Dataset Information

Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.

ABSTRACT: Lung cancer is one of the most common malignant diseases and a major threat to public health on a global scale. Non-small-cell lung cancer (NSCLC) has a higher degree of malignancy and a lower 5-year survival rate compared with that of small-cell lung cancer. NSCLC may be mainly divided into two pathological subtypes, adenocarcinoma and squamous cell carcinoma. The aim of the present study was to identify disease genes based on the gene expression profile and the shortest path analysis of weighted functional protein association networks with the existing protein-protein interaction data from the Search Tool for the Retrieval of Interacting Genes. The gene expression profile (GSE10245) was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database, including 40 lung adenocarcinoma and 18 lung squamous cell carcinoma tissues. A total of 8 disease genes were identified using Naïve Bayesian Classifier based on the Maximum Relevance Minimum Redundancy feature selection method following preprocessing. An additional 21 candidate genes were selected using the shortest path analysis with Dijkstra's algorithm. The AURKA and SLC7A2 genes were selected three and two times in the shortest path analysis, respectively. All those genes participate in a number of important pathways, such as oocyte meiosis, cell cycle and cancer pathways with Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The present findings may provide novel insights into the pathogenesis of NSCLC and enable the development of novel therapeutic strategies. However, further investigation is required to confirm these findings.

SUBMITTER: Chen J

PROVIDER: S-EPMC5774457 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.

Chen Jiangpeng J Dong Xiaoqi X Lei Xun X Xia Yinyin Y Zeng Qing Q Que Ping P Wen Xiaoyan X Hu Shan S Peng Bin B

Molecular and clinical oncology 20171127 2

Lung cancer is one of the most common malignant diseases and a major threat to public health on a global scale. Non-small-cell lung cancer (NSCLC) has a higher degree of malignancy and a lower 5-year survival rate compared with that of small-cell lung cancer. NSCLC may be mainly divided into two pathological subtypes, adenocarcinoma and squamous cell carcinoma. The aim of the present study was to identify disease genes based on the gene expression profile and the shortest path analysis of weight ...[more]

PMID: 29435303

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Project description:The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of KCTD6, KBTBD7, LMO7, SPSB2, RNF19A, FOXA2, DHTKD1, CDH1 and PDHB and high expression level of KLHL25 were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.

Project description:BackgroundAlthough immune checkpoint inhibitors (ICIs) have improved survival for advanced wild-type non-small cell lung cancer (NSCLC), a lack of direct comparisons of various first-line treatments is clouding clinical decision-making. A network meta-analysis was conducted to compare current first-line treatments and identify the optimal regimen for patients with specific characteristics.MethodsPubMed, Embase, the Cochrane Central Register of Controlled Trials, Clinical Trials databases were searched from inception to 31 July 2020. Phase II/III randomized controlled trials (RCTs) comparing first-line treatments including chemotherapy, anti-angiogenesis, ICIs, and their combinations for previously untreated stage IIIB/IV or recurrent driver-gene wild-type NSCLC patients were included.ResultsTwenty-six RCTs were identified and included, involving 16,977 patients and a total of 18 regimens. ICI-containing treatments led to significantly prolonged overall survival (OS) compared with ICI-free treatments (0.82, 0.72-0.93). ICI plus chemotherapy had significantly longer progression-free survival (PFS; 0.70, 0.58-0.86) and marginally longer OS (0.90, 0.79-1.05) compared with ICIs alone. Ranking highest in the Bayesian network meta-analysis, pembrolizumab plus chemotherapy, nivolumab plus ipilimumab and chemotherapy, had significantly superior OS than standard chemotherapy with or without bevacizumab treatments. Pembrolizumab-chemotherapy ranked first for OS, 1-year OS rate, and subgroups of non-squamous, PD-L1 ⩾1%, non-smoking, and liver metastasis; while nivolumab-ipilimumab-chemotherapy for squamous, PD-L1 <1%, brain metastasis NSCLC. Furthermore, the ICI-containing bevacizumab-free treatments, such as pembrolizumab plus chemotherapy, nivolumab and ipilimumab with or without chemotherapy, were not significantly different from atezolizumab plus chemotherapy and bevacizumab in OS.ConclusionsA combination of ICIs with chemotherapy, rather than double ICIs, is the best first-line treatment for advanced wild-type NSCLC, with synergy that leads to better long-term survival. The panoramic view of the relative efficacy of any two regimens with different rankings provides strong evidence for selecting optimal first-line ICIs according to patients' clinical characteristics.

Dataset Information

Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.

Publications

Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.

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