNA 들간의 임상적 유의성을 분석하였다. 상관 관계(Correlation) 분석은 상기 I 단계와 동일한 단계로 이루어졌다.Using the mRNA obtained in Figure 11, mRNAs capable of predicting clinical end points were extracted, and the clinical significance between these and the main miRNAs was analyzed. Correlation analysis was performed in the same steps as step I above.
도 15에 나타난 바와 같이, 도 11을 구성하는 mRNA들을 대상으로 급성 종양 반응(acute tumor response, AR)에 대한 서브그룹(subgroup) 분석을 시행하였다. Adjusted R 값을 기준으로 가능한 모든 경우의 조합을 연산했을 때 지속적으로 포함되는 mRNA들을 선별하였다. DIABLO, SIN3A, UBC, 과 RPL41이 선별되었다.As shown in Figure 15, a subgroup analysis of acute tumor response (AR) was performed on the mRNAs constituting Figure 11. When all possible combinations were calculated based on the Adjusted R value, mRNAs that were consistently included were selected. DIABLO, SIN3A, UBC, and RPL41 were selected.
도 16에 나타난 바와 같이, DIBLO+UBC+SIN3A-RPL41 (log2FC) 값이 증가할 수로 acute tumor response는 유의하게 향상되었다. RPL41의 방향성은 network 분석에서는 miR-3960과 일치하나 다중선형 회귀에서는 반대방향이다.As shown in Figure 16, the acute tumor response was significantly improved as the DIBLO+UBC+SIN3A-RPL41 (log2FC) value increased. The direction of RPL41 is consistent with that of miR-3960 in network analysis, but is in the opposite direction in multiple linear regression.
이상으로 본 발명 내용의 특정한 부분을 상세히 기술하였는 바, 당업계의 통상의 지식을 가진 자에게 있어서, 이러한 구체적 기술은 단지 바람직한 실시양태일 뿐이며, 이에 의해 본 발명의 범위가 제한되는 것이 아닌 점은 명백하다. 즉, 본 발명의 실질적인 범위는 첨부된 청구항들과 그것들의 등가물에 의하여 정의된다.As the specific parts of the present invention have been described in detail above, it is clear to those skilled in the art that these specific techniques are merely preferred embodiments and do not limit the scope of the present invention. do. That is, the practical scope of the present invention is defined by the appended claims and their equivalents.
<110> Ajou University <120> Biomarkers for diagnosing early progression of cervical cancer, and uses thereof <130> ADP-2023-0218 <160> 8 <170> KoPatentIn 3.0 <210> 1 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-92a-1-5p <400> 1 agguugggau cgguugcaau gcu 23 <210> 2 <211> 720 <212> DNA <213> Artificial Sequence <220> <223> DIABLO mRNA <400> 2 atggcggctc tgaagagttg gctgtcgcgc agcgtaactt cattcttcag gtacagacag 60 tgtttgtgtg ttcctgttgt ggctaacttt aagaagcggt gtttctcaga attgataaga 120 ccatggcaca aaactgtgac gattggcttt ggagtaaccc tgtgtgcggt tcctattgca 180 cagaaatcag agcctcattc ccttagtagt gaagcattga tgaggagagc agtgtctttg 240 gtaacagata gcacctctac ctttctctct cagaccacat atgcgttgat tgaagctatt 300 actgaatata ctaaggctgt ttatacctta acttctcttt accgacaata tacaagttta 360 cttgggaaaa tgaattcaga ggaggaagat gaagtgtggc aggtgatcat aggagccaga 420 gctgagatga cttcaaaaca ccaagagtac ttgaagctgg aaaccacttg gatgactgca 480 gttggtcttt cagagatggc agcagaagct gcatatcaaa ctggcgcaga tcaggcctct 540 ataaccgcca ggaatcacat tcagctggtg aaactgcagg tggaagaggt