CN102183662A - Method for establishing colon cancer prognosis prediction model - Google Patents
Method for establishing colon cancer prognosis prediction model Download PDFInfo
- Publication number
- CN102183662A CN102183662A CN2011100694625A CN201110069462A CN102183662A CN 102183662 A CN102183662 A CN 102183662A CN 2011100694625 A CN2011100694625 A CN 2011100694625A CN 201110069462 A CN201110069462 A CN 201110069462A CN 102183662 A CN102183662 A CN 102183662A
- Authority
- CN
- China
- Prior art keywords
- sparcl1
- model
- drip
- prognosis
- colon cancer
- 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.)
- Pending
Links
Images
Abstract
The invention provides a method for establishing a colon cancer prognosis prediction model. The method comprises the following steps of: detecting the expression levels of SPARCL1 and P53 proteins in colon cancer tissues by immunohistochemistry; grading the tissue expression levels of the SPARCL1 and P53 proteins by a semi-quantitative method; and combining, analyzing and verifying the expression levels of the SPARCL1 and P53 proteins through a support vector machine, and finally establishing a judgment model. Immunohistochemistry detection, marker combination and support vector machine analysis are combined for establishing the colon cancer prediction model. The research shows that the model constructed by using the composition of the SPARCL1 and the P53 as a marker has experiment aid effect of predicting the prognosis of colon cancer patients. The model can be applied in postoperative transfer relapse risk prediction experiments of the colon cancer patients.
Description
Technical field
The invention belongs to biological technical field, specifically, relate to a kind of with SPARCL1 and the P53 method for building up of the colorectal cancer prognosis prediction model of thing as a token of.
Background technology
Colorectal cancer is a kind of common malignancy, all occupies malignant tumour spectrum prostatitis at China and its incidence of disease of western developed country and mortality ratio.2000 to 2004, in all malignant tumours, occupy the 3rd at the incidence of disease of U.S.'s colorectal cancer, and mortality ratio also is in the 3rd.In China, along with the change of growth in the living standard and eating habit, the incidence of disease of colorectal cancer also day by day increases, and has leapt to the 4th, and annual New Development colorectal cancer case reaches 400,000 examples, and ascending velocity wherein much is the 30-40 a middle-aged person in year near 5%.The 90's of last century were compared with the 70's, and the incidence of disease of China's colorectal cancer has risen 31.95% on the city, had risen 8.51% on the rural area, and mortality ratio occupies the 4th or the 5th of malignant tumour death notation.Can predict, the incidence and mortality of China's colorectal cancer is also growing steadily continuing in very long one period from now on, becomes one of malignant tumour that China is modal, incidence of disease rising is the fastest.
In recent years, develop rapidly along with science and technology, traditional treatment means such as operative treatment, chemotherapy, radiation therapy constantly develop, biological targeting treatment new drug is is constantly researched and developed, but 5 years survival rates of colorectal cancer still have only about 63.4%, and its improvement speed does not obviously catch up with the progress of treatment means.Judge the method for colorectal cancer prognosis at present, mainly still rely on traditional method by stages, but the many problems in the clinical position, does be can't explain fully with these traditional methods by stages: why the existence situation of same patients with colorectal cancer by stages often exist than big-difference? had why DISTANT METASTASES IN but taken place in the patient of I phase afterwards during first visit? therefore, more extremely important is exactly to search out more colorectal cancer prognosis correlating markings thing and model, prognosis of more accurate prediction patients with colorectal cancer and the susceptibility that difference is treated, seek the target spot of intervening blocking-up, to implement different therapeutic strategies, the final individualized treatment of realizing patient.
P53 is the most important tumor suppressor gene of finding up to now, is positioned human chromosome 17p13, contains 11 extrons and 10 intrones.Be divided into two kinds of wild type and saltants, wild type P53 gene is not only as the effect of tumor suppressor gene performance negative regulation, also participate in transcribing, dna damage and reparation, cell cycle are withered a plurality of processes such as control, Apoptosis, cell proliferation and cell differentiation, have the function of " molecular policeman ".Saltant P53 albumen is facile hydrolysis not, and the long half life period is arranged, and piles up in malignant cell, and saltant P53 albumen can make the effect forfeiture of its negative regulation cell growth with wild type P53 protein combination.The overexpression of saltant P53 albumen and P53 gene mutation are closely related.Previously study P53 with influence the patient by stages, MDR, relevant to situations such as the reaction of chemotherapy or radiotherapy and postoperative recurrence transfers.
