Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data
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ORIGINAL ARTICLE – CANCER RESEARCH
Identification of prognostic biomarkers for major subtypes of non‑small‑cell lung cancer using genomic and clinical data Anjali Lathwal1 · Rajesh Kumar2 · Chakit Arora1 · Gajendra Pal Singh Raghava1 Received: 11 March 2020 / Accepted: 8 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype. Methods In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients’ subtype-specific survival. Results Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively. Individually, 32 and 271 prognostic genes were found and validated in GSE study exclusively for LUSC and LUAD. Nearly, 9–10% of the validated genes in each subtype were already reported in multiple studies thus highlighting their importance as prognostic biomarkers. Strong literature evidence against these prognostic genes like “ELANE” (LUSC) and “AHSG” (LUAD) instigates further investigation for their therapeutic and diagnostic roles in the corresponding cohorts. Prognostic models built on five and four genes were validated for LUSC [HR = 2.10, p value = 1.86 × 10−5] and LUAD [HR = 2.70, p value = 3.31 × 10−7], respectively. The model based on the combination of age and tumor stage performed well in both NSCLC subtypes, suggesting that despite having distinctive histological features and treatment paradigms, some clinical features can be good prognostic predictors in both. Conclusion This study advocates that investigating the survival-specific biomarkers restricted to respective cohorts can advance subtype-specific prognosis, diagnosis, and treatment for NSCLC patients. Prognostic models and markers described for each subtype may provide insight into the heterogeneity of disease etiology and help in the development of new therapeutic approaches for the treatment of NSCLC patients. Keywords NSCLC · Survival analysis · Prognostic biomarker · Cox univariate regression · Subtype-specific
Introduction
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00432-020-03318-3) contains supplementary material, which is available to authorized users. * Gajendra Pal Singh Raghava [email protected] http://webs.iiitd.edu.in/raghava/ 1
Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, Phase III (Near Govind Puri Metro Station), A‑302 (R&D Block), New Delhi 110020, India
Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
2
Disruption in the signaling system that governs cell fate and development is the major initiation factor that contributes to tumorigenesis (Frost and Amos 2018). Lung cancer is the leadi
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