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Background <p>Pancreatic cancer (PC) is one of the leading causes of cancer-related death worldwide. The lack of effective diagnostic biomarkers and therapeutic targets makes PC difficult to screen and treat. The aim of this study was to develop a diagnostic and survival-related gene signature for PC to construct a prognostic model.</p> Methods <p>An Arraystar RNA microarray was used to identify differentially expressed genes (DEGs) in clinical plasma samples between the PC group and the control group. We performed weighted gene co-expression network analysis (WGCNA) to identify significant modules of DEGs in the Gene Expression Omnibus (GEO) cohort and to obtain potential diagnostic hub genes by intersecting the significant module genes with microarray-derived DEGs. In addition, least absolute shrinkage and selection operator (LASSO) cox regression analysis were performed to construct a prognostic model. Moreover, clinical samples were analyzed to evaluate the expression levels of the independent risk genes.</p> Results <p>Our microarray data revealed 228 significantly upregulated mRNA in plasma samples. FERMT1, S100A14, KCNN4, PKM, and ITGA3 were identified as robust diagnostic biomarkers. Integrating these hub genes, we constructed a prognostic model, with the nomogram exhibiting prognostic value in TCGA cohort. Univariate and multivariate Cox proportional hazard analyses revealed that the expression of FERMT1, S100A14, and ITGA3 was an independent risk factor for poor prognosis. RT-qPCR validation in clinical plasma sample demonstrated concordant expression patterns of the independent risk gene, further supporting its potential prognostic relevance.</p> Conclusion <p>Our results revealed the potential biomarkers for the prediction of PC prognosis in addition to clinicopathological factors. Moreover, this study identifies potential biomarkers warranting further functional investigation.</p>

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Identifying diagnostic markers and constructing a prognostic model for pancreatic cancer based on microarray and bioinformatic analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Chang Liu,
  • Dan Qian,
  • Ye Tian,
  • Ying Yang,
  • Menglu Li,
  • Yiqin You,
  • Liqun Zhang

摘要

Background

Pancreatic cancer (PC) is one of the leading causes of cancer-related death worldwide. The lack of effective diagnostic biomarkers and therapeutic targets makes PC difficult to screen and treat. The aim of this study was to develop a diagnostic and survival-related gene signature for PC to construct a prognostic model.

Methods

An Arraystar RNA microarray was used to identify differentially expressed genes (DEGs) in clinical plasma samples between the PC group and the control group. We performed weighted gene co-expression network analysis (WGCNA) to identify significant modules of DEGs in the Gene Expression Omnibus (GEO) cohort and to obtain potential diagnostic hub genes by intersecting the significant module genes with microarray-derived DEGs. In addition, least absolute shrinkage and selection operator (LASSO) cox regression analysis were performed to construct a prognostic model. Moreover, clinical samples were analyzed to evaluate the expression levels of the independent risk genes.

Results

Our microarray data revealed 228 significantly upregulated mRNA in plasma samples. FERMT1, S100A14, KCNN4, PKM, and ITGA3 were identified as robust diagnostic biomarkers. Integrating these hub genes, we constructed a prognostic model, with the nomogram exhibiting prognostic value in TCGA cohort. Univariate and multivariate Cox proportional hazard analyses revealed that the expression of FERMT1, S100A14, and ITGA3 was an independent risk factor for poor prognosis. RT-qPCR validation in clinical plasma sample demonstrated concordant expression patterns of the independent risk gene, further supporting its potential prognostic relevance.

Conclusion

Our results revealed the potential biomarkers for the prediction of PC prognosis in addition to clinicopathological factors. Moreover, this study identifies potential biomarkers warranting further functional investigation.