CN112877429B - 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 - Google Patents
基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 Download PDFInfo
- Publication number
- CN112877429B CN112877429B CN202110080948.2A CN202110080948A CN112877429B CN 112877429 B CN112877429 B CN 112877429B CN 202110080948 A CN202110080948 A CN 202110080948A CN 112877429 B CN112877429 B CN 112877429B
- Authority
- CN
- China
- Prior art keywords
- gene
- liver cancer
- expression amount
- prognosis
- gene expression
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 201000007270 liver cancer Diseases 0.000 title claims abstract description 57
- 208000014018 liver neoplasm Diseases 0.000 title claims abstract description 57
- 238000004393 prognosis Methods 0.000 title claims abstract description 33
- 108090000623 proteins and genes Proteins 0.000 title claims abstract description 31
- 230000035945 sensitivity Effects 0.000 title claims abstract description 21
- 230000007774 longterm Effects 0.000 title claims abstract description 15
- 238000001514 detection method Methods 0.000 title claims abstract description 13
- 239000003560 cancer drug Substances 0.000 title claims abstract description 4
- 230000006536 aerobic glycolysis Effects 0.000 claims abstract description 48
- MLDQJTXFUGDVEO-UHFFFAOYSA-N BAY-43-9006 Chemical compound C1=NC(C(=O)NC)=CC(OC=2C=CC(NC(=O)NC=3C=C(C(Cl)=CC=3)C(F)(F)F)=CC=2)=C1 MLDQJTXFUGDVEO-UHFFFAOYSA-N 0.000 claims abstract description 27
- 239000005511 L01XE05 - Sorafenib Substances 0.000 claims abstract description 27
- 229960003787 sorafenib Drugs 0.000 claims abstract description 27
- 238000000034 method Methods 0.000 claims abstract description 7
- 230000037361 pathway Effects 0.000 claims abstract description 6
- 238000000611 regression analysis Methods 0.000 claims abstract description 6
- 238000012216 screening Methods 0.000 claims abstract description 3
- 230000014509 gene expression Effects 0.000 claims description 48
- 239000003153 chemical reaction reagent Substances 0.000 claims description 9
- 239000000523 sample Substances 0.000 claims description 7
- 101150047078 G6PD gene Proteins 0.000 claims description 6
- 101150030514 GPC1 gene Proteins 0.000 claims description 6
- 101150029182 Hmmr gene Proteins 0.000 claims description 6
- 101000595907 Homo sapiens Procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 Proteins 0.000 claims description 6
- 101150088335 STC2 gene Proteins 0.000 claims description 6
- 101150025593 Srd5a3 gene Proteins 0.000 claims description 6
- 101150042043 TKTL1 gene Proteins 0.000 claims description 6
- 101150041530 ldha gene Proteins 0.000 claims description 6
- 101150037747 rars1 gene Proteins 0.000 claims description 6
- 101150053833 Cenpa gene Proteins 0.000 claims description 5
- 102100035198 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 Human genes 0.000 claims description 5