SPARCL1 belongs to the interactional adhesion molecule of mediated cell matrix.SPARCL1 finds called after MAST9 in the researchs of people at non-small cell lung cancer such as Schraml in 1993.SPARCL1 albumen belongs to SPARC family, with SPARC(secreted protein acidic and rich in cysteine) sequence has 62% homology.The both has cysteine rich follistatin-like (FS) domain and extracellular calcium binding (EC) domain, but the N of SPARCL1 end far beyond SPARC for long, it also because of with this high homology structurally of the SPARC SPARC-like 1 that gains the name.The function of SPARCL1 is not clear and definite fully as yet at present.SPARCL1 is down-regulated expression in non-small cell lung cancer, metastatic prostate cancer, colorectal cancer, carcinoma of urinary bladder, pancreatic duct cancer, but in liver cancer up-regulated.But the expression of SPARCL1 in colorectal cancer reaches and the relation of the Clinical symptoms such as prognosis of colorectal cancer is not seen clearly report.
Bioinformatics is an emerging cross discipline of utilizing computing machine that the biological information in the life science is stored, retrieved and analyzes.Its basic research process is: utilize Computer Storage nucleic acid or protein information, the algorithm that studies science is worked out corresponding software its information is analyzed, compared and prediction, therefrom finds rule.(support vector machine is a kind of new sorting techniques that Vapnik etc. 1995 proposes SVM) to support vector machine, now has been widely used in a plurality of fields such as face recognition, genomics.
Summary of the invention
The method for building up that the purpose of this invention is to provide a kind of colorectal cancer prognosis prediction model.It promptly is the method that a kind of use in conjunction by SABC detection, mark combination and support vector machine analysis is set up the mark built-up pattern.This method realizes by following steps:
1, the SABC method detects SPARCL1, the expression of P53 albumen in Colorectal Carcinoma: paraffin embedded tissues specimen collection → section → baking box spends the night → dewaxes → aquation → antigen retrieval → natural cooling → develop a film → dry, putting in the wet box → drip A reagent (Endogenous Peroxidase Blocking Solution endogenous peroxidase blocking agent) leaves standstill → develop a film → drips B reagent (the non-immune serum of Blocking Solution Non-Immune Serum) and leave standstill → drip one and resist, leave standstill → develop a film → drip C reagent (Biotinylated Second Antibody Goat anti Mouse IgG biotin labeled two is anti-), leave standstill → develop a film → drip D reagent (Enzyme Conjugate HRP-Streptavidin streptomycete avidin-peroxidase), leave standstill → develop a film → DAB colour developing → observation colour developing back drip washing → nucleus dyeing → drip washing → differentiation → tap water drip washing → return indigo plant → dewater → dry up → mounting;
2, semiquantitative method is with the horizontal classification of tissue expression of SPARCL1, P53 albumen: every routine sample is with 5 high power lens visuals field of machine testing, give the marking of positive scope and dye level respectively: positive scope is divided into 0-4 level: 0(＜10%), 1(10%-25%), 2(25%-50%), 3(50%-75%), 4(〉 75%); Dye level is divided into the 0-3 level: 0 is negative, and 1 is light yellow, and 2 is pale brown look, and 3 is dark-brown.With the addition of preceding two parts score, end product is divided into the 0-7 level at last;
3, SPARCL1, P53 protein expression level are through support vector machine combinatory analysis and checking, and set up discrimination model at last: support vector machine adopts radially basic kernel function (radial based kernel), and the Gamma value is made as 0.6, and point penalty function (C) is made as 19.Choosing of proper vector adopts statistics to filter combination model dependence method for screening.SPARCL1 and P53 are made up the input that is used for supporting vector machine model, prediction effect (existence/death in 3 years) with ten times of cross validation method assessment models, select the highest combination of the youden index (youden index=[(sensitivity+specificity)/2+ sensitivity]/2) of setting up supporting vector machine model prediction as final model, through the predicted value of ten times of cross validations as final result.
Another object of the present invention provides the application of described model in patients with colorectal cancer postoperative metastasis risk of relapse degree prognostic experiment.
The present invention makes up thing structure model as a token of with SPARCL1 and P53, is used for patients with colorectal cancer postoperative metastasis risk of relapse degree prognostic experiment.Binding immunoassay group of the present invention detection, mark combination and support vector machine analysis, use in conjunction is in setting up the colorectal cancer forecast model.Through research, SABC detects SPARCL1 and the P53 expression in PATIENTS WITH LARGE BOWEL is performed the operation tumor resection tissue, make up the two expression values first by bioinformatics, form the SPARCL1/P53 built-up pattern, and confirm to find that this prognosis model has the effect of the auxiliary forecast colorectal cancer patient prognosis of experiment.Find first thus, set up the method for mark built-up pattern by the use in conjunction of SABC detection, mark combination and support vector machine analysis.
Description of drawings
Fig. 1 has shown the different survivorship curves of differentiating patient as a result (all by stages) of SPARCL1/P53 prognosis model.
Fig. 2 has shown II phase and III phase patient's survivorship curve.
Fig. 3 has shown the different differentiations of SPARCL1/P53 prognosis model patient's's (II+III phase) survivorship curve as a result.
Fig. 4 has shown the different differentiations of SPARCL1/P53 prognosis model patient's's (II phase) survivorship curve as a result.
Fig. 5 has shown the different differentiations of SPARCL1/P53 prognosis model patient's's (III phase) survivorship curve as a result.
Embodiment
Below in conjunction with the drawings and specific embodiments, further set forth the present invention.Should be understood that these concrete enforcements only to be used to the present invention is described and be not used in and limit the scope of the invention.
Embodiment 1
Step 1:SPARCL1, the detection of P53 albumen in Colorectal Carcinoma
Take the SABC method, detect the expression that P53 and SPARCL1 in the Colorectal Carcinoma have been excised in patient's operation.
131 Colorectal Carcinoma wax stones choosing come from the PATIENTS WITH LARGE BOWEL excision tissue that 1999 to 2004 2nd Affiliated Hospital Zhejiang University School of Medicine accept for medical treatment (I phases 23 example,
Phases 43 example,
Phases 56 example,
Phases 9 example), and diagnosis is all through verified by postoperative pathology, all patients' clinical and pathological data postoperative is all registered to follow up a case by regular visits to sheet form, and follows up a case by regular visits to situations such as its existence and relapse and metastasis more than 36 months by mail or phone every year, follows up a case by regular visits to once more before this research experiment begins and examines.
All paraffin embedded tissues samples, all paraffin embedded tissues that files from 2nd Affiliated Hospital Zhejiang University School of Medicine pathology department.Be organized in take off in the operation after, place 4% formalin fixing immediately, fixing fully after, through draw materials, after the dehydration, transparent, waxdip, embedding, section HE dyeing pathology is made a definite diagnosis, then long preservation.
The SABC kit comprises the quick SABC MaxVision kit of instant (mouse/rabbit, step neoformation, catalog number:KIT-5010), the super quick kit (sheep of SP immunohistochemical, step neoformation, catalog number:KIT-9709) and DAB chromogenic reagent box (step neoformation, catalog number:DAB-0031).
Used antibody comprises: P53 antibody (mouse-anti people P53 monoclonal antibody, middle China fir Golden Bridge, catalog number:ZM-0408, instant), SPARCL1 antibody (Polyclonal goat anti-human SPARC-like 1 antibody (R﹠amp; D, catalog number:AF2728, dilutability 1:160).
The SABC process: paraffin embedded tissues is cut into the thick section of about 4 μ m, exhibition sheet and paster on the pretreated microslide of poly-D-lysine → 59 ℃ of baking boxs spend the night → the dimethylbenzene dewaxing is (in 20min * 3 → gradient alcohol aquation (no watery wine 5min * 3 → 95% alcohol 5min → 75% alcohol 5min) → distilled water among aquation → 1 * EDTA, 98 ℃ of heating 15 minutes (antigen retrieval) → naturally cool in room temperature → distilled water develop a film → and TBS solution (PH 7.4) develops a film (5min) → dries, place in the wet box → drip A reagent (endogenous peroxidase blocking agent), leave standstill 10min → TBS 3min * 3 time → drip B reagent (non-immune serum) of developing a film and leave standstill the unnecessary liquid of 15min → blot, dripping one resists, leave standstill 2 hours → TBS 5min * 3 times → dropping C reagent (biotin labeled two is anti-), leave standstill 15min → TBS 5min * 2 time → drip D reagent streptomycete avidin-peroxidase), leave standstill 15min → TBS 3min * 3 time → the drip fresh DAB developer for preparing → about 1min, observing colour developing is placed on drip washing in the tap water → haematoxylin and soaks to soak in 10min → tap water drip washing → hydrochloride alcohol differentiation 1s → tap water drip washing → 56 ℃ hot water and return indigo plant → gradient alcohol dehydration (75% alcohol 5min → 95% alcohol 5min → anhydrous alcohol 5min * 3) → dry up the neutral resins mounting.
Step 2:SPARCL1, the classification of P53 protein expression level
Present embodiment carries out classification to the result of step 1.
Every routine sample adopts semiquantitative method with 5 high power lens visuals field of machine testing (* 400 times), takes all factors into consideration the scope and the dye level of positive cell.Positive scope is divided into 0-4 level: 0(＜10%), 1(10%-25%), 2(25%-50%), 3(50%-75%), 4(〉 75%); Dye level is divided into the 0-3 level: 0 is negative, and 1 is light yellow, and 2 is pale brown look, and 3 is dark-brown.With the addition of preceding two parts score, end product is divided into the 0-7 level.
Step 3:SPARCL1, P53 protein expression level are combined to form model through bioinformatics
This experimental data adopts the ZUCI-ProteinChip Data Analyze System software package of the triumphant design of the surplus victory in Zhejiang University institute of oncology to analyze.Set up discrimination model with support vector machine method, differentiate the method for effect with ten times of cross validation methods as assessment models.Support vector machine adopts radially basic kernel function (radial based kernel), and the Gamma value is made as 0.6, and point penalty function (C) is made as 19.Choosing of proper vector adopts statistics to filter combination model dependence method for screening.SPARCL1 and P53 are made up the input that is used for supporting vector machine model, prediction effect (existence/death in 3 years) with ten times of cross validation method assessment models, select the highest combination of the youden index (youden index=[(sensitivity+specificity)/2+ sensitivity]/2) of setting up supporting vector machine model prediction as final model, through the predicted value of ten times of cross validations as final result.According to the SPARCL1/P53 forecast model, patient can be divided into two groups, be respectively good prognosis(predicted value=0) and bad prognosis(predicted value=1).
Embodiment 2
1.SPARCL1/P53 model is to the prognosis judgement effect of patients with colorectal cancer (all by stages)
Fig. 1 has shown the different survivorship curves of differentiating patient as a result (all by stages) of SPARCL1/P53 prognosis model.The checking of Kaplan-Meier method shows, the SPARCL1/P53 forecast model can be divided into 131 routine patients great two groups of life span difference (P＜0.001), be respectively good prognosis(predicted value=0) and bad prognosis(predicted value=1), wherein good prognosis group patient estimated median survival time 91.676 months, and bad prognosis group patient estimated median survival time 41.928 months.
.SPARCL1/P53 model is to the prediction effect of patients with colorectal cancer postoperative recurrence transfer case
In these patients, it is 22.34% that good prognosis group (predicted value=0) postoperative recurrence shifts incidence, and bad prognosis group (predicted value=1) postoperative recurrence transfer incidence is 64.86%, and both compare P＜0.001.
The prediction effect that the prognosis model shifts postoperative recurrence
| Relapse and metastasis is arranged | No relapse and metastasis | |
| Prediction(0) | 73 | 21 |
| Prediction(1) | 13 | 24 |
Prediction: each mark built-up pattern is to the predicted value of prognosis
(0=good?prognosis，1=bad?prognosis)。
.SPARCL1/P53 model is to the prognosis judgement effect of II phase and III phase patients with colorectal cancer
Choose in total 131 colorectal cancer cases II phase and III phase totally 99 examples, observe the prediction effect of SPARCL1/P53 model by stages II phase and III phase patients with colorectal cancer existence situation in conjunction with tradition relatively.
Fig. 2 has shown II phase and III phase patient's survivorship curve.The Kaplan-Meier survival analysis shows that II phase and III phase, patient estimated that median survival time difference is little, was respectively 80.566 months VS 63.038 months (P=0.039).
Fig. 3 has shown the different differentiations of SPARCL1/ P53 prognosis model patient's's (II+III phase) survivorship curve as a result.The SPARCL1/P53 model is divided into two groups (P＜0.001) that life span differs greatly with 99 routine II and III phase patient, be respectively good prognosis(predicted value=0) and bad prognosis(predicted value=1), wherein good prognosis group patient estimates that median survival time is 81.658 months, and bad prognosis group patient estimates that median survival time is 43.889 months.
Fig. 4 has shown the different differentiations of SPARCL1/P53 prognosis model patient's's (II phase) survivorship curve as a result.In 43 routine II phase patients with colorectal cancer, the SPARCL1/P53 model also can be divided into patient two groups (P＜0.001) that life span differs greatly, be respectively good prognosis(predicted value=0) and bad prognosis(predicted value=1), wherein good prognosis group patient estimates that median survival time is 88.65 months, and bad prognosis group patient estimates that median survival time is 50.509 months.
Fig. 5 has shown the different differentiations of SPARCL1/P53 prognosis model patient's's (III phase) survivorship curve as a result.In 56 routine III phase patients with colorectal cancer, SPARCL1/P53 prognosis model also can be divided into patient two groups (P＜0.001) that life span differs greatly, be respectively good prognosis(predicted value=0) and bad prognosis(predicted value=1), wherein good prognosis group patient estimates that median survival time is 75.74 months, and bad prognosis group patient estimates that median survival time is 36.167 months.
Claims (2)
1. the method for building up of a colorectal cancer prognosis prediction model is characterized in that, realizes by following steps:
(1) the SABC method detects SPARCL1, the expression of P53 albumen in Colorectal Carcinoma: collection organization's wax stone sample → section → baking box spends the night → dewaxes → aquation → antigen retrieval → natural cooling → develop a film → dry, put in the wet box → drip endogenous peroxidase blocking agent, leave standstill → develop a film → drip non-immune serum, leaving standstill → drip one resists, leaving standstill → develop a film → drip biotin labeled two resists, leave standstill → develop a film → drip streptomycete avidin-peroxidase, leave standstill → develop a film → DAB colour developing → observation colour developing back drip washing → nucleus dyeing → drip washing → differentiation → tap water drip washing → return indigo plant → dewater → dry up → mounting;
(2) semiquantitative method is with the horizontal classification of tissue expression of SPARCL1, P53 albumen: every routine sample is with 5 high power lens visuals field of machine testing, give the marking of positive scope and dye level respectively: positive scope is divided into the 0-4 level:＜10% is 0 grade, 10%-25% is 1 grade, 25%-50% is 2 grades, 50%-75% is 3 grades,〉75%4 grades; Dye level is divided into the 0-3 level: 0 is negative, and 1 is light yellow, and 2 is pale brown look, and 3 is dark-brown; With the addition of preceding two parts score, end product is divided into the 0-7 level at last;
(3) SPARCL1, the P53 protein expression level is through support vector machine combinatory analysis and checking, set up discrimination model: support vector machine adopts radially basic kernel function, the Gamma value is made as 0.6, point penalty function (C) is made as 19, choosing of proper vector adopts statistics to filter combination model dependence method for screening, SPARCL1 and P53 are made up the input that is used for supporting vector machine model, prediction effect with ten times of cross validation method assessment models, existence/death in 3 years, select the youden index of setting up the supporting vector machine model prediction: youden index=[(sensitivity+specificity)/2+ sensitivity]/2), the highest combination is as final model, through the predicted value of ten times of cross validations as final result.
2. the application of model in patients with colorectal cancer postoperative metastasis risk of relapse degree prognostic experiment of setting up according to the described method of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100694625A CN102183662A (en) | 2011-03-22 | 2011-03-22 | Method for establishing colon cancer prognosis prediction model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100694625A CN102183662A (en) | 2011-03-22 | 2011-03-22 | Method for establishing colon cancer prognosis prediction model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102183662A true CN102183662A (en) | 2011-09-14 |
Family
ID=44569872
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100694625A Pending CN102183662A (en) | 2011-03-22 | 2011-03-22 | Method for establishing colon cancer prognosis prediction model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102183662A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102621324A (en) * | 2012-04-05 | 2012-08-01 | 武汉康迈生物技术有限公司 | Kit for immunohistochemical staining detection of colorectal cancer and application |
| CN109317083A (en) * | 2018-11-05 | 2019-02-12 | 暨南大学 | Nanometer selenium is preparing the application in DNA immunization adsorbent |
| CN109521193A (en) * | 2018-11-05 | 2019-03-26 | 暨南大学 | DNA immunization adsorbent is preparing the application in Anti-hCG action detection reagent |
| CN112216386A (en) * | 2019-07-11 | 2021-01-12 | 沈阳美鳌生物科技有限公司 | Method for predicting the risk of occult liver metastasis in colorectal cancer patients |
| CN113571194A (en) * | 2021-07-09 | 2021-10-29 | 清华大学 | Modeling method and device for hepatocellular carcinoma long-term prognosis prediction |
| CN117219158A (en) * | 2022-12-02 | 2023-12-12 | 上海爱谱蒂康生物科技有限公司 | Individualized treatment decision-making method and system for intestinal cancer and storage medium containing individualized treatment decision-making method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101389957A (en) * | 2005-12-23 | 2009-03-18 | 环太平洋生物技术有限公司 | Prognosis prediction for colorectal cancer |
| WO2009125303A2 (en) * | 2008-04-10 | 2009-10-15 | Genenews Corporation | Method and apparatus for determining a probability of colorectal cancer in a subject |
| CN101896817A (en) * | 2007-12-10 | 2010-11-24 | 霍夫曼-拉罗奇有限公司 | Marker panel for colorectal cancer |
| CN101971033A (en) * | 2007-06-04 | 2011-02-09 | 戴诺普雷克斯公司 | Biomarker combinations for colorectal cancer |
-
2011
- 2011-03-22 CN CN2011100694625A patent/CN102183662A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101389957A (en) * | 2005-12-23 | 2009-03-18 | 环太平洋生物技术有限公司 | Prognosis prediction for colorectal cancer |
| CN101971033A (en) * | 2007-06-04 | 2011-02-09 | 戴诺普雷克斯公司 | Biomarker combinations for colorectal cancer |
| CN101896817A (en) * | 2007-12-10 | 2010-11-24 | 霍夫曼-拉罗奇有限公司 | Marker panel for colorectal cancer |
| WO2009125303A2 (en) * | 2008-04-10 | 2009-10-15 | Genenews Corporation | Method and apparatus for determining a probability of colorectal cancer in a subject |
Non-Patent Citations (3)
| Title |
|---|
| 孟潘庆，等: "组织芯片技术与人工智能神经网络在大肠癌诊断中的应用", 《中华肿瘤防治杂志》, vol. 14, no. 17, 30 September 2007 (2007-09-30), pages 1324 - 1327 * |
| 徐文鸿，等: "基于支持向量机的大肠黏液腺癌术前血清蛋白质标志物检测及分析", 《细胞生物学杂志》, no. 6, 15 December 2008 (2008-12-15), pages 819 - 822 * |
| 虞舒静: "基于通路的大肠癌预后标志物寻找及SPARCL1抑制转移的研究", 《中国博士学位论文全文数据库 医药卫生科技辑》, no. 10, 15 October 2009 (2009-10-15) * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102621324A (en) * | 2012-04-05 | 2012-08-01 | 武汉康迈生物技术有限公司 | Kit for immunohistochemical staining detection of colorectal cancer and application |
| CN109317083A (en) * | 2018-11-05 | 2019-02-12 | 暨南大学 | Nanometer selenium is preparing the application in DNA immunization adsorbent |
| CN109521193A (en) * | 2018-11-05 | 2019-03-26 | 暨南大学 | DNA immunization adsorbent is preparing the application in Anti-hCG action detection reagent |
| CN112216386A (en) * | 2019-07-11 | 2021-01-12 | 沈阳美鳌生物科技有限公司 | Method for predicting the risk of occult liver metastasis in colorectal cancer patients |
| CN113571194A (en) * | 2021-07-09 | 2021-10-29 | 清华大学 | Modeling method and device for hepatocellular carcinoma long-term prognosis prediction |
| CN113571194B (en) * | 2021-07-09 | 2022-05-13 | 清华大学 | Modeling method and device for hepatocellular carcinoma long-term prognosis prediction |
| CN117219158A (en) * | 2022-12-02 | 2023-12-12 | 上海爱谱蒂康生物科技有限公司 | Individualized treatment decision-making method and system for intestinal cancer and storage medium containing individualized treatment decision-making method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lopez-Beltran et al. | Prognostic factors in stage T1 grade 3 bladder cancer survival: the role of G1–S modulators (p53, p21Waf1, p27Kip1, Cyclin D1, and Cyclin D3) and proliferation index (ki67-MIB1) | |
| Won et al. | A survey of immunohistochemical biomarkers for basal-like breast cancer against a gene expression profile gold standard | |
| Du et al. | Sox2 nuclear expression is closely associated with poor prognosis in patients with histologically node-negative oral tongue squamous cell carcinoma | |
| Stearman et al. | Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model | |
| Alford et al. | Increased risk for distant metastasis in patients with familial early-stage breast cancer and high EZH2 expression | |
| Koopmann et al. | Mac‐2‐binding protein is a diagnostic marker for biliary tract carcinoma | |
| Zhou et al. | CRM1 is a novel independent prognostic factor for the poor prognosis of gastric carcinomas | |
| Bell et al. | In‐depth characterization of the salivary adenoid cystic carcinoma transcriptome with emphasis on dominant cell type | |
| CN102183662A (en) | Method for establishing colon cancer prognosis prediction model | |
| Mayinuer et al. | Upregulation of protein tyrosine phosphatase type IVA member 3 (PTP4A3/PRL-3) is associated with tumor differentiation and a poor prognosis in human hepatocellular carcinoma | |
| Zhao et al. | Overexpression of T lymphoma invasion and metastasis 1 predict renal cell carcinoma metastasis and overall patient survival | |
| Hessman et al. | Loss of expression of the cancer stem cell marker aldehyde dehydrogenase 1 correlates with advanced-stage colorectal cancer | |
| Ding et al. | CLIC1 overexpression is associated with poor prognosis in gallbladder cancer | |
| Hashmi et al. | Molecular subtypes of breast cancer in South Asian population by immunohistochemical profile and Her2neu gene amplification by FISH technique: association with other clinicopathologic parameters | |
| Morris et al. | Gene expression profiling in breast cancer | |
| Song et al. | CCR7 and VEGF-C: molecular indicator of lymphatic metastatic recurrence in pN0 esophageal squamous cell carcinoma after Ivor-Lewis esophagectomy? | |
| Yu et al. | Proteomics analysis identified TPI1 as a novel biomarker for predicting recurrence of intrahepatic cholangiocarcinoma | |
| Roh et al. | Phosphorylated 4E-binding protein 1 expression is associated with poor prognosis in small-cell lung cancer | |
| Qiao et al. | Clinical significance of topoisomerase 2A expression and gene change in operable invasive breast cancer | |
| Wu et al. | Low expression of keratin17 is related to poor prognosis in bladder cancer | |
| Liang et al. | Expression of FSCN1 and FOXM1 are associated with poor prognosis of adrenocortical carcinoma patients | |
| Zhong et al. | TNFAIP8 promotes the migration of clear cell renal cell carcinoma by regulating the EMT | |
| Lei et al. | Negative correlation of cytoplasm TIMP3 with miR-222 indicates a good prognosis for NSCLC | |
| Na et al. | High expression of HMGA2 predicts poor survival in patients with clear cell renal cell carcinoma | |
| Zhao et al. | High expression of Y-box-binding protein 1 correlates with poor prognosis and early recurrence in patients with small invasive lung adenocarcinoma |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20110914 |