Independent Prognostic Factor For Pancreatic Cancer Research Articles

A Novel Pancreatic Cancer Hypoxia Status Related Gene Signature for Prognosis and Therapeutic Responses.

Pancreatic cancer (PAC) is a highly fatal and aggressive type of cancer. Hypoxia is a common feature of PAC. The aim of this study was to develop a hypoxia status-related prognostic model for predicting the survival outcomes in PAC. The data sets of PAC from The Cancer Genome Atlas and the International Cancer Genome Consortium were used to construct and validate the signature. A 6 hypoxia status-related differential expression genes prognostic model for predicting the survival outcomes was established. The Kaplan-Meier analysis and Received operating characteristic curve indicated the good performance of the signature at predicting overall survival. Univariate and Multivariate Cox regression revealed that the signature was an independent prognostic factor in PAC. Weighted Gene Co-expression Network Analysis and immune infiltration analysis indicated that Immune-related pathways and immune cell infiltration was mostly enriched in the low-risk group, which presented a better prognosis. We also evaluated the predictive of the signature for immunotherapy and chemoradiotherapy. Risk gene LY6D may be a potential prognostic predictor of PAC. This model can be used as an independent prognostic factor for predicting clinical outcomes and a possible classifier for response to chemotherapy.

Cuproptosis-related LncRNAs signature as biomarker of prognosis and immune infiltration in pancreatic cancer.

Background: Pancreatic cancer (PC) is a malignant gastrointestinal tumor with a terrible prognosis. Cuproptosis is a recently discovered form of cell death. This study is intended to explore the relationship between cuproptosis-related lncRNAs (CRLncs) signature with the prognosis and the tumor microenvironment (TME) of PC. Methods: Transcript sequencing data of PC samples with clinical information were obtained from the Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and LASSO regression analysis were employed to construct the prognostic signature based on CRLncs associated with PC survival. A nomogram was created according to this signature, and the signaling pathway enrichment was analyzed. Subsequently, we explored the link between this prognostic signature with the mutational landscape and TME. Eventually, drug sensitivity was predicted based on this signature. Results: Forty-six of 159 CRLncs were most significantly relevant to the prognosis of PC, and a 6-lncRNA prognostic signature was established. The expression level of signature lncRNAs were detected in PC cell lines. The AUC value of the ROC curve for this risk score predicting 5-year survival in PC was .944, which was an independent prognostic factor for PC. The risk score was tightly related to the mutational pattern of PC, especially the driver genes of PC. Single-sample gene set enrichment analysis (ssGSEA) demonstrated a significant correlation between signature with the TME of PC. Ultimately, compounds were measured for therapy in high-risk and low-risk PC patients, respectively. Conclusion: A prognostic signature of CRLncs for PC was established in the current study, which may serve as a promising marker for the outcomes of PC patients and has important forecasting roles for gene mutations, immune cell infiltration, and drug sensitivity in PC.

Combining Single-Cell and Transcriptomic Data Revealed the Prognostic Significance of Glycolysis in Pancreatic Cancer.

Background: Pancreatic cancer (PC), the most common fatal solid malignancy, has a very dismal prognosis. Clinical computerized tomography (CT) and pathological TNM staging are no longer sufficient for determining a patient’s prognosis. Although numerous studies have suggested that glycolysis is important in the onset and progression of cancer, there are few publications on its impact on PC. Methods: To begin, the single-sample gene set enrichment analysis (ssGSEA) approach was used to quantify the glycolysis pathway enrichment fraction in PC patients and establish its prognostic significance. The genes most related to the glycolytic pathway were then identified using weighted gene co-expression network analysis (WGCNA). The glycolysis-associated prognostic signature in PC patients was then constructed using univariate Cox regression and lasso regression methods, which were validated in numerous external validation cohorts. Furthermore, we investigated the activation of the glycolysis pathway in PC cell subtypes at the single-cell level, performed a quasi-time series analysis on the activated cell subtypes and then detected gene changes in the signature during cell development. Finally, we constructed a decision tree and a nomogram that could divide the patients into different risk subtypes, according to the signature score and their different clinical characteristics and assessed the prognosis of PC patients. Results: Glycolysis plays a risky role in PC patients. Our glycolysis-related signature could effectively discriminate the high-risk and low-risk patients in both the trained cohort and the independent externally validated cohort. The survival analysis and multivariate Cox analysis indicated this gene signature to be an independent prognostic factor in PC. The prognostic ROC curve analysis suggested a high accuracy of this gene signature in predicting the patient prognosis in PC. The single-cell analysis suggested that the glycolytic pathway may be more activated in epithelial cells and that the genes in the signature were also mainly expressed in epithelial cells. The decision tree analysis could effectively identify patients in different risk subgroups, and the nomograms clearly show the prognostic assessment of PC patients. Conclusion: Our study developed a glycolysis-related signature, which contributes to the risk subtype assessment of patients with PC and to the individualized management of patients in the clinical setting.