- 238000003559 RNA-seq method Methods 0.000 claims description 5
- 101150075063 HOMER1 gene Proteins 0.000 claims description 4
- 101150100264 GOT2 gene Proteins 0.000 claims description 3
- 101150058068 SLC2A1 gene Proteins 0.000 claims description 3
- 101150116689 Slc2a2 gene Proteins 0.000 claims description 3
- 239000003795 chemical substances by application Substances 0.000 claims 2
- 108700026220 vif Genes Proteins 0.000 claims 2
- 238000002560 therapeutic procedure Methods 0.000 abstract description 11
- 210000005229 liver cell Anatomy 0.000 abstract 1
- 230000004083 survival effect Effects 0.000 description 24
- 206010028980 Neoplasm Diseases 0.000 description 11
- 230000002414 glycolytic effect Effects 0.000 description 7
- 238000012163 sequencing technique Methods 0.000 description 6
- 210000004027 cell Anatomy 0.000 description 5
- 230000000875 corresponding effect Effects 0.000 description 4
- 206010059866 Drug resistance Diseases 0.000 description 3
- 230000002596 correlated effect Effects 0.000 description 3
- 239000003814 drug Substances 0.000 description 3
- 230000008595 infiltration Effects 0.000 description 3
- 238000001764 infiltration Methods 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 101150082072 14 gene Proteins 0.000 description 2
- 238000009098 adjuvant therapy Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 210000004204 blood vessel Anatomy 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 229940079593 drug Drugs 0.000 description 2
- 206010073071 hepatocellular carcinoma Diseases 0.000 description 2
- 230000004060 metabolic process Effects 0.000 description 2
- 230000019491 signal transduction Effects 0.000 description 2
- 230000001225 therapeutic effect Effects 0.000 description 2
- 210000004881 tumor cell Anatomy 0.000 description 2
- 101150084750 1 gene Proteins 0.000 description 1
- 208000031404 Chromosome Aberrations Diseases 0.000 description 1
- 206010067477 Cytogenetic abnormality Diseases 0.000 description 1
- 206010064571 Gene mutation Diseases 0.000 description 1
- 206010027476 Metastases Diseases 0.000 description 1
- 108700020796 Oncogene Proteins 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 239000002246 antineoplastic agent Substances 0.000 description 1
- 229940041181 antineoplastic drug Drugs 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000034659 glycolysis Effects 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 101150044508 key gene Proteins 0.000 description 1
- 230000036210 malignancy Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- 230000009401 metastasis Effects 0.000 description 1
- 238000011119 multifactor regression analysis Methods 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000010837 poor prognosis Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 230000009790 vascular invasion Effects 0.000 description 1
