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.
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Data availability
The data that support the findings of this study are openly available on TCGA website as well as in the supplementary files.
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Conceptualization, AL, RK, and GPSR; methodology, AL, RK, and GPSR; formal analysis, AL, RK, and GPSR.; investigation, AL, RK, and GPSR; code development, AL; visualization and figures, AL, RK, and GPSR; interpretation of data and results, AL, RK, CA and GPSR; supervision, GPSR; project administration, GPSR; funding acquisition, GPSR; writing and editing, AL, RK, CA, and GPSR. All authors have read and agreed to the published version of the manuscript.
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Lathwal, A., Kumar, R., Arora, C. et al. Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data. J Cancer Res Clin Oncol 146, 2743–2752 (2020). https://doi.org/10.1007/s00432-020-03318-3
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DOI: https://doi.org/10.1007/s00432-020-03318-3