Anillin is a prognostic factor and is correlated with genovariation in pancreatic cancer based on databases analysis.

Pancreatic cancer has a low survival rate globally. Anillin (ANLN) is involved in the pathogenesis of pancreatic cancer (PC). The present study used databases and reverse transcription-quantitative PCR to investigate the association between ANLN expression, clinical variables and the survival rate of patients with pancreatic cancer. Gene expression of ANLN in normal and cancer tissues was analyzed using data from The Cancer Genome Atlas, Oncomine and Gene Expression database of Normal and Tumor tissues 2 and ANOVA, and the association between ANLN mRNA expression and ANLN genovariation was analyzed using cBioPortal. The association between ANLN expression and the survival, clinical, pathological and prognostic characteristics of PC was analyzed using Kaplan-Meier (K-M) survival analysis, Kruskal Wallis and Mann Whitney-U tests, and logistic and Cox regression models. Gene Set Enrichment Analysis (GSEA) revealed the molecular pathways underpinning ANLN function in PC. Overexpression of ANLN was observed in PC cells (normal vs. tumor, P<0.01) and tissues (normal vs. tumor, P=0.008). Enhanced ANLN expression was associated with high tumor grade (grade 1 vs. grade 3, odds ratio: 5.662, P<0.001). However, ANLN expression was not associated with other clinical features (all P>0.05). K-M analysis suggested that increased ANLN expression was associated with poor survival (P=0.002). Univariate and multivariate analysis revealed the ANLN is an independent prognostic factor for PC (P<0.001). GSEA demonstrated the p53, cell cycle, DNA replication, mismatch repair, nucleotide excision repair and PC pathways were associated with low expression of ANLN. Overall, ANLN is more highly expressed in PC compared with in normal tissue, and is associated with poor differentiation. The expression of ANLN may be a novel prognostic marker of poor survival. Finally, ANLN exert its functions in PC through the p53, cell cycle, DNA replication, mismatch repair and nucleotide excision repair and pathways.

PLS3 predicts poor prognosis in pancreatic cancer and promotes cancer cell proliferation via PI3K/AKT signaling.

Plastin-3 plays a key role in cancer cell proliferation and invasion, but its prognostic value in pancreatic cancer (PACA) remains poorly defined. In this study, we show that PLS3 messenger RNA is overexpressed in PACA tissue compared with normal tissue. We accumulated 207 cases of PACA specimens to perform immunohistochemical analysis and demonstrated that PLS3 levels correlate with T-classification (p < .001) and pathology (p < .001). Furthermore, overall survival rates (p < .001) in tumors with high PLS3 expression were poor, as assessed through Kaplan-Meier survival analysis. PLS3 was found to be an independent prognostic factor for PACA through multivariate Cox regression analysis. Moreover, we found that PLS3 enhances the proliferation and invasion of tumor cells as assessed through Cell Counting Kit-8, wounding healing assays, and Transwell assays. The upregulation of PLS3 also led to enhanced phosphatidylinositol-3 kinase/protein kinase B signaling in PACA cells. These data suggest that PLS3 is a biomarker to estimate PACA progression and represents a molecular target for PACA therapy.