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/118—Prognosis of disease development
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Organic Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Engineering & Computer Science (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Zoology (AREA)
- Genetics & Genomics (AREA)
- Wood Science & Technology (AREA)
- Physics & Mathematics (AREA)
- Biotechnology (AREA)
- Microbiology (AREA)
- Molecular Biology (AREA)
- Hospice & Palliative Care (AREA)
- Biophysics (AREA)
- Oncology (AREA)
- Biochemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
本发明公开了一种基于基因检测判断肝癌药物敏感性和远期预后的预测工具，本发明通过统计分析TCGA数据中的与肝癌预后相关有氧糖酵解通路基因，在此基础上采用LASSO回归分析简化预后相关基因，建立基于有氧糖酵解通路基因的预测工具，简称有氧糖酵解指数。将该指数于多个公共数据库及邵逸夫医院临床样本中验证，发现该指数可准确预测肝癌患者对索拉菲尼疗法的敏感和远期预后。本发明可以有效的筛选对索拉菲尼疗法敏感的肝细胞肝癌患者，为肝癌患者精准、综合治疗提供新思路。
Description
技术领域
本发明属于生物技术和医学领域，具体地，本发明涉及抗肿瘤耐药相关的基因检测及其应用。
背景技术
肝癌是我国也是世界范围内第六常见的恶性肿瘤，同时在肿瘤相关死因中排名第四位。尽管治疗方法上取得了长足发展，但肝癌的五年生存率仍在25％～55％之间。远处转移、肝内复发以及对各种疗法敏感性不高是肝癌预后差的主要原因。基因突变、染色体异常、细胞信号通路异常与肝癌发生、发展密切相关。通过分子生物学特点对肝癌进行分型有助于实现精准治疗，改善肝癌患者预后。
有氧糖酵解是肿瘤恶性程度的一大标志性特征，其主要是指即使在氧浓度处于生理浓度时，肿瘤细胞仍然大量通过糖酵解的途径获得能量。通过这种糖代谢模式的改变，肿瘤细胞在快速获得能量的同时，还产生了大量生理合成所需的代谢产物。此外有氧糖酵解与多种癌基因信号通路密切相关。因此，通过有氧糖酵解水平将肝癌进行可能揭示新的肝癌分子分型。
索拉菲尼目前是晚期肝癌的一线治疗药物。但是索拉菲尼耐药现象在临床中非常常见。如何筛选出对索拉菲尼疗法敏感的患者，精准用药，对改善肝癌患者预后至关重要。肿瘤代谢、肿瘤微环境改变、表观遗传学等也被认为可能与肝癌索拉非尼耐药相关。但主导机制或者说关键基因仍然是目前困扰肝癌对索拉非尼耐药研究的主要难题。
因此，本领域迫切需要寻找能够预测肝癌对索拉菲尼敏感性和肝癌远期预后的新方法，通过此实现精准治疗，改善患者预后。
发明内容
本发明的目的是针对现有技术的不足，寻找一种新的预测肝癌对索拉菲尼敏感性和远期预后的预测工具。
本发明是通过以下技术方案来实现的：
1.通过使用单因素Cox回归模型统计筛选TCGA数据中与肝癌预后相关有氧糖酵解通路基因；
2.在此基础上采用LASSO回归分析简化预后相关基因，建立基于有氧糖酵解通路基因的预测工具，简称有氧糖酵解指数；有氧糖酵解指数＝LDHA基因表达量*0.163+STC2基因表达量*0.004+GPC1基因表达量*0.034+TKTL1基因表达量*0.0001+SLC2A1基因表达量*0.014+SRD5A3基因表达量*0.032+PLOD2基因表达量*0.070+G6PD基因表达量*0.083+HMMR基因表达量*0.040+HOMER1基因表达量*0.001+RARS1基因表达量*0.132-GOT2基因表达量*0.146+CENPA基因表达量*0.053-SLC2A2基因表达量*0.001。
其中，基因表达量的检测方法/技术包括：二代RNA测序或三代RNA测序或基因芯片技术。
3.将该指数于多个公共数据库及邵逸夫医院临床样本中验证，发现该指数可准确预测肝癌患者远期预后；利用“survminer”数据包，根据数据集的生存资料，获取数据集相应检测方法对应的有氧糖酵解指数最佳阈值，若患者有氧糖酵解指数高于阈值，则指示肝癌患者预后差，反之，则指示肝癌患者预后好。
4.将该指数于GDSC和CCLE数据库及“STORM”试验临床样本中验证，发现该指数与索拉菲尼敏感性负相关，并可准确预测患者对索拉菲尼疗法的敏感。利用“survminer”数据包，根据数据集的生存资料，获取数据集相应检测方法对应的有氧糖酵解指数最佳阈值，若患者有氧糖酵解指数高于阈值，则指示患者对索拉菲尼疗法敏感性差，反之，则指示患者对索拉菲尼疗法敏感性好。
本发明还提供了一种基于基因检测判断肝癌药物敏感性和远期预后的试剂盒，包含用于测量LDHA基因、STC2基因、GPC1基因、TKTL1基因、SLC2A1基因、SRD5A3基因、PLOD2基因、G6PD基因、HMMR基因、HOMER1基因、RARS1基因、GOT2基因、CENPA基因、SLC2A2基因表达量的试剂。
优选地，所述的试剂为与所述基因特异性结合的引物或探针。
本发明的有益效果是：本发明的指数仅基于14个基因表达水平，方法简单，预测准确性高，易于推广，具有非常好的临床转化价值。
附图说明
下面结合附图和实施例对本发明进一步说明；
图1为单因素Cox分析提示80个有氧糖酵解相关基因与肝癌预后相关；
图2为LASSO回归分析简化预后相关基因，建立了基于14个基因表达量的有氧糖酵解指数；
图3是有氧糖酵解指数可预测TCGA数据库中肝癌病人的总体生存率(a)和无瘤生存率(b)曲线图；图中，2表示低AGI的生存曲线，1、3分别是低AGI的生存曲线的误差线；5表示高AGI的生存曲线，4、6分别是高AGI的生存曲线的误差线。
图4是TCGA-LIHC数据的ROC曲线图；
图5是有氧糖酵解指数可预测GSE14520(a)和LIRI-JP数据库(b)及邵逸夫医院中肝癌病人(c)的总体生存率曲线图；图中，2表示低AGI的生存曲线，1、3分别是低AGI的生存曲线的误差线；5表示高AGI的生存曲线，4、6分别是高AGI的生存曲线的误差线。
图6是GDSC(a)和CCLE数据库(b)中肝癌细胞系对索拉菲尼敏感性与有氧糖酵解指数负相关的曲线图；
