scholarly journals Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Rong Ma ◽  
Yanyun Zhao ◽  
Miao He ◽  
Hongliang Zhao ◽  
Yifan Zhang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Colon Adenocarcinoma ◽  
Functional Enrichment ◽  
Time Dependent ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background Increasing studies have suggested that aberrant expression of microRNAs might play essential roles in the progression of cancers. In this study, we sought to construct a high-specific and superior microRNAs signature to improve the survival prediction of colon adenocarcinoma (COAD) patients. Methods The genome-wide miRNAs, mRNA and lncRNA expression profiles and corresponding clinical information of COAD were collected from the TCGA database. Differential expression analysis, Kaplan–Meier curve and time-dependent ROC curve were calculated and performed using R software and GraphPad Prism7. Univariate and multivariate Cox analysis was performed to evaluate the prognostic ability of signature. Functional enrichment analysis was analyzed using STRING database. Results We identified ten prognosis-related microRNAs, including seven risky factors (hsa-miR-197, hsa-miR-32, hsa-miR-887, hsa-miR-3199-2, hsa-miR-4999, hsa-miR-561, hsa-miR-210) and three protective factors (hsa-miR-3917, hsa-miR-3189, hsa-miR-6854). The Kaplan–Meier survival analysis showed that the patients with high risk score had shorter overall survival (OS) in test series. And the similar results were observed in both validation and entire series. The time-dependent ROC curve suggested this signature have high accuracy of OS for COAD. The Multivariate Cox regression analysis and stratification analysis suggested that the ten-microRNA signature was an independent factor after being adjusted with other clinical characteristics. In addition, we also found microRNA signature have higher AUC than other signature. Furthermore, we identified some miRNA-target genes that affect lymphatic metastasis and invasion of COAD patients. Conclusion In this study, we established a ten-microRNA signature as a potentially reliable and independent biomarker for survival prediction of COAD patients.

scholarly journals Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

scholarly journals Upregulation of LIMK1 Is Correlated With Poor Prognosis and Immune Infiltrates in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guojun Lu ◽  
Ying Zhou ◽  
Chenxi Zhang ◽  
Yu Zhang
Keyword(s):  
Lung Adenocarcinoma ◽  
Mrna Expression ◽  
Roc Curve ◽  
Poor Prognosis ◽  
Expression Profiles ◽  
Immune Therapy ◽  
Functional Enrichment ◽  
Normal Tissues ◽  
Ppi Networks ◽  
Kaplan Meier

BackgroundProtein-coding gene LIM Domain Kinase 1 (LIMK1) is upregulated in various tumors and reported to promote tumor invasion and metastasis. However, the prognostic values of LIMK1 and correlation with immune infiltrates in lung adenocarcinoma are still not understood. Therefore, we evaluated the prognostic role of LIMK1 and its correlation with immune infiltrates in lung adenocarcinoma.MethodsTranscriptional expression profiles of LIMK1 between lung adenocarcinoma tissues and normal tissues were downloaded from the Cancer Genome Atlas (TCGA). The LIMK1 protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas. Receiver operating characteristic (ROC) curve was used to differentiate lung adenocarcinoma from adjacent normal tissues. Kaplan-Meier method was conducted to assess the effect of LIMK1 on survival. Protein-protein interaction (PPI) networks were constructed by the STRING. Functional enrichment analyses were performed using the “ClusterProfiler” package. The relationship between LIMK1 mRNA expression and immune infiltrates was determined by tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB).ResultsThe expression of LIMK1 in lung adenocarcinoma tissues was significantly upregulated than those in adjacent normal tissues. Increased LIMK1 mRNA expression was associated with lymph node metastases and high TNM stage. The ROC curve analysis showed that with a cutoff level of 4.908, the accuracy, sensitivity, and specificity for LIMK1 differentiate lung adenocarcinoma from adjacent controls were 69.5, 93.2, and 71.9%, respectively. Kaplan-Meier survival analysis showed lung adenocarcinoma patients with high- LIMK1 had a worse prognosis than those with low- LIMK1 (43.1 vs. 55.1 months, P = 0.028). Correlation analysis indicated LIMK1 mRNA expression was correlated with tumor purity and immune infiltrates.ConclusionUpregulated LIMK1 is significantly correlated with poor survival and immune infiltrates in lung adenocarcinoma. Our study suggests that LIMK1 can be used as a biomarker of poor prognosis and potential immune therapy target in lung adenocarcinoma.

