Bioinformatic screening and experimental validation of programmed cell death–linked genes in lung adenocarcinoma

Abstract Background Lung cancer remains the primary cause of cancer-related mortality worldwide and is characterized by highly aggressive biological behavior. Programmed cell death (PCD) is a key regulator of tumor development and has been increasingly recognized as an important determinant of response to immunotherapy. Accordingly, investigating PCD-related mechanisms in lung adenocarcinoma (LUAD) may provide valuable information about disease progression and facilitate the identification of novel therapeutic targets. Methods To find differentially expressed genes linked to PCD in LUAD, data from the Gene Expression Omnibus (GEO) (GSE117570, GSE140797, GSE118370) and TCGA (TCGA-LUAD) were examined. Machine learning techniques were employed to find hub PCD differentially expressed genes with significantly linked LUAD features (hub LUAD-PCDDEGs). To evaluate the diagnostic capacity of hub LUAD-PCDDEGs, we developed diagnostic nomograms and receiver operating characteristic curves. Moreover, immune cell infiltration analysis and gene set enrichment analysis were employed to identify underlying molecular pathways. Western blot and qRT-PCR were utilized to verify the results. Results In both normal and LUAD samples, we found 264 PCDDEGs. We identified four hub genes, GGCT, HYAL2, BEX1, and FABP4, as candidate diagnostic biomarkers for LUAD using LASSO, random forest (RF), and support vector machine–recursive feature elimination studies. GGCT was significantly upregulated in LUAD tissues, while HYAL2, BEX1, and FABP4 were significantly down-regulated. These expression patterns were validated by qRT-PCR analysis. The upregulation of GGCT was further confirmed at the protein level by Western blotting and supported by immunohistochemistry (IHC). Additionally, a substantial increase in infiltration of 10 immune cell types in LUAD was observed, and this infiltration was strongly correlated with hub LUAD-PCDDEGs. These cell types included plasma cells, memory B cells, naive B cells, CD4 + memory-activated T cells, follicular helper T cells, gamma delta T cells, regulatory T cells, resting dendritic cells, M1 macrophages, and eosinophils. Conclusion In the context of LUAD, this study successfully identified four genes (GGCT, HYAL2, BEX1, and FABP4) linked to PCD. These findings suggest their potential as candidate biomarkers for LUAD; however, their clinical diagnostic utility requires further validation in independent prospective cohorts.

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Publication Details

Journal
BMC Cancer
Published
2026-11-11
DOI
https://doi.org/10.1186/s12885-026-17345-9
Primary Topic
Lung Cancer Research Studies
Type
article
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article

Bioinformatic screening and experimental validation of programmed cell death–linked genes in lung adenocarcinoma

Xuebing Zhan, Zebiao Liu, Zhun Shu, Wenli Zhao et al.
BMC Cancer
Lung Cancer Research Studies
article

Bioinformatic screening and experimental validation of programmed cell death–linked genes in lung adenocarcinoma

Xuebing Zhan, Zebiao Liu, Zhun Shu, Wenli Zhao, Mingquan Chen, Ping Zhou, Xiaohui Fan
article en

Abstract

Abstract Background Lung cancer remains the primary cause of cancer-related mortality worldwide and is characterized by highly aggressive biological behavior. Programmed cell death (PCD) is a key regulator of tumor development and has been increasingly recognized as an important determinant of response to immunotherapy. Accordingly, investigating PCD-related mechanisms in lung adenocarcinoma (LUAD) may provide valuable information about disease progression and facilitate the identification of novel therapeutic targets. Methods To find differentially expressed genes linked to PCD in LUAD, data from the Gene Expression Omnibus (GEO) (GSE117570, GSE140797, GSE118370) and TCGA (TCGA-LUAD) were examined. Machine learning techniques were employed to find hub PCD differentially expressed genes with significantly linked LUAD features (hub LUAD-PCDDEGs). To evaluate the diagnostic capacity of hub LUAD-PCDDEGs, we developed diagnostic nomograms and receiver operating characteristic curves. Moreover, immune cell infiltration analysis and gene set enrichment analysis were employed to identify underlying molecular pathways. Western blot and qRT-PCR were utilized to verify the results. Results In both normal and LUAD samples, we found 264 PCDDEGs. We identified four hub genes, GGCT, HYAL2, BEX1, and FABP4, as candidate diagnostic biomarkers for LUAD using LASSO, random forest (RF), and support vector machine–recursive feature elimination studies. GGCT was significantly upregulated in LUAD tissues, while HYAL2, BEX1, and FABP4 were significantly down-regulated. These expression patterns were validated by qRT-PCR analysis. The upregulation of GGCT was further confirmed at the protein level by Western blotting and supported by immunohistochemistry (IHC). Additionally, a substantial increase in infiltration of 10 immune cell types in LUAD was observed, and this infiltration was strongly correlated with hub LUAD-PCDDEGs. These cell types included plasma cells, memory B cells, naive B cells, CD4 + memory-activated T cells, follicular helper T cells, gamma delta T cells, regulatory T cells, resting dendritic cells, M1 macrophages, and eosinophils. Conclusion In the context of LUAD, this study successfully identified four genes (GGCT, HYAL2, BEX1, and FABP4) linked to PCD. These findings suggest their potential as candidate biomarkers for LUAD; however, their clinical diagnostic utility requires further validation in independent prospective cohorts.

BMC Cancer
Lung Cancer Research Studies
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