gcaccagctc 600 tcccggaaag cagaaaccaa gctggcagaa gcacagatag aagagctccg tcagaaaaca 660 caggaggaag gggaggagcg ggctgagtcg gagcaggagg cctacctgcg tgaggattga 720 720 <210> 3 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-3960 <400> 3 ggcggcggcg gaggcggggg 20 <210> 4 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-202-5p <400> 4 uuccuaugca uauacuucuu ug 22 <210> 5 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-3928-5p <400> 5 ugaagcucua agguuccgcc ugc 23 <210> 6 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-574-3p <400> 6 cacgcucaug cacacaccca ca 22 <210> 7 <211> 2058 <212> DNA <213> Artificial Sequence <220> <223> UBC mRNA <400> 7 atgcagatct tcgtgaagac tctgactggt aagaccatca ccctcgaggt tgagcccagt 60 gacaccatcg agaatgtcaa ggcaaagatc caagataagg aaggcatccc tcctgaccag 120 cagaggctga tctttgctgg aaaacagctg gaagatgggc gcaccctgtc tgactacaac 180 atccagaaag agtccaccct gcacctggtg ctccgtctca gaggtgggat gcaaatcttc 240 gtgaagacac tcactggcaa gaccatcacc cttgaggtcg agcccagtga caccatcgag 300 aacgtcaaag caaagatcca ggacaaggaa ggcattcctc ctgaccagca gaggttgatc 360 tttgccggaa agcagctgga agatgggcgc accctgtctg actacaacat ccagaaagag 420 tctaccctgc acctggtgct ccgtctcaga ggtgggatgc agatcttcgt gaagaccctg 480 actggtaaga ccatcaccct cgaggtggag cccagtgaca ccatcgagaa tgtcaaggca 540 aagatccaag ataaggaagg cattcctcct gatcagcaga ggttgatctt tgccggaaaa 600 cagctggaag atggtcgtac cctgtctgac tacaacatcc agaaagagtc caccttgcac 660 ctggtactcc gtctcagagg tgggatgcaa atcttcgtga agacactcac tggcaagacc 720 atcacccttg aggtcgagcc cagtgacact atcgagaacg tcaaagcaaa gatccaagac 780 aaggaaggca ttcctcctga ccagcagagg ttgatctttg ccggaaagca gctggaagat 840 gggcgcaccc tgtctgacta caacatccag aaagagtcta ccctgcacct ggtgctccgt 900 ctcagaggtg ggatgcagat cttcgtgaag accctgactg gtaagaccat cactctcgaa 960 gtggagccga gtgacaccat tgagaatgtc aaggcaaaga tccaagacaa ggaaggcatc 1020 cctcctgacc agcagaggtt gatctttgcc ggaaaacagc tggaagatgg tcgtaccctg 1080 tctgactaca acatccagaa agagtccacc ttgcacctgg tgctccgtct cagaggtggg 1140 atgcagatct tcgtgaagac cctgactggt aagaccatca ctctcgaggt ggagccgagt 1200 gacaccattg agaatgtcaa ggcaaagatc caagacaagg aaggcatccc tcctgaccag 1260 cagaggttga tctttgctgg gaaacagctg gaagatggac gcaccctgtc tgactacaac 1320 atccagaaag agtccaccct gcacctggtg ctccgtctta gaggtgggat gcagatcttc 1380 gtgaagaccc tgactggtaa gaccatcact ctcgaagtgg agccgagtga caccattgag 1440 aatgtcaagg caaagatcca agacaaggaa ggcatccctc ctgaccagca gaggttgatc 1500 tttgctggga aacagctgga agatggacgc accctgtctg actacaacat ccagaaagag 1560 tccaccctgc acctggtgct ccgtcttaga ggtgggatgc agatcttcgt gaagaccctg 1620 actggtaaga ccatcactct cgaagtggag ccgagtgaca ccattgagaa tgtcaaggca 1680 aagatccaag acaaggaagg catccctcct gaccagcaga ggttgatctt tgctgggaaa 1740 cagctggaag atggacgcac cctgtctgac tacaacatcc agaaagagtc caccctgcac 1800 ctggtgctcc gtctcagagg tgggatgcaa atcttcgtga agaccctgac