scholarly journals COL5A3 is a prognostic biomarker and correlated with immune infiltrates in pancreatic cancer

2021 ◽  
Author(s):  
Yongjie Li ◽  
Min Zeng ◽  
Ting Wang ◽  
Feng Jiang
Keyword(s):  
Pancreatic Cancer ◽  
Roc Curve ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
High Expression ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Tumor Tissues

Abstract Background Pancreatic cancer is a malignant tumor of digestive system with high fatality rate, and its prognosis is very poor. Type Ⅴ collagen α3 (COL5A3) is highly expressed in a variety of tumor tissues, but its prognostic value and immune infiltration in pancreatic cancer are still unclear. Therefore, we evaluated the prognostic role of COL5A3 in pancreatic cancer and its correlation with immune infiltration. Methods The transcriptional expression profiles of COL5A3 in pancreatic cancer and normal tissues were downloaded from the Cancer Genome Atlas (TCGA). In the GEO (Gene expression omnibus) dataset (GSE16515), we compared the expression of COL5A3 in normal and tumor tissues. The expression of COL5A3 protein was evaluated by the human protein atlas (THPA). The effect of COL5A3 on survival was evaluated by Kaplan-Meier method. Receiver operating characteristic (ROC) curve was used to distinguish pancreatic cancer from paracancerous normal tissues. Protein-protein interaction (PPI) network was constructed by the STRING. Use the "ClusterProfiler" package for functional enrichment analysis. Tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB) were used to determine the relationship between COL5A3mRNA expression and immune infiltration. Results Compared with normal tissues, COL5A3 is highly expressed in pancreatic cancer tissues. The high expression of COL5A3 is related to the poor clinicopathological features and poor prognosis of pancreatic cancer. Kaplan-Meier survival analysis showed that the prognosis of pancreatic cancer patients with high expression of COL5A3 was worse than that of patients with low expression of COL5A3. ROC curve shows that COL5A3 has high sensitivity and specificity in the diagnosis of pancreatic cancer. Correlation analysis showed that the expression of COL5A3mRNA was related to immune cell infiltration. Conclusion This study reveals for the first time that COL5A3 may be a new prognostic biomarker related to immune infiltration and provide a new target for the diagnosis and treatment of pancreatic cancer.

scholarly journals Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Sun ◽  
Xin Zheng ◽  
Yingxin Sun ◽  
Ju Yu ◽  
Minfeng Sheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Roc Curve ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Low Grade ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Underlying Mechanisms ◽  
Genome Atlas

N6-methyladenosine (m6A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. It is involved in various types of cancer. However, its role in gliomas is not well known. This study aimed to determine the prognostic value of the m6A RNA methylation regulator in gliomas and investigate the underlying mechanisms of the aberrant expression of m6A-related genes.mRNA expression profiles and clinical information of 448 glioma samples were obtained from The Cancer Genome Atlas and cBioportal. The expression of m6A-related genes in normal controls and low-grade glioma and glioblastoma was obtained from Gene Expression Profiling Interactive Analysis. Further, m6A-related gene expression and its relationship with prognosis were obtained through The Chinese Glioma Genome Atlas (CGGA). Multivariate Cox regression analyses were performed, and a nomogram was built with potential risk factors based on a multivariate Cox analysis to predict survival probability. Online tools such as Gene Set Enrichment Analysis, STRING, Cytoscape, and Molecular Complex Detection were applied for bioinformatics analysis and to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. The multivariate Cox regression analysis found that higher expression levels of YTHDC2 and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3, also called IMP3) were independent negative and positive prognostic factors for overall survival (OS), respectively. Data from the CGGA database showed that IGF2BP3 expression increased when the tumor grade increased. Receiver operating characteristic (ROC) curve was used to evaluate the predictive specificity and sensitivity. The area under the ROC curve indicated that the OS prediction was 0.92 (1-year) and 0.917 (3-years), indicating that m6A-related genes could predict patient survival. In addition, IGF2BP3 was closely related to the shorter survival period of patients. Copy number variation and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of IGF2BP3 in gliomas. Significantly altered genes were identified, and the protein–protein interaction network was constructed. Based on the data presented, our study identified several m6A-related genes, especially IGF2BP3, that could be potential prognostic biomarkers of gliomas. The study unveiled the potential regulatory mechanism of IGF2BP3 in gliomas.

