Frontiers in genetics · 2026
Machine learning prioritization identifies PANX1 as an inflammation-associated candidate regulator in lung adenocarcinoma.
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Yang Ya, Fan Tingyuan, Miao Xiaye, Ren Chuanli
Аннотация
BACKGROUND: Tumor-associated inflammation is an important contributor to cancer progression and therapeutic resistance. However, the molecular regulators linking inflammatory signaling with tumor biology in lung adenocarcinoma (LUAD) remain incompletely defined.
METHODS: We implemented an integrative multi-cohort and machine learning framework to identify inflammation-associated candidate regulators in LUAD. Immune-inflammatory genes derived from hallmark pathways were intersected with differentially expressed and prognosis-associated genes in TCGA-LUAD. Transcriptomic and clinical data from TCGA and 13 independent GEO cohorts were integrated for model development and validation. Multi-algorithm prioritization was applied to derive a consensus prognostic signature and nominate core candidates. Multi-omics analyses were conducted to characterize genomic alterations and immune associations. Functional relevance was examined using loss-of-function experiments in lung cancer cell lines.
RESULTS: We identified 52 inflammation-associated candidate genes and derived a 10-gene core signature with moderate prognostic performance across independent cohorts, while cohort and endpoint heterogeneity remained evident. Among these genes, PANX1 emerged as a focused candidate for further characterization based on an integrative assessment of model contribution, recurrence across analyses, and biological plausibility. Multi-omics analyses linked PANX1 expression to copy-number alterations, mutational context, and inferred immune microenvironment features. PANX1 was significantly upregulated in lung cancer cells, and its knockdown inhibited cell proliferation, reduced extracellular ATP release, and suppressed pro-inflammatory cytokine expression, including IL6, TNFA, and IL1B.
CONCLUSION: By integrating large-scale multi-cohort analysis, machine learning prioritization, and experimental validation, this study identifies PANX1 as a potential inflammation-associated regulator in LUAD. These findings support a previously underrecognized association between PANX1 and inflammatory tumor biology and suggest its potential value as a biomarker candidate, while further mechanistic and translational validation is required.
METHODS: We implemented an integrative multi-cohort and machine learning framework to identify inflammation-associated candidate regulators in LUAD. Immune-inflammatory genes derived from hallmark pathways were intersected with differentially expressed and prognosis-associated genes in TCGA-LUAD. Transcriptomic and clinical data from TCGA and 13 independent GEO cohorts were integrated for model development and validation. Multi-algorithm prioritization was applied to derive a consensus prognostic signature and nominate core candidates. Multi-omics analyses were conducted to characterize genomic alterations and immune associations. Functional relevance was examined using loss-of-function experiments in lung cancer cell lines.
RESULTS: We identified 52 inflammation-associated candidate genes and derived a 10-gene core signature with moderate prognostic performance across independent cohorts, while cohort and endpoint heterogeneity remained evident. Among these genes, PANX1 emerged as a focused candidate for further characterization based on an integrative assessment of model contribution, recurrence across analyses, and biological plausibility. Multi-omics analyses linked PANX1 expression to copy-number alterations, mutational context, and inferred immune microenvironment features. PANX1 was significantly upregulated in lung cancer cells, and its knockdown inhibited cell proliferation, reduced extracellular ATP release, and suppressed pro-inflammatory cytokine expression, including IL6, TNFA, and IL1B.
CONCLUSION: By integrating large-scale multi-cohort analysis, machine learning prioritization, and experimental validation, this study identifies PANX1 as a potential inflammation-associated regulator in LUAD. These findings support a previously underrecognized association between PANX1 and inflammatory tumor biology and suggest its potential value as a biomarker candidate, while further mechanistic and translational validation is required.