Dataset Information

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.

ABSTRACT: Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein-protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

SUBMITTER: Erkin OC

PROVIDER: S-EPMC9003654 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.

Erkin Özgür Cem ÖC Cömertpay Betül B Göv Esra E

Bioinformatics and biology insights 20220406

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and ...[more]

PMID: 35422618

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Project description:BackgroundNon-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide. Despite advances in treatment, prognosis remains poor, necessitating the identification of reliable prognostic biomarkers. Costimulatory molecules (CMs) have shown to enhance antitumor immune responses. We aimed to explore their prognostic signals in NSCLC.MethodsThis study is a combination of bioinformatics analysis and laboratory validation. Gene expression profiles from The Cancer Genome Atlas (TCGA), GSE120622, and GSE131907 datasets were collected. NSCLC samples in TCGA were clustered based on CMs using consensus clustering. We used LASSO regression to identify CMs-related signatures and constructed nomogram and risk models. Differences in immune cells and checkpoint expressions between risk models were evaluated. Enrichment analysis was performed for differentially expressed CMs between NSCLC and controls. Key results were validated using qRT-PCR and flow cytometry.ResultsNSCLC samples in TCGA were divided into two clusters based on CMs, with cluster 1 showing poor overall survival. Ten CMs-related signatures were identified using LASSO regression. NSCLC samples in TCGA were stratified into high- and low-risk groups based on the median risk score of these signatures, revealing differences in survival probability, drug sensitivity, immune cell infiltration and checkpoints expression. The area under the ROC curve values (AUC) for EDA, ICOS, PDCD1LG2, and VTCN1 exceeded 0.7 in both datasets and considered as hub genes. Expression of these hub genes was significance in GSE131907 and validated by qRT-PCR. Macrophage M1 and T cell follicular helper showed high correlation with hub genes and were lower in NSCLC than controls detected by flow cytometry.ConclusionThe identified hub genes can serve as prognostic biomarkers for NSCLC, aiding in treatment decisions and highlighting potential targets for immunotherapy. This study provides new insights into the role of CMs in NSCLC prognosis and suggests future directions for clinical research and therapeutic strategies.

Dataset Information

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.

Publications

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer.

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