Identification of prognostic genes after platinum-based chemotherapy in ovarian cancer

2020 ◽  
Author(s):  
Jianyang Feng ◽  
Lijiang Xu ◽  
Yangping Chen ◽  
Weifeng Li ◽  
Yuyuan Zhu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Roc Curve ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Operative Therapy ◽  
Time Dependent ◽  
Rank Test ◽  
Survival Prediction ◽  
Pathway Enrichment ◽  
Platinum Based Chemotherapy

Abstract Background: Platinum-based chemotherapy plays a crucial role in pre- and post-operative therapy in advanced stage ovarian cancer (OC). The objective of this study was to explore differentially expression genes (DEGs) and their survival impact after exposing to platinum-based chemotherapy in OC patient via integrated bioinformatics analysis.Methods: Gene expression profiles of RNA-seq data in OC were extracted from the GEO and TCGA databases respectively. DEGs were sent to perform functional Gene Ontology and KEGG pathway enrichment analyses. Survival analysis was processed to identify significant prognostic genes. After overlapping between DEGs and prognostic genes, univariate and multivariate Cox proportion hazards models were utilized to estimate the hazard ratio of the potential genes with a 95% confidence interval. Finally, the Kaplan-Meier log rank test and the time-dependent receiver operating characteristic (ROC) curve were performed to evaluate the potential prognostic prediction in platinum-based chemotherapy OC patients.Results: A total of 484 up-regulated and 495 down-regulated DEGs were identified. Down-regulated DEGs remarkedly enriched in the cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation, homologous recombination in KEGG pathway enrichment analyses. After overlapping with survival genes in the TCGA cohort, 64 DEGs were demonstrated prognostic potential. Then 29 genes were further identified by univariate analyses. Multivariate analyses indicated eight of 29 genes (DHRS9, OVOS2, STAC2, TCF15, AADAC, LOC730183, LOC440910, ARHGDIG) demonstrated survival prediction potential in platinum-based chemotherapy OC patients. The area under the curve of the time-dependent ROC curve was 0.725 for 5-year survival prediction based on those eight genes.Conclusion: These prognostic genes identified in this study indicate some significance for prognosis prediction in platinum-based chemotherapy OC patients.

scholarly journals Construction of a Pseudogene‐associated ceRNA Network and Identification of Prognostic Pseudogene Biomarkers for Colorectal Cancer

2021 ◽  
Author(s):  
Zhuoqi Li ◽  
Jing Zhou ◽  
Liankun Gu ◽  
Baozhen Zhang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.

scholarly journals COL5A3 is a Prognostic Biomarker and Correlated with Immune Infiltrates in Pancreatic Cancer

2021 ◽  
Author(s):  
Yongjie Li ◽  
Min Zeng ◽  
Ting Wang ◽  
Feng Jiang
Keyword(s):  
Pancreatic Cancer ◽  
Roc Curve ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
High Expression ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Tumor Tissues

Abstract Pancreatic cancer is a malignant tumor of digestive system with high fatality rate, and its prognosis is very poor. Type Ⅴ collagen α3 (COL5A3) is highly expressed in a variety of tumor tissues, but its prognostic value and immune infiltration in pancreatic cancer are still unclear. Therefore, we evaluated the prognostic role of COL5A3 in pancreatic cancer and its correlation with immune infiltration. The transcriptional expression profiles of COL5A3 in pancreatic cancer and normal tissues were downloaded from the Cancer Genome Atlas (TCGA). In the GEO (Gene expression omnibus) dataset (GSE16515), we compared the expression of COL5A3 in normal and tumor tissues. The expression of COL5A3 protein was evaluated by the human protein atlas (THPA). The effect of COL5A3 on survival was evaluated by Kaplan-Meier method. Receiver operating characteristic (ROC) curve was used to distinguish pancreatic cancer from paracancerous normal tissues. Protein-protein interaction (PPI) network was constructed by the STRING. Use the "ClusterProfiler" package for functional enrichment analysis. Tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB) were used to determine the relationship between COL5A3 mRNA expression and immune infiltration. Compared with normal tissues, COL5A3 is highly expressed in pancreatic cancer tissues. The high expression of COL5A3 is related to the poor clinicopathological features and poor prognosis of pancreatic cancer. Kaplan-Meier survival analysis showed that the prognosis of pancreatic cancer patients with high expression of COL5A3 was worse than that of patients with low expression of COL5A3. ROC curve shows that COL5A3 has high sensitivity and specificity in the diagnosis of pancreatic cancer. Correlation analysis showed that the expression of COL5A3 mRNA was related to immune cell infiltration. This study reveals for the first time that COL5A3 may be a new prognostic biomarker related to immune infiltration and provide a new target for the diagnosis and treatment of pancreatic cancer.

scholarly journals Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianwei Lin ◽  
Zichao Cao ◽  
Dingye Yu ◽  
Wei Cai
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Differential Expression Analysis ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Cox Analysis

The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan–Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.

scholarly journals An Integrated mRNA-lncRNA Signature for Overall Survival Prediction in Cholangiocarcinoma

2020 ◽  
Author(s):  
Liping Zeng ◽  
Robert Mukiibi ◽  
Derong Xu ◽  
Hongbo Xin ◽  
Feng Zhang
Keyword(s):  
Roc Curve ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Functional Enrichment ◽  
P Value ◽  
Survival Prediction ◽  
Aberrant Expression ◽  
Specificity And Sensitivity ◽  
Incidence And Mortality ◽  
Prognostic Prediction

Abstract BackgroundThe incidence and mortality rate of cholangiocarcinoma (CCA) have been rising globally. Patients with CCA have extremely poor prognosis, partly due to the silent clinical character and hence diagnosed at advantage stage without effective treatments. There is growing evidence showing that aberrant expression of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) are involved in tumorigenesis and development of CCA. It is essential to establish an integrated mRNA-lncRNA signature to improve the ability of prognostic prediction in CCA patients.MethodsWe collected a training dataset of 45 patients from The Cancer Genome Atlas dataset and a validation cohort (GSE107943) of 57 patients from Gene Expression Omnibus. An integrated mRNA-lncRNA risk score was established by a univariate and a multivariate Cox regression analyses. Time-dependent receiver operating characteristic (ROC) analysis was used to evaluate prognostic performance. Moreover, we conducted a correlation analysis between the signature and different clinical characteristics, and preformed weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis to investigate functional roles of the integrated signature.ResultsA total of two mRNAs (CFHR3 and PIWIL4) and two lncRNAs (AC007285.1 and AC134682.1) were identified to construct the integrated signature through a univariate Cox regression (P-value = 1.35E-02) and a multivariable Cox analysis (P-value = 1.12E-02). The ROC curve suggested the integrated mRNA-lncRNA signature possessed a high specificity and sensitivity of prognostic prediction with an area under the curve (AUC) of 0.872 and 0.790 at 1-year and 3-years, respectively. Subsequently, the signature was validated in GSE107943 cohort and combined dataset, and an area under the ROC curve reached up to 0.750 and 0.819 at 1-year. The signature was not only independent from different clinical features (P-value= 1.12E-02), but also outperformed other clinical characteristics as prognostic biomarkers with AUC of 0.781 at 3 years. These molecules in the integrated signature may associated with metabolic-related biological process and lipid metabolism pathway, which was highly involved in CCA carcinogenesis. ConclusionThese results showed that the integrated mRNA-lncRNA signature had an independent prognostic value for risk stratification, and further facilitated personalized treatment for CCA patients.

scholarly journals A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nan Ma ◽  
Lu Si ◽  
Meiling Yang ◽  
Meihua Li ◽  
Zhiyi He
Keyword(s):  
High Risk ◽  
Roc Curve ◽  
Expression Profiles ◽  
Low Risk ◽  
Stage I ◽  
Risk Scores ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Nsclc Patients ◽  
Rna Signature

AbstractThere is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

Sign in / Sign up

Export Citation Format

Share Document