tggtaagacc 1860 atcaccctcg aggtggagcc cagtgacacc atcgagaatg tcaaggcaaa gatccaagat 1920 aaggaaggca tccctcctga tcagcagagg ttgatctttg ctgggaaaca gctggaagat 1980 ggacgcaccc tgtctgacta caacatccag aaagagtcca ctctgcactt ggtcctgcgc 2040 ttgagggggg gtgtctaa 2058 <210> 8 <211> 3822 <212> DNA <213> Artificial Sequence <220> <223> SIN3A mRNA <400> 8 atgaagcggc gtttggatga ccaggagtca ccggtgtatg cagcccagca gcgtcggatc 60 cctggcagca cagaggcttt tcctcaccag caccgggtgc ttgcccctgc ccctcctgtg 120 tatgaagcag tgtctgagac catgcagtca gctacgggaa ttcagtactc tgtaacaccc 180 agctaccagg tttcagccat gccacagagc tccggcagtc atgggcccgc tatagcagca 240 gttcatagca gccatcatca cccaacagcg gtgcagcccc acggaggcca ggtggtccag 300 agtcatgctc atccagcccc accagttgca ccagtgcagg gacagcagca atttcagagg 360 ctgaaggtgg aggatgcgct atcttatctt gaccaggtga agctgcagtt tggtagtcag 420 cctcaggtct acaatgattt ccttgacatc atgaaggaat ttaaatctca gagcatcgac 480 accccaggag tgattagtcg tgtgtcccag ctattcaaag gccaccccga tctgataatg 540 ggattcaaca ccttcttgcc ccctggctac aaaattgagg tgcaaaccaa tgacatggtg 600 aatgtgacaa ctcctggcca ggttcatcag attcccaccc atggcatcca gccacagcct 660 caaccaccac cccaacatcc ttcccagcct tcagcccagt cagccccagc tcctgcccag 720 ccagctcctc agcccccacc tgccaaagtc agcaagccct cccaactgca agcacatact 780 ccggccagtc agcagactcc cccacttcca ccgtatgcat ccccacgttc tccgccggtc 840 cagcctcaca caccagtgac aatctcgttg ggaacggccc catccttgca gaacaatcaa 900 cctgtggagt ttaatcatgc catcaactat gttaataaga tcaagaacag atttcagggc 960 caaccagaca tctacaaagc attcctggag attttgcaca catatcagaa agagcagaga 1020 aatgccaagg aagctggagg aaactacact ccagcattga cagagcagga ggtgtatgcc 1080 caggttgctc gtctctttaa aaaccaggaa gatttgttgt cagagtttgg acaattccta 1140 ccagatgcca acagctccgt gcttttaagc aaaacaactg ctgagaaggt tgattctgtg 1200 agaaatgatc atggaggcac tgtcaagaag ccccaactga acaacaagcc gcagaggccc 1260 agccagaatg gctgccagat ccgcagacat cctacaggaa ccacccctcc agttaagaag 1320 aaacccaaac tgctcaatct gaaggattct tctatggcag atgccagcaa acatggtggt 1380 ggaacagaat cgttattttt tgataaggtc cgaaaggctc ttcggagtgc agaagcctac 1440 gaaaatttcc tacgctgtct tgttattttt aaccaggagg tgatctctcg tgctgagctt 1500 gtgcaactag tctctccttt cctggggaaa ttccctgagt tgtttaattg gtttaaaaac 1560 tttctgggct ataaggagtc tgtacatctg gaaacttatc caaaggagcg agccacagag 1620 ggcattgcta tggagataga ttatgcttct tgtaaacgat tgggctccag ctatcgagcc 1680 ttaccaaaga gttaccagca gcccaagtgt acaggacgga ctcctctctg taaagaggtt 1740 ttaaatgata cctgggtttc cttcccttcg tggtctgagg actctacctt tgtgagttcc 1800 aagaagactc aatatgaaga acatatttat cgttgtgaag atgaacgctt tgagcttgat 1860 gtagttttag agaccaatct ggcaacaatc cgggttctgg aagcaataca gaagaagctt 1920 tcccgcttgt ctgctgaaga acaagccaaa tttcgcttgg acaacaccct tgggggcaca 1980 tcagaagtca tccatagaaa agcactccag aggatatatg ctgataaagc agctgacatc 2040 attgatggtc tgagaaagaa tccctccatt gctgttccaa ttgtccttaa aaggttgaag 2100 atgaaagagg