Stress-induced phosphoprotein 1 promotes pancreatic cancer progression through activation of the FAK/AKT/MMP signaling axis

BackgroundDependent on the extent of adenosine triphosphate (ATP) hydrolysis and/or ATP/ADP exchange, the stress-induced phosphoprotein 1 (STIP1) mediates molecular interaction and complex formation between the molecular chaperones heat shock protein (Hsp)70 and Hsp90. The overexpression of STIP1 is increasingly being documented in various human malignancies, including ovarian, cholangiocellular, renal and gastric cancers. However, the role of STIP1 in pancreatic cancer (PANC) and probable molecular mechanism remains largely unexplored. Methods & resultsIn the present study, using clinical samples (n = 88) and human PANC cell lines PANC-1, Capan-2, SW1990, and BxPC-3, we demonstrated that STIP1 is aberrantly expressed in human PANC tissues or cell lines compared to adjacent non-tumor pancreas samples or human pancreatic duct epithelial cells (HPDEC), respectively. Clinicopathological correlation studies revealed significant positive correlation between high STIP1 expression and lymph node involvement (p = 0.001), cancer metastasis (p = 0.002), microvascular invasion (p = 0.002), advance TNM stage (p = 0.024), perineural invasion (PNI; p = 0.013), and cancer-related death (p = 0.002) among patients with PANC. Univariate and multivariate analyses indicate that STIP1overexpression is an independent prognostic factor of PANC. Furthermore, STIP1 knockdown significantly inhibit the migration and invasive ability of PANC-1 and SW1990 cells, while downregulating N-cadherin and Vimentin, but upregulating E-cadherin mRNA expression levels, concurrently. We also demonstrated that STIP1 knockdown suppressed p-FAK, p-AKT, MMP2, MMP9, and Slug protein and mRNA expression levels, thus, indicating, at least in part, a role for STIP1 in the activation of FAK/AKT/MMP signaling. ConclusionTaken together, our results demonstrate a critical role for STIP1 in cancer metastasis, disease progression and poor prognosis, as well as, provide evidence suggestive of the therapeutic efficacy of STIP1-mediated targeting of the FAK/AKT/MMP signaling axis in patients with PANC.

Three hypomethylated genes were associated with poor overall survival in pancreatic cancer patients.

Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality. Aberrant DNA methylation plays a critical role in the occurrence, progression and prognosis of malignant tumors. In this study, we employed multiple datasets from APGI, TCGA and GEO to perform Multi-Omics analysis, including DNA methylation and expression profiling analysis. Three differentially expressed genes (SULT1E1, IGF2BP3, MAP4K4) with altered status of DNA methylation were identified and then enrolled into prognostic risk score model using LASSO regression. Univariate cox regression analysis indicated that high risk score was significantly associated with poor prognosis. Multivariate cox regression analysis proved the risk score was an independent prognostic factor for PC. In addition, time-dependent ROC curves indicated good performance of our model in predicting the 1-, 3- and 5-year survival of PC patients. Besides, stratified survival analysis revealed that the risk score model had greater prognostic value for patients of late stage with T3/T4 and N+. Pathway enrichment analysis suggested that these three genes might promote tumor progression by affecting signaling by Rho GTPases and chromosome segregation. In summary, three hypomethylated gene signature were significantly associated with patients’ overall survival, which might serve as potential prognostic biomarkers for PC patients.

The Expression and Prognostic Roles of MCMs in Pancreatic Cancer.

ObjectivesMinichromosome maintenance (MCM) proteins play important roles in DNA replication by interacting with other factors which participate in the regulation of DNA synthesis. Abnormal over-expression of MCMs was observed in numerous malignancies, such as colorectal cancer. However, the expression of MCMs in pancreatic cancer (PC) was less investigated so far. This study was designed to analyze the expression and prognostic roles of MCM1-10 in PC based on the data provided by The Cancer Genome Atlas (TCGA).MethodsPearson χ2 test was applied to evaluate the association of MCMs expression with clinicopathologic indicators, and biomarkers for tumor biological behaviors. Kaplan-Meier plots and log-rank tests were used to assess survival analysis, and univariate and multivariate Cox proportional hazard regression models were used to recognize independent prognostic factors.ResultsMCM1-10 were generally expressed in PC samples. The levels of some molecules were markedly correlated with that of biomarkers for S phase, proliferation, gemcitabine resistance. And part of these molecules over-expression was significantly associated with indicators of disease progression, such as depth of tumor invasion and lymph node metastasis. Furthermore, MCM2, 4, 6, 8, and 10 over-expression was remarkably associated with shorter disease free survival time, and MCM2, 4,8, and 10 over-expression was associated with shorter overall survival time. Further multivariate analysis suggested that MCM8 was an independent prognostic factor for PC.ConclusionMCMs abnormal over-expression was significantly associated with PC progression and prognosis. These molecules could be regarded as prognostic and therapeutic biomarkers for PC. The roles of MCMs may be vitally important and the underlying mechanisms need to be furtherinvestigated.