图7是“STORM”临床数据显示有氧糖酵解指数可以预测对索拉菲尼疗法反应。
图8是“STORM”临床数据的AUC曲线图。
具体实施方式
下面通过实验并结合实例对本发明做进一步说明，应该理解的是，这些实施例仅用于例证的目的，决不限制本发明的保护范围。
本实施例所涉及测序和临床数据及试剂来源：
TCGA-LIHC数据下载于UCSC数据库(https://xenabrowser.net/datapages),LIRI-JP数据下载于HCCDB数据库(http://lifeome.net/database/hccdb/ download.html).GSE14520和GSE109211数据下载于GEO数据库(https://www.ncbi.nlm.nih.gov/geo/).肝癌细胞系对索拉菲尼敏感性数据下载于GDSC数据库(https://www.cancerrxgene.org)和CCLE数据库(https:// portals.broadinstitute.org/ccle/data).邵逸夫医院数据源自浙江大学医学院附属邵逸夫医院2008年1月至2018年1月的102例就诊病例，该102例就诊病例中，均确诊为肝癌，TNM分期为Ⅰ-Ⅳ，T分期为T1-T4，年龄为32-88岁，临床随访时间大于2年。
实施例：
选取TCGA-LIHC数据中371例肝癌病人测序数据和临床随访信息，通过单因素COX回归分析有氧糖酵解基因对该371例患者总体生存率影响。结果显示，共有80个基因显著影响肝癌患者总体生存率，如图1所示。
通过LASSO回归分析，将简化预后相关基因，建立了基于14个基因表达量的有氧糖酵解指数，如图2所示，赋值具体为有氧糖酵解指数(AGI)＝LDHA基因表达量*0.163+STC2基因表达量*0.004+GPC1基因表达量*0.034+TKTL1基因表达量*0.0001+SLC2A1基因表达量*0.014+SRD5A3基因表达量*0.032+PLOD2基因表达量*0.070+G6PD基因表达量*0.083+HMMR基因表达量*0.040+HOMER1基因表达量*0.001+RARS1基因表达量*0.132-GOT2基因表达量*0.146+CENPA基因表达量*0.053-SLC2A2基因表达量*0.001。
基于有氧糖酵解指数(AGI)可以对病例进行分组，其中分组的阈值是所分开的两组病人预后差异最大的点，如根据病人生存资料，使用R语言软件“survminer”数据包获取最佳阈值，需要指出的是，针对不同的测序方法，阈值会有所不同。下面结合具体的验证集进行详细说明：
在TCGA-LIHC数据中验证有氧糖酵解指数对肝癌患者远期预后的影响，即通过Illumina HiSeq 2000RNA测序平台，检测病人肝癌组织各基因表达水平，经标准化处理后，计算各个肝癌病人有氧糖酵解指数，根据病人生存资料，使用R语言软件“survminer”数据包，取最佳阈值4.05，有氧糖酵解指数低于4.05为低有氧糖酵解指数组低有氧糖酵解指数组(低AGI组)，有氧糖酵解指数高于4.05为高有氧糖酵解指数组(高AGI组)。其中，通过Kaplan-Meier生存曲线和log-rank生存分析发现有氧糖酵解指数提示高AGI组的肝癌患者更差的远期预后，包括总体生存率和无瘤生存率，如图3所示。
同时应用ROC曲线图评价本实施例模型的临床准确性，ROC曲线图见图4，横坐标是1-特异度，纵坐标是灵敏度，五年生存率为节点的情况下，当取4.05时，其特异度为0.65，敏感性为0.69，计算模型的AUC值为0.714，说明模型预测结果准确性较高。ROC曲线下的面积值在1.0和0.5之间，在AUC大于0.5的情况下，AUC越接近于1，说明诊断效果越好。
进一步地，采用COX回归分析在TCGA中验证有氧糖酵解指数对肝癌患者远期预后的相关危险因素，多因素回归分析发现年龄(大于等于60岁，对照小于60岁)、性别(男，对照女)、肿瘤分化程度(G3级、G2级，对照G1级)、肿瘤分期(IV期、III期、II期，对照I期)、血管侵犯(大血管浸润、微浸润，对照无浸润)等临床指标均不是肝癌患者远期预后的独立危险因素，有氧糖酵解指数是肝癌患者远期预后的独立危险因素，如图5所示。该结果说明，利用本发明的有氧糖酵解指数能够独立地预测肝癌患者的远期预后，且不受年龄、性别、肿瘤分化程度、肿瘤分期、血管侵犯等临床指标的影响。
在GSE14520数据库243例肝癌患者、LIRI-JP数据库中200例肝癌患者、和邵逸夫医院102肝癌患者中进一步验证有氧糖酵解指数对肝癌患者远期预后的影响，同样地，分别通过Affymetrix Human Genome U133A 2.0Array(GSE14520)、Illumina RNA-Seq(LIRI-JP)、Illumina(邵逸夫医院)测序平台，检测病人肝癌组织各基因表达水平，经标准化处理后，计算各个肝癌病人有氧糖酵解指数并取最佳阈值(3.245(GSE14520)、1.785(LIRI-JP)、1.64(邵逸夫医院))，有氧糖酵解指数低于最佳阈值为低AGI组，AGI高于最佳阈值为高AGI组。有氧糖酵解指数提示高AGI组的肝癌患者更差的总体生存率，如图6所示。
在GDSC数据库中提示肝癌细胞系对索拉菲尼IC50浓度与有氧糖酵解指数成正相关。在CCLE数据库中提示肝癌细胞系对索拉菲尼EC50浓度与有氧糖酵解指数成正相关，如图7a、7b所示。
在“STORM”数据库67例接受索拉菲尼治疗的肝癌患者中，有氧糖酵解指数可有效预测肝癌患者对索拉菲尼的敏感性，曲线下面积为0.879，如图8所示。其中，阈值3.488对应的敏感性为0.905、特异度为0.848。
本实施例还提供一种利用本发明预测患者对索拉菲尼疗法的敏感性方法，具体包括如下步骤：
1.收取肝癌患者组织样本(如手术标本、穿刺标本等)，提取组织中总RNA。
2.选取合适的测序平台检测有氧糖酵解指数相关基因，计算有氧糖酵解指数。
3.根据已经建立的有氧糖酵解指数数据库，通过参照数据库中有氧糖酵解指数最佳阈值，判断样本的有氧糖酵解指数水平。
4.若被检样本有氧糖酵解指数水平低于阈值，则该病人预后较好，对索拉菲尼疗法敏感，可进行索拉菲尼辅助治疗，改善预后。反之，若被检样本有氧糖酵解指数水平高于阈值，则该病人预后较差，对索拉菲尼疗法不敏感，不适合索拉菲尼辅助治疗。
Claims (5)
1.一种基因检测试剂在制备判断患者对肝癌药物索拉非尼敏感性试剂盒中的应用，其中所述基因检测试剂为测量LDHA基因、STC2基因、GPC1基因、TKTL1基因、SLC2A1基因、SRD5A3基因、PLOD2基因、G6PD基因、HMMR基因、HOMER1 基因、RARS1基因、GOT2基因、CENPA基因和SLC2A2基因表达量的试剂。
