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Background Gene co-expression correlations frequently signal shared biological functions with coordinated regulation. We hypothesized that maintained correlations might be essential for cellular survival, representing potential vulnerabilities of cancer cells. We aimed to reveal correlations preserved in pancreatic ductal adenocarcinomas (PDAC) across normal and tumor tissues. Methods: We searched the NCBI GEO for raw microarray data and the TCGA project for RNA-seq data. The microarray dataset consisted of 248 tumors and 108 normal samples, allowing the analysis of 12,210 genes. The RNA-seq dataset incorporated 177 tumors, four normal samples from TCGA, and 248 normal samples from GTEx, enabling the analysis of 21,479 genes. Genes with an altered expression were identified with a Mann-Whitney U test at p Results: Altogether 371 significant correlations involving 262 genes were preserved across normal samples and tumors in both RNA-seq and gene chip platforms. The identified close-knit gene network is mainly responsible for extracellular matrix organization. Seven genes (SPARC, COL6A3, MMP2, HTRA1, FN1, PALLD, and COL3A1) were heavily overrepresented in maintained correlations, some of them participating in as many as 58 interactions. High expression of 28 genes was linked to poor disease outcome at FDR ≤ 10%, out of which FN1, an extracellular matrix component, was both overrepresented in maintained correlations and associated with worse overall survival (p = 0.00097, FDR ≤ 5%). The growing expression of two genes, MYL12A and MYL12B, across normal tissues, primary, and metastatic tumors may drive the acquisition of motility by cancer cells. Conclusions: Our results propose novel prognostic biomarkers of PDAC and pinpoint fundamental cellular interactions as potential targets for combination therapies. Furthermore, the presence of significant correlations across different data platforms substantiates the validity of our findings.

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IntroductionWhile Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.MethodsEvaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.ResultsSix proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.DiscussionOur study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.