AccScience Publishing / TD / Online First / DOI: 10.36922/TD026130027
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ORIGINAL RESEARCH ARTICLE

Computational identification of immune-suppressive gene signatures associated with tumor progression and immunotherapy resistance

Kanayo Samuel Okonji1,2* ,  Harshavardhan Anandalakshmanan3
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1 Department of Chemistry, Faculty of Science, Federal University Oye-Ekiti, Ekiti State, Nigeria
2 Department of Health Sciences, University of the People, Pasadena, California, United States of America
3 Department of Biomedical Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
Tumor Discovery, 026130027 https://doi.org/10.36922/TD026130027
Received: 23 March 2026 | Revised: 21 April 2026 | Accepted: 13 May 2026 | Published online: 9 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The tumor microenvironment integrates immune‑activating and immune‑suppressive cues that critically shape cancer progression and response to immunotherapy. Here, we performed a pan‑cancer in silico analysis of transcriptome data from The Cancer Genome Atlas to define an immune‑suppressive gene signature and associated immune states linked to checkpoint activity and clinical outcome. Gene set-based scoring was used to quantify CD8+ T cells, regulatory T cells, macrophage subsets, and composite checkpoint and suppressive immune signatures across tumor types. Correlation and network analyses revealed that PDCD1, CTLA4, TIGIT, and LAG3 form a tightly coordinated checkpoint module strongly associated with Treg enrichment, higher suppressive immune signatures, and, notably, increased CD8+ T‑cell infiltration, indicating co‑occurring immune activation and suppression rather than mutually exclusive states. Unsupervised clustering of immune scores identified three recurrent immune subtypes, immune‑poor, intermediate, and immune‑enriched/suppressed, spanning multiple cancers and capturing distinct immunological configurations. The derived suppressive signature stratified patients into high- and low-risk groups, with elevated suppressive immune signature scores associated with poorer overall survival in Kaplan–Meier analyses. Multivariable Cox regression confirmed that the suppressive signature is an independent prognostic factor after adjusting for age, sex, and tumor stage, suggesting that it encodes biological information not captured by standard clinicopathologic variables. Together, these findings define a coordinated immune‑suppressive program that transcends tissue of origin and provides a tractable transcriptomic biomarker for risk stratification and rational design of combination immunotherapeutic strategies.

Graphical abstract
Keywords
Immune suppressive signature
Tumor microenvironment
Immune checkpoint
Pan cancer
Survival
Funding
None.
Conflict of interest
The authors declare that they have no competing financial or non-financial interests.
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Tumor Discovery, Electronic ISSN: 2810-9775 Print ISSN: 3060-8597, Published by AccScience Publishing
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