aagaatggcg agaagctcag agaggcttta acaaagtatg gcgagaacaa 2160 aatgagaaat actacttgaa gtctctggac caccagggga tcaactttaa acagaatgac 2220 accaaggtcc tgaggtctaa gagcttactc aatgagattg agagtatcta tgatgagagg 2280 caagagcagg ctacggagga gaatgctggt gtacctgttg gcccacacct ctcacttgcg 2340 tatgaagaca aacaaatact ggaagatgct gctgctctga ttatccacca tgtgaagagg 2400 cagacaggca ttcagaagga ggacaaatat aagataaaac aaatcatgca tcattttatt 2460 ccagatttgc tctttgccca aagaggtgat ctctcagatg tggaggaaga ggaagaagaa 2520 gagatggatg tagatgaagc cacaggggca gttaagaagc acaatggtgt tgggggcagt 2580 ccccctaagt ccaagttact gtttagtaac acagcagctc aaaaattaag aggaatggat 2640 gaagtataca acctcttcta tgtcaacaac aactggtata tttttatgcg actgcaccag 2700 attctctgcc tgaggctgct acggatttgt tcccaagccg aacggcaaat tgaagaagaa 2760 aaccgagaga gagaatggga acgggaagtg ctgggcataa agcgagacaa gagtgacagc 2820 cctgccattc agctacgtct caaagaacct atggatgttg atgtagaaga ttattaccca 2880 gctttcctgg acatggtgcg gagcctgctg gatggcaaca tagactcatc acagtatgaa 2940 gattcactga gagagatgtt caccattcat gcctacattg cctttaccat ggacaaactg 3000 atccagagca ttgtcagaca gctgcagcat atcgtgagtg atgagatctg tgtgcaggtg 3060 actgaccttt acctggcaga aaataataat ggggccaccg gaggccagct gaacacacag 3120 aactcaagga gcctcctgga gtcaacgtat cagcggaaag ctgagcagct aatgtcagat 3180 gagaattgct ttaagcttat gtttattcag agccaaggcc aggtccagct gactattgag 3240 cttctggaca cagaagagga gaattcggat gaccctgtgg aagcagagcg ctggtcagac 3300 tacgtggagc gatacatgaa ttcagatact acctcgcctg agcttcgtga acatctagca 3360 cagaaaccag tatttctccc caggaatcta cggcggatcc ggaagtgtca acgtggtcga 3420 gagcagcagg aaaaggaagg gaaggaagga aacagcaaga agaccatgga gaatgtggat 3480 agtctggata agctggagtg tagattcaag ctgaattcct acaagatggt gtatgtgatc 3540 aaatcagagg actatatgta tcggaggacc gccctgctcc gggctcatca gtcccatgag 3600 cgtgtaagca agcgtctaca tcagagattc caggcctggg tagataaatg gaccaaggag 3660 catgtgcccc gtgaaatggc agcagagacc agcaagtggc tcatgggtga ggggctggag 3720 ggcctggtgc cctgtaccac cacctgtgat acagagaccc tgcattttgt gagcattaac 3780 aagtatcgtg tcaaatacgg cacagtattc aaagcccctt aa 3822 <110> Ajou University <120> Biomarkers for diagnosing early progression of cervical cancer, and uses it <130> ADP-2023-0218 <160> 8 <170> KoPatentIn 3.0 <210> 1 <211> 23 <212> DNA <213> Artificial Sequence <220> <223>hsa-miR-92a-1-5p <400> 1 agguugggau cgguugcaau gcu 23 <210> 2 <211> 720 <212> DNA <213> Artificial Sequence <220> <223> DIABLO mRNA <400> 2 atggcggctc tgaagagttg gctgtcgcgc agcgtaactt cattcttcag gtacagacag 60 tgtttgtgtg ttcctgttgt ggctaacttt aagaagcggt gtttctcaga attgataaga 120 ccatggcaca aaactgtgac gattggcttt ggagtaaccc tgtgtgcggt tcctattgca 180 cagaaatcag agcctcattc ccttagtagt gaagcattga tgaggagagc agtgtctttg 240 gtaacagata gcacctctac ctttctctct cagaccacat atgcgttgat tgaagctatt 300 actgaatata ctaaggctgt ttatacctta acttctcttt accgacaata tacaagttta 360 cttgggaaaa tgaattcaga ggaggaagat gaagtgtggc aggtgatcat aggagccaga 420 gctgagatga cttcaaaaca ccaagagtac ttgaagctgg aaaccacttg gatgactgca 480 gttggtcttt cagagatggc agcagaagct gcatatcaaa ctggcgcaga tcaggcctct 540 ataaccgcca ggaatcacat tcagctggtg aaactgcagg tggaagaggt gcaccagctc 600 tcccggaaag cagaaaccaa gctggcagaa gcacagatag aagagctccg tcagaaaaca 660 caggaggaag gggaggagcg ggctgagtcg gagcaggagg cctacctgcg tgaggattga 720 720 <210> 3 <211> 20 <212> DNA <213> Artificial Sequence <220> <223>hsa-miR-3960 <400> 3 ggcggcggcg gaggcggggg 20 <210> 4 <211> 22 <212> DNA <213> Artificial Sequence <220> <223>hsa-miR-202-5p <400> 4 uuccuaugca uauacuucuu ug 22 <210> 5 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-3928-5p <400> 5 ugaagcucua agguuccgcc ugc 23 <210> 6 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-574-3p <400> 6 cacgcucaug cacacaccca ca 22 <210> 7 <211> 2058 <212> DNA <213> Artificial Sequence <220> <223> UBC mRNA <400> 7 atgcagatct tcgtgaagac tctgactggt aagaccatca ccctcgaggt tgagcccagt 60 gacaccatcg agaatgtcaa ggcaaagatc caagataagg aaggcatccc tcctgaccag 120 cagaggctga tctttgctgg aaaacagctg gaagatgggc gcaccctgtc tgactacaac 180 atccagaaag agtccaccct gcacctggtg ctccgtctca gaggtgggat gcaaatcttc 240 gtgaagacac tcactggcaa gaccatcacc cttgaggtcg agcccagtga caccatcgag 300 aacgtcaaag caaagatcca ggacaaggaa ggcattcctc ctgaccagca gaggttgatc 360 tttgccggaa agcagctgga agatgggcgc accctgtctg actacaacat ccagaaagag 420 tctaccctgc acctggtgct ccgtctcaga ggtgggatgc agatcttcgt gaagaccctg 480 actggtaaga ccatcaccct cgaggtggag cccagtgaca ccatcgagaa tgtcaaggca 540 aagatccaag ataaggaagg cattcctcct gatcagcaga ggttgatctt tgccggaaaa 600 cagctggaag atggtcgtac cctgtctgac tacaacatcc agaaagagtc caccttgcac 660 ctggtactcc gtctcagagg tgggatgcaa atcttcgtga agacactcac tggcaagacc 720 atcacccttg aggtcgagcc cagtgacact atcgagaacg tcaaagcaaa gatccaagac 780 aaggaaggca ttcctcctga ccagcagagg ttgatctttg ccggaaagca gctggaagat 840 gggcgcaccc tgtctgacta caacatccag aaagagtcta ccctgcacct ggtgctccgt 900 ctcagaggtg ggatgcagat cttcgtgaag accctgactg gtaagaccat cactctcgaa 960 gtggagccga gtgacaccat tgagaatgtc aaggcaaaga tccaagacaa ggaaggcatc 1020 cctcctgacc agcagaggtt gatctttgcc ggaaaacagc tggaagatgg tcgtaccctg 1080 tctgactaca acatccagaa agagtccacc ttgcacctgg tgctccgtct cagaggtggg 1140 atgcagatct tcgtgaagac cctgactggt aagaccatca ctctcgaggt ggagccgagt 1200 gacaccatg agaatgtcaa ggcaaagatc caagacaagg aaggcatccc tcctgaccag 1260 cagaggttga tctttgctgg gaaacagctg gaagatggac gcaccctgtc tgactacaac 1320 atccagaaag agtccaccct gcacctggtg ctccgtctta gaggtgggat gcagatcttc 1380 gtgaagaccc tgactggtaa gaccatcact ctcgaagtgg agccgagtga caccattgag 1440 aatgtcaagg caaagatcca agacaaggaa ggcatccctc ctgaccagca gaggttgatc 1500 tttgctggga aacagctgga agatggacgc accctgtctg actacaacat ccagaaagag 1560 tccaccctgc acctggtgct ccgtcttaga ggtgggatgc agatcttcgt