2.根据权利要求1所述的应用，其特征在于，试剂为与所述基因特异性结合的引物或探针。
3.一种基因检测试剂在制备肝癌患者远期预后预测试剂盒中的应用，其中所述基因检测试剂为测量LDHA基因、STC2基因、GPC1基因、TKTL1基因、SLC2A1基因、SRD5A3基因、PLOD2基因、G6PD基因、HMMR基因、HOMER1 基因、RARS1基因、GOT2基因、CENPA基因和SLC2A2基因表达量的试剂。
4.根据权利要求3所述的应用，其特征在于，试剂为与所述基因特异性结合的引物或探针。
5.一种构建预测肝癌患者对索拉菲尼敏感性和远期预后的预测工具的方法，其特征在于，具体为：
（1）通过使用单因素Cox回归模型统计筛选TCGA数据中与肝癌预后相关有氧糖酵解通路基因；
（2）在此基础上采用LASSO回归分析简化预后相关基因，建立基于有氧糖酵解通路基因的预测工具，简称有氧糖酵解指数；有氧糖酵解指数=LDHA基因表达量*0.163+STC2基因表达量*0.004+GPC1基因表达量*0.034+TKTL1基因表达量*0.0001+SLC2A1基因表达量*0.014+SRD5A3基因表达量*0.032+PLOD2基因表达量*0.070+G6PD基因表达量*0.083+HMMR基因表达量*0.040+HOMER1基因表达量*0.001+RARS1基因表达量*0.132-GOT2基因表达量*0.146+CENPA基因表达量*0.053-SLC2A2基因表达量*0.001；
其中，基因表达量的检测技术包括：二代RNA测序或三代RNA测序或基因芯片技术。
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110080948.2A CN112877429B (zh) | 2021-01-21 | 2021-01-21 | 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 |
| PCT/CN2022/072196 WO2022156610A1 (zh) | 2021-01-21 | 2022-01-15 | 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 |
| US18/356,261 US20230366037A1 (en) | 2021-01-21 | 2023-07-21 | Prediction tool for judging drug sensitivity and long-term prognosis of liver cancer based on gene detection and use thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110080948.2A CN112877429B (zh) | 2021-01-21 | 2021-01-21 | 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112877429A CN112877429A (zh) | 2021-06-01 |
| CN112877429B true CN112877429B (zh) | 2022-04-12 |
Family
ID=76051461
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110080948.2A Active CN112877429B (zh) | 2021-01-21 | 2021-01-21 | 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 |
Country Status (3)
| Country | Link |
|---|---|
| US (1) | US20230366037A1 (zh) |
| CN (1) | CN112877429B (zh) |
| WO (1) | WO2022156610A1 (zh) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112877429B (zh) * | 2021-01-21 | 2022-04-12 | 浙江大学 | 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 |
| CN117987550A (zh) * | 2024-01-22 | 2024-05-07 | 深圳慕光生物科技有限公司 | 一种生物标志物、应用及对癌症患者总生存率的预测方法 |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110499364A (zh) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | 一种用于检测扩展型遗传病全外显子的探针组及其试剂盒和应用 |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201901439D0 (en) * | 2019-02-01 | 2019-03-27 | Univ London | Method of predicting survival rates for cancer patients |
| CN111402949B (zh) * | 2020-04-17 | 2023-12-22 | 北京恩瑞尼生物科技股份有限公司 | 一种肝细胞肝癌患者诊断、预后和复发统一模型的构建方法 |
| CN112877429B (zh) * | 2021-01-21 | 2022-04-12 | 浙江大学 | 基于基因检测判断肝癌药物敏感性和远期预后的预测工具及其应用 |
-
2021
- 2021-01-21 CN CN202110080948.2A patent/CN112877429B/zh active Active
-
2022
- 2022-01-15 WO PCT/CN2022/072196 patent/WO2022156610A1/zh active Application Filing
-
2023
- 2023-07-21 US US18/356,261 patent/US20230366037A1/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110499364A (zh) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | 一种用于检测扩展型遗传病全外显子的探针组及其试剂盒和应用 |
