Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.MethodsA comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan–Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.ResultsWe identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.ConclusionThis study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

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Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Journal of Translational Medicine ◽

10.1186/s12967-019-02173-2 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 19

Author(s):

Lei Zhang ◽

Zhe Zhang ◽

Zhenglun Yu

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Single Gene ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Test Series ◽

Cox Regression Analysis ◽

Incidence And Mortality

Abstract Background Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD. Methods Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model. Results We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors. Conclusion Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

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A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Cancers ◽

10.3390/cancers13225837 ◽

2021 ◽

Vol 13 (22) ◽

pp. 5837

Author(s):

Changwu Wu ◽

Siming Gong ◽

Georg Osterhoff ◽

Nikolas Schopow

Keyword(s):

Soft Tissue ◽

Risk Score ◽

Cox Regression ◽

Soft Tissue Sarcomas ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Malignant Tumours ◽

Free Survival

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

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A 17-Gene Signature Predicted Prognosis in Renal Cell Carcinoma

Disease Markers ◽

10.1155/2020/8352809 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Fan Li ◽

Weifeng Hu ◽

Wei Zhang ◽

Guohao Li ◽

Yonglian Guo

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Malignant Tumors ◽

Prognostic Significance ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Incidence And Mortality ◽

Tcga Dataset

Renal cell carcinoma (RCC), which was one of the most common malignant tumors in urinary system, had gradually increased incidence and mortality in recent years. Although significant advances had been made in molecular and biology research on the pathogenesis of RCC, effective treatments and prognostic indicators were still lacking. In order to predict the prognosis of RCC better, we identified 17 genes that were associated with the overall survival (OS) of RCC patients from The Cancer Genome Atlas (TCGA) dataset and a 17-gene signature was developed. Through SurvExpress, we analyzed the expression differences of the 17 genes and their correlation with the survival of RCC patients in five datasets (ZHAO, TCGA, KIPAN, KIRC, and KIRP), and then evaluated the survival prognostic significance of the 17-gene signature for RCC. Our results showed that the 17-gene signature had a predictive prognostic value not only in single pathologic RCC, but also in multiple pathologic types of RCC. In conclusion, the 17-gene signature model was related to the survival of RCC patients and could help predict the prognosis with significant clinical implications.

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Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

10.21203/rs.3.rs-720991/v1 ◽

2021 ◽

Author(s):

Menglin He ◽

Cheng Hu ◽

Jian Deng ◽

Hui Ji ◽

Weiqian Tian

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Expression Levels ◽

Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

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A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.741112 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jun Wu ◽

Yuqing Lou ◽

Yi-Min Ma ◽

Jun Xu ◽

Tieliu Shi

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Early Stage ◽

Differential Expression Analysis ◽

The Cancer Genome Atlas ◽

Clinical Staging ◽

Risk Scores ◽

Regulatory Relationship ◽

Score Model

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e−16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e−4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

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Identification of a methylomics-associated nomogram for predicting overall survival of stage I–II lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-89429-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Heng Wang ◽

Chuangye Wei ◽

Peng Pan ◽

Fengfeng Yuan ◽

Jiancheng Cheng

Keyword(s):

Dna Methylation ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Stage I ◽

The Cancer Genome Atlas ◽

Validation Dataset ◽

Major Advance ◽

Operating Characteristics ◽

Tcga Dataset

AbstractThe aim of this paper was to identify DNA methylation based biomarkers for predicting overall survival (OS) of stage I–II lung adenocarcinoma (LUAD) patients. Methylation profile data of patients with stage I–II LUAD from The Cancer Genome Atlas (TCGA) database was used to determine methylation sites-based hallmark for stage I–II LUAD patients’ OS. The patients were separated into training and validation datasets by using median risk score as cutoff. Univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to develop a DNA methylation signature for OS of patients with stage I–II LUAD. As a result, an 11-DNA methylation signature was determined to be critically associated with the OS of patients with stage I–II LUAD. Analysis of receiver operating characteristics (ROC) suggested a high prognostic effectiveness of the 11-DNA methylation signature in patients with stage I–II LUAD (AUC at 1, 3, 5 years in training set were (0.849, 0.879, 0.831, respectively), validation set (0.742, 0.807, 0.904, respectively), entire TCGA dataset (0.747, 0.818, 0.870, respectively). Kaplan–Meier survival analyses exhibited that survival was significantly longer in the low-risk cohort compared to the high-risk cohort in the training dataset (P = 7e − 07), in the validation dataset (P = 1e − 08), and in the all-cohort dataset (P = 6e − 14). In addition, a nomogram was developed based on molecular factor (methylation risk score) as well as clinical factors (age and cancer status) (AUC at 1, 3, 5 years entire TCGA dataset were 0.770, 0.849, 0.979, respectively). The result verified that our methylomics-associated nomogram had a strong robustness for predicting stage I–II LUAD patients’ OS. Furthermore, the nomogram combined clinical and molecular factors to determine an individualized probability of recurrence for patients with stage I–II LUAD, which stood for a major advance in the field of personalized medicine for pulmonary oncology. Collectively, we successfully identified a DNA methylation biomarker and a DNA methylation-based nomogram to predict the OS of patients with stage I–II LUAD.

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Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer

Cancers ◽

10.3390/cancers13133343 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3343

Author(s):

Mingjun Zheng ◽

Junyu Long ◽

Anca Chelariu-Raicu ◽

Heather Mullikin ◽

Theresa Vilsmaier ◽

...

Keyword(s):

Ovarian Cancer ◽

Tumor Microenvironment ◽

Risk Score ◽

Malignant Tumors ◽

Biological Response ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Serous Ovarian Cancer ◽

Lasso Regression ◽

Cancer Genome Atlas

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.

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Development and validation of a novel ten-gene signature predicting prognosis in Hepatocellular carcinoma

10.21203/rs.3.rs-46933/v1 ◽

2020 ◽

Author(s):

Guanbao Zhou ◽

Genjie Lu ◽

Liang Yang ◽

Yangfang Lu

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Hierarchical Clustering ◽

Risk Score ◽

Cox Regression ◽

Gene Signature ◽

Molecular Biomarkers ◽

The Cancer Genome Atlas ◽

Expression Levels ◽

Gene Expression Levels

Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with relatively poor prognosis. Thus, we aimed to identify novel molecular biomarkers to effectively predict the prognosis of HCC patients and eventually guide treatment. Methods: Prognosis-associated genes were determined by Kaplan-Meier and multivariate Cox regression analyses using the expression and clinical data of 373 HCC patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. The classification of AML was performed by unsupervised hierarchical clustering of ten gene expression levels. A prognostic risk score was established based on a linear combination of ten gene expression levels using the regression coefficients derived from the multivariate Cox regression models. Results: A total of 183 genes were significantly associated with prognosis in HCC. SLC25A15, RAB8A, GOT2, SORBS2, IL18RAP were top five protective genes, while FHL3, AMD1, DCAF13, UBE2E1, PTDSS2 were top five risk genes in HCC. SLC25A15, GOT2, IL18RAP were significantly down-regulated and DCAF13, PTDSS2 and SORBS2 were significantly up-regulated in the HCC samples and these genes exhibited high accuracy in differentiating HCC tissues from normal liver tissues. Hierarchical clustering analysis of the ten genes discovered three clusters of HCC patients. HCC tumors of cluster1 and 2 were significantly associated with more favourable OS than those of cluster3, cluster2 tumors showed higher pathologic stage than cluster3 tumors. The risk score was predictive of increased mortality rate in HCC patients. Conclusions: The ten-gene signature and the risk score may turn out to be novel molecular biomarkers and stratification of HCC patients to considerably ameliorate the prognostic prediction.

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The Expression of RN7SL494P (7SL) Predicts Nodal Metastasis and Prognosis in Lung Adenocarcinoma

10.20944/preprints201903.0064.v1 ◽

2019 ◽

Author(s):

Xiao Zhu ◽

Yongmei Huang ◽

Hui Luo ◽

Ying Xu

Keyword(s):

Lung Adenocarcinoma ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Cancer Metastasis ◽

Nodal Metastasis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Rank Test ◽

Primary Cancer

The metastasis of lung cancer can spread to the lymph nodes around the lungs. Metastasis, rather than the primary cancer, judges patients survival. Wherefore, a more detailed study on transcriptome of metastatic lung adenocarcinoma including primary carcinoma was carried out. LUAD RNA-seq data and the corresponding clinical information were available from The Cancer Genome Atlas (TCGA), which included 522 cases but only 515 cases have transcriptome data. Differential expression analyses between cases and controls, between primary cancer and metastasis subgroup, or between TNM stages, were respectively carried out using edgeR package. Then, the Kruskal-Wallis tests were used to verify the gradient changes of cancer metastasis or staging with the differential expression genes. The survival analyses were calculated using the Kaplan-Meier algorithm and log-rank test. The functional predictions for the differentially expressed genes were porformed with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (GO/KEGG). Single gene set enrichment analysis (single GSEA) was run to explore the biological pathways associated with the expressions of RN7SL494P gene based on the Molecular Signatures Database (MSigDB). 406 and 439 differentially expressed genes were identified respectively in lymph node metastasis or TNM stages. 112/296 intersection genes were associated with nodal metastasis and/or staging, among them only 25 genes were associated with the nodal metastasis, 13 genes were associated with the staging with gradient changes. Only one gene (RN7SL494P) was found to be associated with prognosis. But RN7SL494P was not found joining any biological functions or processes or cellular components with GO/KEGG analyses. Finally, single GSEA enrichment and pathway analyses showed that RN7SL494P might be involving in cancer development process and poor outcome in lung adenocarcinoma. These findings highlight the potential applications of RN7SL494P as a promising molecular predictor not only in nodal metastasis but prognosis evalution in lung adenocarcinoma patients.

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Lipid metabolic gene-wide profile and survival signature of lung adenocarcinoma

10.21203/rs.3.rs-31951/v3 ◽

2020 ◽

Author(s):

Jinyou Li ◽

Qiang Li ◽

Zhenyu Su ◽

Qi Sun ◽

Yong Zhao ◽

...

Keyword(s):

Overall Survival ◽

Lipid Metabolism ◽

Lung Adenocarcinoma ◽

Interaction Network ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

High Morbidity ◽

Hub Genes ◽

Histologic Subtype ◽

Normal Tissues

Abstract Background: Lung cancer is the cancer with high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. The disorder of lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on diagnosis and prognostic biomarkers of LUAD. Methods: In this study, we performed an expression analysis of 1045 lipid metabolism-related genes between LUAD tumors and normal tissues from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differential expression genes (DEGs) was constructed to identify. The association between hub genes and overall survival was evaluated and formed a model to predict the prognosis of LUAD using a nomogram, and the model was validated by another cohort (GSE13213). Results: Finally, a total of 217 lipid metabolism-related DEGs were detected in LUAD. They were significantly enriched in Glycerophospholipid metabolism, fatty acid metabolic process, and Eicosanoid Signaling. Then we identified 6 hub genes through network and cytoHubba, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. The high expression of CYP2C9, UGT1A6, and INS, whereas low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival (OS) for 1925 LUAD patients. Our model found that the high-risk score group had a worse OS, and the validated cohort had the same result.Conclusion: This study constructed a signature of six lipid metabolic genes, which was significantly associated with the diagnosis and prognosis of LUAD patients. The gene signature can be used as a biomarker for LUAD in the term of lipid metabolic.

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