Scholarlink

<p>Pancreatic cancer is one of the most lethal malignancies worldwide, characterized by late diagnosis, aggressive progression, and poor survival. Dysregulation of regulated cell death (RCD) pathways, including apoptosis, ferroptosis, necroptosis, and mitochondrial dysfunction, contributes to tumor survival, therapy resistance, and immune evasion. Understanding the molecular mechanisms underlying these processes is critical for identifying prognostic biomarkers and therapeutic targets. Publicly available pancreatic cancer gene expression datasets (GSE227567 and GSE275246) were analyzed to identify differentially expressed RCD and mitochondrial genes. FRGs, APGs, NRGs, and MGs were intersected with the DEGs, followed by functional enrichment, protein–protein interaction (PPI) network construction, and hub gene prioritization. Co-expression, immune infiltration, and pathway activity analyses were performed across pathological stages and pan-cancer datasets. Missense SNPs in top hub genes ATF4 and PMAIP1 were evaluated for structural and energetic impact using HOPE and DynaMut. Analysis identified 448 FRGs, 73 APGs, 32 NRGs, and 930 MGs in GSE227567, and 123 APGs, 0 FRGs, 36 NRGs, and 577 MGs in GSE275246. Cross-dataset overlap was highest for APGs (116 genes), while other RCD and mitochondrial genes were largely dataset-specific. Filtering and PPI network analysis prioritized nine hub genes (ATF4, PMAIP1, BCL2L1, ATF3, TRADD, SRC, SFN, KCNMA1, GUK1). Functional enrichment highlighted mitochondrial metabolism, oxidative phosphorylation, and apoptosis pathways, with integration into PI3K–AKT, MAPK, p53, and VEGF signaling. Immune infiltration analysis revealed myeloid-enriched tumor microenvironments in mutant contexts. Pan-cancer and stage-wise expression profiling indicated that ATF4 and PMAIP1 exhibit consistent stage-dependent modulation. Survival analysis showed that high PMAIP1 expression correlates with poor prognosis (HR = 1.26, <i>p</i> = 0.00185), while ATF4 shows a protective trend (HR = 0.84, <i>p</i> = 0.0145). SNP analysis revealed six missense variants in each gene, with ATF4 variants predominantly destabilizing and affecting the bZIP domain, whereas PMAIP1 variants were mostly surface-exposed and modulatory. Our integrative analysis identifies ATF4 and PMAIP1 as key RCD- and mitochondrial-associated genes in pancreatic cancer, with functional SNPs that influence prognosis and may serve as therapeutic targets. These findings provide insights into mitochondrial-driven apoptosis, tumor progression, and potential avenues for precision medicine interventions.</p> Graphical Abstract <p>The analytical workflow was designed to ensure a clear and sequential representation of the study pipeline. Briefly, differential expression analysis was performed independently on GSE227567 (tumor vs. normal) and GSE275246 (treatment vs. control) datasets using standardized thresholds (|log₂ fold change|≥ 1 and FDR ≤ 0.05). The resulting differentially expressed genes were intersected with curated ferroptosis-related genes (FRGs), apoptosis-related genes (APGs), necroptosis-related genes (NRGs), and mitochondrial genes (MGs) to identify biologically relevant candidates. These gene sets were subsequently subjected to functional enrichment analysis to characterize their involvement in mitochondrial and regulated cell death pathways. Protein–protein interaction (PPI) analysis was then performed in two complementary contexts: (i) pathway-focused PPI networks to identify functionally enriched gene clusters, and (ii) a global PPI network for topological analysis and hub gene prioritization. Finally, candidate genes for SNP analysis, including ATF4 and PMAIP1, were selected based on integrated criteria encompassing network centrality, enrichment significance, stage-dependent expression patterns, and survival associations. This structured pipeline ensures a transparent and biologically coherent progression from data acquisition to functional and structural interpretation.</p> <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Functional variants in ATF4 and PMAIP1 reveal mitochondrial apoptosis dependencies driving pancreatic cancer progression