Non-Patent Citations (2)
| Title |
|---|
| A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma;Weige Zhou等;《PeerJ》;20200929;第8卷;第e9944页 * |
| Integrated analysis reveals critical glycolytic regulators in hepatocellular carcinoma;Chenying Lu等;《Cell communication and signaling：CCS》;20200623;第18卷(第1期);第1-14页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112877429A (zh) | 2021-06-01 |
| US20230366037A1 (en) | 2023-11-16 |
| WO2022156610A1 (zh) | 2022-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Singh et al. | Screening of over 1000 Indian patients with breast and/or ovarian cancer with a multi-gene panel: prevalence of BRCA1/2 and non-BRCA mutations | |
| Manne et al. | Keynote review: Recent advances in biomarkers for cancer diagnosis and treatment | |
| Pierce et al. | Genome-wide “pleiotropy scan” identifies HNF1A region as a novel pancreatic cancer susceptibility locus | |
| JP2022521492A (ja) | 相同組換え欠損を推定するための統合された機械学習フレームワーク | |
| Kumar et al. | Biomarkers of diseases in medicine | |
| US20230366037A1 (en) | Prediction tool for judging drug sensitivity and long-term prognosis of liver cancer based on gene detection and use thereof | |
| CN113450873A (zh) | 一种预测胃癌预后和免疫治疗适用性的标志物及其应用 | |
| KR102170726B1 (ko) | 바이오마커의 선별 방법 및 이를 이용한 암의 진단을 위한 정보제공방법 | |
| CN111653314B (zh) | 一种分析识别淋巴管浸润的方法 | |
| CN106367527A (zh) | 直肠癌放化疗疗效相关靶基因的鉴定 | |
| Huang et al. | Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma | |
| Zafari et al. | Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer | |
| Dong et al. | Prognostic model development and molecular subtypes identification in bladder urothelial cancer by oxidative stress signatures | |
| CN110004229A (zh) | 多基因作为egfr单克隆抗体类药物耐药标志物的应用 | |
| Yang et al. | Integrated analysis of gene expression and metabolite data reveals candidate molecular markers in colorectal carcinoma | |
| Yang et al. | Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective | |
| CN111088352B (zh) | 一种多基因肝癌预后分级体系的建立方法及应用 | |
| CN114300089B (zh) | 一种中晚期结直肠癌治疗方案决策算法 | |
| CN111763740A (zh) | 基于lncRNA分子模型预测食管鳞癌患者新辅助放化疗的疗效和预后的系统 | |
| Lu et al. | Bioinformatics analysis and identification of genes and molecular pathways involved in venous thromboembolism (VTE) | |
| Zhai et al. | Expression pattern and prognostic value of key regulators for N7-methylguanosine RNA modification in prostate cancer: m7G RNA modification in PCa | |
| CN111748626A (zh) | 用于预测食管鳞癌患者新辅助放化疗的疗效和预后的系统及其应用 | |
| Ni et al. | Unraveling the underlying pathogenic factors driving nonalcoholic steatohepatitis and hepatocellular carcinoma: an in-depth analysis of prognostically relevant gene signatures in hepatocellular carcinoma | |
| Liu et al. | Identification of T‐cell exhaustion‐related gene signature for predicting prognosis in glioblastoma multiforme | |
| Zhang et al. | Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |