Decoding Prognostic Signatures in Brain Metastatic Non-Small-Cell Lung Cancer via Integrated Multi-Omics and Network Analysis.

Overview

1. Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
2. Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (T.S.); (H.O.A.A.)

Abstract

Non-small-cell lung cancer (NSCLC) constitutes approximately all lung cancers (LCs), and metastasis remains a major challenge in its treatment, thus necessitating the detection of novel molecular players involved in this process. In this study, we performed a comprehensive analysis of microarray and RNA-seq cohorts extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) and associated them with metastasis-related genes involved in brain metastasis (BM) in NSCLC. We thus identified differentially expressed metastatic genes (DEMGs) and constructed a protein–protein interaction network (PPIN) using these DEMGs. These DEMGs were further analyzed for associations with patient age, gender, and tumor stage, and the significant impact of specific genes on overall survival (OS) was assessed to determine the prognostic significance of the identified targets. We finally constructed a three-node microRNA (miRNA) feed-forward loop (FFL) involving miR-23b-3p, CD44, and five transcription factors (TFs) [EOMES, FOS, FOSL1, GLIS3, TP63] specific to NSCLC metastasis. Further mutational analysis of these FFL elements revealed that all were altered in the patient samples analyzed. Thus, our study identified potential genomic drivers that may play crucial roles in NSCLC BM. Overall, it provides valuable insights for the discovery of novel therapeutic targets in the management of NSCLC metastasis. However, further in vitro and in vivo experimentations are needed to justify the prognostic role of NSCLC biomarkers in BM pathogenesis.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

• geo:GSE161116, at NCBI GEO; found in “Data Availability Statement”

Other data links

• ncbi.nlm.nih.gov/geo, NCBI; found in the text, “4.1.2. Microarray Data Extraction”

Data Availability Statement

The raw HTSeq count datasets of TCGA-LUAD and LUSC used in our study were downloaded from UCSC Xena Browser available at https://xenabrowser.net/datapages/?dataset=TCGA-LUAD.htseq_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 (https://xenabrowser.net/datapages/?dataset=TCGA-LUAD.htseq_counts.tsv&host=https://gdc.xenahubs.net&removeHub=https://xena.treehouse.gi.ucsc.edu:443), accessed on 20 March 2025 and https://xenabrowser.net/datapages/?dataset=TCGA-LUSC.htseq_counts.tsv&host=https%3A%2F%2Fgdc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 (https://xenabrowser.net/datapages/?dataset=TCGA-LUSC.htseq_counts.tsv&host=https://gdc.xenahubs.net&removeHub=https://xena.treehouse.gi.ucsc.edu:443), accessed on 20 March 2025. Microarray dataset used in our study was downloaded from NCBI-GEO available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161116, accessed on 20 March 2025.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 14 MeSH terms, 1 funder, 56 references.

Cite

This paper

Singh, P., Dohare, R., Sarwar, T., Alharbi, H. O. A., & Rahmani, A. H. (2026). Decoding Prognostic Signatures in Brain Metastatic Non-Small-Cell Lung Cancer via Integrated Multi-Omics and Network Analysis. International journal of molecular sciences, 27(8), 3598. https://doi.org/10.3390/ijms27083598

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