Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Adiba Sultana; Md Shahin Alam; Xingyun Liu; Rohit Sharma; Rajeev K Singla; Rohit Gundamaraju; Bairong Shen

doi:10.1016/j.tranon.2022.101571

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Transl Oncol. 2023 Jan:27:101571. doi: 10.1016/j.tranon.2022.101571. Epub 2022 Nov 16.

Authors

Adiba Sultana¹, Md Shahin Alam², Xingyun Liu³, Rohit Sharma⁴, Rajeev K Singla⁵, Rohit Gundamaraju⁶, Bairong Shen⁷

Affiliations

¹ School of Biology and Basic Medical Sciences, Soochow University Medical College, 199 Ren'ai Road, Suzhou 215123, China; Center for Systems Biology, Soochow University, Suzhou 215006, China; Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China.
² School of Biology and Basic Medical Sciences, Soochow University Medical College, 199 Ren'ai Road, Suzhou 215123, China.
³ Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China.
⁴ Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India. Electronic address: rohitsharma@bhu.ac.in.
⁵ Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India. Electronic address: rajeevsingla26@gmail.com.
⁶ ER Stress and Mucosal Immunology Lab, School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, Tasmania, TAS 7248, Australia.
⁷ Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China. Electronic address: bairong.shen@scu.edu.cn.

Abstract

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Keywords: Diagnosis; Gene biomarkers; Networking analysis; Non-small cell lung cancer; Single-cell RNA-sequencing.