gaagaccctg 1620 actggtaaga ccatcactct cgaagtggag ccgagtgaca ccattgagaa tgtcaaggca 1680 aagatccaag acaaggaagg catccctcct gaccagcaga ggttgatctt tgctgggaaa 1740 cagctggaag atggacgcac cctgtctgac tacaacatcc agaaagagtc caccctgcac 1800 ctggtgctcc gtctcagagg tgggatgcaa atcttcgtga agaccctgac tggtaagacc 1860 atcaccctcg aggtggagcc cagtgacacc atcgagaatg tcaaggcaaa gatccaagat 1920 aaggaaggca tccctcctga tcagcagagg ttgatctttg ctgggaaaca gctggaagat 1980 ggacgcaccc tgtctgacta caacatccag aaagagtcca ctctgcactt ggtcctgcgc 2040 ttgagggggg gtgtctaa 2058 <210> 8 <211> 3822 <212> DNA <213> Artificial Sequence <220> <223> SIN3A mRNA <400> 8 atgaagcggc gtttggatga ccaggagtca ccggtgtatg cagcccagca gcgtcggatc 60 cctggcagca cagaggcttt tcctcaccag caccgggtgc ttgcccctgc ccctcctgtg 120 tatgaagcag tgtctgagac catgcagtca gctacgggaa ttcagtactc tgtaacaccc 180 agctaccagg tttcagccat gccacagagc tccggcagtc atgggcccgc tatagcagca 240 gttcatagca gccatcatca cccaacagcg gtgcagcccc acggaggcca ggtggtccag 300 agtcatgctc atccagcccc accagttgca ccagtgcagg gacagcagca atttcagagg 360 ctgaaggtgg aggatgcgct atcttatctt gaccaggtga agctgcagtt tggtagtcag 420 cctcaggtct acaatgattt ccttgacatc atgaaggaat ttaaatctca gagcatcgac 480 accccagggag tgattagtcg tgtgtcccag ctattcaaag gccaccccga tctgataatg 540 ggattcaaca ccttcttgcc ccctggctac aaaattgagg tgcaaaccaa tgacatggtg 600 aatgtgacaa ctcctggcca ggttcatcag attcccaccc atggcatcca gccacagcct 660 caaccaccac cccaacatcc ttcccagcct tcagcccagt cagccccagc tcctgcccag 720 ccagctcctc agcccccacc tgccaaagtc agcaagccct cccaactgca agcacatact 780 ccggccagtc agcagactcc cccacttcca ccgtatgcat ccccacgttc tccgccggtc 840 cagcctcaca caccagtgac aatctcgttg ggaacggccc catccttgca gaacaatcaa 900 cctgtggagt ttaatcatgc catcaactat gttaataaga tcaagaacag atttcagggc 960 caaccagaca tctacaaagc attcctggag attttgcaca catatcagaa agagcagaga 1020 aatgccaagg aagctggagg aaactacact ccagcattga cagagcagga ggtgtatgcc 1080 caggttgctc gtctctttaa aaaccaggaa gatttgttgt cagagtttgg acaattccta 1140 ccagatgcca acagctccgt gcttttaagc aaaacaactg ctgagaaggt tgattctgtg 1200 agaaatgatc atggaggcac tgtcaagaag ccccaactga acaacaagcc gcagaggccc 1260 agccagaatg gctgccagat ccgcagacat cctacaggaa ccacccctcc agttaagaag 1320 aaacccaaac tgctcaatct gaaggattct tctatggcag atgccagcaa acatggtggt 1380 ggaacagaat cgttatttt tgataaggtc cgaaaggctc ttcggagtgc agaagcctac 1440 gaaaatttcc tacgctgtct tgttatttt aaccaggagg tgatctctcg tgctgagctt 1500 gtgcaactag tctctccttt cctggggaaa ttccctgagt tgtttaattg gtttaaaaac 1560 tttctgggct ataaggagtc tgtacatctg gaaacttatc caaaggagcg agccacagag 1620 ggcattgcta tggagataga ttatgcttct tgtaaacgat tgggctccag ctatcgagcc 1680 ttaaccaaaga gttaccagca gcccaagtgt acaggacgga ctcctctctg taaagaggtt 1740 ttaaatgata cctgggtttc cttcccttcg tggtctgagg actctacctt tgtgagttcc 1800 aagaagactc aatatgaaga acatatttat cgttgtgaag atgaacgctt tgagcttgat 1860 gtagttttag