  • 期刊论文

  • 发表日期：

    2026年4月23日

查看全文
  • Syed Luqman Ali,
  • Awais Ali,
  • Elham Mohammed Khatrawi,
  • Rafia Kiran,
  • Bilal Khan

摘要

Pancreatic cancer is one of the most lethal malignancies worldwide, characterized by late diagnosis, aggressive progression, and poor survival. Dysregulation of regulated cell death (RCD) pathways, including apoptosis, ferroptosis, necroptosis, and mitochondrial dysfunction, contributes to tumor survival, therapy resistance, and immune evasion. Understanding the molecular mechanisms underlying these processes is critical for identifying prognostic biomarkers and therapeutic targets. Publicly available pancreatic cancer gene expression datasets (GSE227567 and GSE275246) were analyzed to identify differentially expressed RCD and mitochondrial genes. FRGs, APGs, NRGs, and MGs were intersected with the DEGs, followed by functional enrichment, protein–protein interaction (PPI) network construction, and hub gene prioritization. Co-expression, immune infiltration, and pathway activity analyses were performed across pathological stages and pan-cancer datasets. Missense SNPs in top hub genes ATF4 and PMAIP1 were evaluated for structural and energetic impact using HOPE and DynaMut. Analysis identified 448 FRGs, 73 APGs, 32 NRGs, and 930 MGs in GSE227567, and 123 APGs, 0 FRGs, 36 NRGs, and 577 MGs in GSE275246. Cross-dataset overlap was highest for APGs (116 genes), while other RCD and mitochondrial genes were largely dataset-specific. Filtering and PPI network analysis prioritized nine hub genes (ATF4, PMAIP1, BCL2L1, ATF3, TRADD, SRC, SFN, KCNMA1, GUK1). Functional enrichment highlighted mitochondrial metabolism, oxidative phosphorylation, and apoptosis pathways, with integration into PI3K–AKT, MAPK, p53, and VEGF signaling. Immune infiltration analysis revealed myeloid-enriched tumor microenvironments in mutant contexts. Pan-cancer and stage-wise expression profiling indicated that ATF4 and PMAIP1 exhibit consistent stage-dependent modulation. Survival analysis showed that high PMAIP1 expression correlates with poor prognosis (HR = 1.26, p = 0.00185), while ATF4 shows a protective trend (HR = 0.84, p = 0.0145). SNP analysis revealed six missense variants in each gene, with ATF4 variants predominantly destabilizing and affecting the bZIP domain, whereas PMAIP1 variants were mostly surface-exposed and modulatory. Our integrative analysis identifies ATF4 and PMAIP1 as key RCD- and mitochondrial-associated genes in pancreatic cancer, with functional SNPs that influence prognosis and may serve as therapeutic targets. These findings provide insights into mitochondrial-driven apoptosis, tumor progression, and potential avenues for precision medicine interventions.

Graphical Abstract

The analytical workflow was designed to ensure a clear and sequential representation of the study pipeline. Briefly, differential expression analysis was performed independently on GSE227567 (tumor vs. normal) and GSE275246 (treatment vs. control) datasets using standardized thresholds (|log₂ fold change|≥ 1 and FDR ≤ 0.05). The resulting differentially expressed genes were intersected with curated ferroptosis-related genes (FRGs), apoptosis-related genes (APGs), necroptosis-related genes (NRGs), and mitochondrial genes (MGs) to identify biologically relevant candidates. These gene sets were subsequently subjected to functional enrichment analysis to characterize their involvement in mitochondrial and regulated cell death pathways. Protein–protein interaction (PPI) analysis was then performed in two complementary contexts: (i) pathway-focused PPI networks to identify functionally enriched gene clusters, and (ii) a global PPI network for topological analysis and hub gene prioritization. Finally, candidate genes for SNP analysis, including ATF4 and PMAIP1, were selected based on integrated criteria encompassing network centrality, enrichment significance, stage-dependent expression patterns, and survival associations. This structured pipeline ensures a transparent and biologically coherent progression from data acquisition to functional and structural interpretation.