PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 5, 2026Cancer Research0 citations

Abstract 5434: Immune cell infiltration analysis of lung cancer based on proteomics.

View Full Paper
RZRuoxian ZhangLLL B Lyu

Key Points

  • The aim is to quantify tumor-infiltrating immune cells in lung cancer by developing a novel proteomics-based algorithm.
  • Developed a deconvolution-based algorithm for proteomics data
  • Used support vector regression with an immune signature matrix
  • Applied the algorithm to analyze lung cancer proteomics datasets
  • Performed NMF-based subtyping to identify immune-associated subtypes
  • Identified high infiltration levels of MO non-classical cells, NK cells, and T4-EMRA cells
  • Cluster 1 was linked to poorer prognosis and enriched in specific signaling pathways
  • NK cells and certain CD4+ T-cell subsets were more abundant in Cluster 1 than Cluster 2

Abstract

Abstract Lung cancer remains a leading cause of cancer-related mortality, accounting for nearly one-quarter of all cancer deaths. Its initiation, progression, and metastasis are tightly influenced by the tumor microenvironment, within which diverse immune cell populations play critical roles. Quantifying tumor-infiltrating immune cells is therefore essential for understanding lung cancer biology and improving therapeutic strategies. While numerous computational methods have been developed for immune infiltration analysis based on transcriptomic data, algorithms optimized for proteomics data are largely unavailable, and the applicability of existing transcriptome-based tools to proteomic datasets remains unclear.In this study, we developed a deconvolution-based algorithm tailored for proteomics data to quantify tumor-infiltrating immune cells in lung cancer. Using a support vector regression model constructed from an immune signature matrix, we decomposed tumor tissue proteomics profiles into proportions of distinct immune cell types. Application of this algorithm to lung cancer proteomics datasets revealed that MO non-classical cells, NK cells, and T4-EMRA cells exhibited the highest infiltration levels. Integrating proteomics and clinical data, we further performed NMF-based subtyping and identified two immune-associated subtypes. Cluster 1, characterized by poorer prognosis, showed significant enrichment in complement cascade signaling, IGF transport and uptake regulation, and post-translational protein phosphorylation pathways. Additionally, NK cells and several CD4+ T-cell subsets were more abundant in Cluster 1 than in Cluster 2, indicating stronger immune infiltration and elevated immune activity, potentially contributing to adverse clinical outcomes.These findings provide a proteomics-based framework for immune infiltration analysis and offer new insights into lung cancer molecular subtyping and precision oncology. Citation Format: Ruoxian Zhang, Liangcheng Lyu. Immune cell infiltration analysis of lung cancer based on proteomics abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5434.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69d1fdf7a79560c99a0a469ehttps://doi.org/10.1158/1538-7445.am2026-5434
Ask AI
Helpful
Bookmark
Share
View Full Paper