agaccaatct ggcaacaatc cgggttctgg aagcaataca gaagaagctt 1920 tcccgcttgt ctgctgaaga acaagccaaa tttcgcttgg acaacaccct tgggggcaca 1980 tcagaagtca tccatagaaa agcactccag aggatatatg ctgataaagc agctgacatc 2040 attgatggtc tgagaaagaa tccctccatt gctgttccaa ttgtccttaa aaggttgaag 2100 atgaaagagg aagaatggcg agaagctcag agaggcttta acaaagtatg gcgagaacaa 2160 aatgagaaat actacttgaa gtctctggac caccagggga tcaactttaa acagaatgac 2220 accaaggtcc tgaggtctaa gagcttactc aatgagattg agagtatcta tgatgagagg 2280 caagagcagg ctacggagga gaatgctggt gtacctgttg gcccacacct ctcacttgcg 2340 tatgaagaca aacaaatact ggaagatgct gctgctctga ttatccacca tgtgaagagg 2400 cagacaggca ttcagaagga ggacaaatat aagataaaac aaatcatgca tcattttatt 2460 ccagatttgc tctttgccca aagaggtgat ctctcagatg tggaggaaga ggaagaagaa 2520 gagatggatg tagatgaagc cacaggggca gttaagaagc acaatggtgt tgggggcagt 2580 ccccctaagt ccaagttact gtttagtaac acagcagctc aaaaattaag aggaatggat 2640 gaagtataca acctcttcta tgtcaacaac aactggtata tttttatgcg actgcaccag 2700 attctctgcc tgaggctgct acggatttgt tcccaagccg aacggcaaat tgaagaagaa 2760 aaccgagaga gagaatggga acgggaagtg ctgggcataa agcgagacaa gagtgacagc 2820 cctgccattc agctacgtct caaagaacct atggatgttg atgtagaaga ttattaccca 2880 gctttcctgg acatggtgcg gagcctgctg gatggcaaca tagactcatc acagtatgaa 2940 gattcactga gagagatgtt caccattcat gcctacattg cctttaccat ggacaaactg 3000 atccagagca ttgtcagaca gctgcagcat atcgtgagtg atgagatctg tgtgcaggtg 3060 actgaccttt acctggcaga aaataataat ggggccaccg gaggccagct gaacacacag 3120 aactcaagga gcctcctgga gtcaacgtat cagcggaaag ctgagcagct aatgtcagat 3180 gagaattgct ttaagcttat gtttatcag agccaaggcc aggtccagct gactattgag 3240 cttctggaca cagaagagga gaattcggat gaccctgtgg aagcagagcg ctggtcagac 3300 tacgtggagc gatacatgaa ttcagatact acctcgcctg agcttcgtga acatctagca 3360 cagaaaccag tatttctccc caggaatcta cggcggatcc ggaagtgtca acgtggtcga 3420 gagcagcagg aaaaggaagg gaaggaagga aacagcaaga agaccatgga gaatgtggat 3480 agtctggata agctggagtg tagattcaag ctgaattcct acaagatggt gtatgtgatc 3540 aaatcagagg actatatgta tcggaggacc gccctgctcc gggctcatca gtcccatgag 3600 cgtgtaagca agcgtctaca tcagagattc caggcctggg tagataaatg gaccaaggag 3660 catgtgcccc gtgaaatggc agcagagacc agcaagtggc tcatgggtga ggggctggag 3720 ggcctggtgc cctgtaccac cacctgtgat acagagaccc tgcattttgt gagcattaac 3780 aagtatcgtg tcaaatacgg cacagtattc aaagcccctt aa 3822
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020230051423A KR102601998B1 (en) | 2020-11-11 | 2023-04-19 | Kit for diagnosing acute tumor response of cervical cancer |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020200150116A KR102534201B1 (en) | 2020-11-11 | 2020-11-11 | Biomarkers for diagnosing early progression of cervical cancer, and uses thereof |
| KR1020230051423A KR102601998B1 (en) | 2020-11-11 | 2023-04-19 | Kit for diagnosing acute tumor response of cervical cancer |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
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| KR1020200150116A Division KR102534201B1 (en) | 2020-11-11 | 2020-11-11 | Biomarkers for diagnosing early progression of cervical cancer, and uses thereof |
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| Publication Number | Publication Date |
|---|---|
| KR20230057326A KR20230057326A (en) | 2023-04-28 |
| KR102601998B1 true KR102601998B1 (en) | 2023-11-14 |
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Family Applications (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020200150116A KR102534201B1 (en) | 2020-11-11 | 2020-11-11 | Biomarkers for diagnosing early progression of cervical cancer, and uses thereof |
| KR1020230051422A KR102546810B1 (en) | 2020-11-11 | 2023-04-19 | Composition for diagnosing acute tumor response of cervical cancer |
| KR1020230051424A KR102546809B1 (en) | 2020-11-11 | 2023-04-19 | Method of providing information for diagnosing acute tumor response of cervical cancer |
| KR1020230051423A KR102601998B1 (en) | 2020-11-11 | 2023-04-19 | Kit for diagnosing acute tumor response of cervical cancer |
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| KR1020200150116A KR102534201B1 (en) | 2020-11-11 | 2020-11-11 | Biomarkers for diagnosing early progression of cervical cancer, and uses thereof |
| KR1020230051422A KR102546810B1 (en) | 2020-11-11 | 2023-04-19 | Composition for diagnosing acute tumor response of cervical cancer |
| KR1020230051424A KR102546809B1 (en) | 2020-11-11 | 2023-04-19 | Method of providing information for diagnosing acute tumor response of cervical cancer |
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| WO2009036332A1 (en) * | 2007-09-14 | 2009-03-19 | Asuragen, Inc. | Micrornas differentially expressed in cervical cancer and uses thereof |
| US20100234445A1 (en) * | 2008-07-01 | 2010-09-16 | Weng Onn Lui | Patterns of known and novel small RNAS in human cervical cancer |
| KR102096498B1 (en) | 2018-03-27 | 2020-05-27 | 순천향대학교 산학협력단 | MicroRNA-4732-5p for diagnosing or predicting recurrence of colorectal cancer and use thereof |
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Non-Patent Citations (3)
| Title |
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| Ann Transl Med, 3(18): 261 (2015.10.30.) |
| Chin Med J (Engl), 118(3): 226-230 (2005.02.05.) |
| Exp Ther Med, 14(2): 1227-1234 (2017.06.13.) |
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| KR20230057326A (en) | 2023-04-28 |
| WO2022103027A1 (en) | 2022-05-19 |
| KR102546809B1 (en) | 2023-06-23 |
| KR20220064044A (en) | 2022-05-18 |
| KR102546810B1 (en) | 2023-06-23 |
| KR20230056645A (en) | 2023-04-27 |
| KR20230056646A (en) | 2023-04-27 |
| KR102534201B1 (en) | 2023-05-19 |
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