Pancreatic Cancer Gene Research Articles

Identification of the immune cell infiltration landscape in pancreatic cancer to assist immunotherapy.

Background: A malignant tumor's immune environment, including infiltrating immune cell status, can be critical to patient outcomes. Recent studies have shown that immune cell infiltration (ICI) in pancreatic cancer (PC) is highly correlated with the response to immunotherapy and patient prognosis. Therefore, we aimed to create an ICI score that accurately predicts patient outcomes and immunotherapeutic efficacy. Methods: The ICI statuses of patients with PC were estimated from the publicly available The Cancer Genome Atlas (TCGA) pancreatic ductal adenocarcinoma and GSE57495 gene expression datasets using two computational algorithms (CIBERSORT and ESTIMATE). ICI and transcriptome subsets were defined using a clustering algorithm, and survival analysis was also performed. Principal component analysis was used to calculate the novel ICI score, and gene set enrichment analysis was performed to identify the pathways underlying the defined clusters. The tumor mutational burden (TMB) was further explored in TCGA cohort, and survival analysis was used to assess the capability of the ICI and TMB scores to predict overall survival. Additionally, common driver gene mutations and their differential expression in the different ICI score group were investigated. Results: The ICI landscapes of 240 patients were generated using the devised algorithm, revealing three ICI and three gene clusters whose use improved the prediction of overall survival (p=0.019 and p<0.001, respectively). Crucial immune checkpoint genes were differentially expressed among these subtypes; the RIG-I-LIKE and NOD-LIKE receptor signaling pathways were enriched in samples with low ICI scores (p<0.05). We also found that the TMB scores could predict survival outcomes, whereas the ICI scores also could predict prognoses independent of TMB. Notably, ICI scores could effectively predict responses to immunotherapy. KRAS, TP53, CDKN2A, SMAD4and TTN remained the most commonly mutated genes in PC; moreover, KRAS and TP53 mutation rates were significantly different between the two ICI score groups. Conclusions: We developed a novel ICI score that could independently predict the response to immunotherapy and survival of patients with PC. Evaluation of the ICI landscape in a larger cohort could clarify the interactions between these infiltrating cells, the tumor microenvironmentand response to immunotherapy.

Bioinformatic Analysis of Prognostic and Immune-Related Genes in Pancreatic Cancer.

Pancreatic cancer (PC) is a malignant tumor with poor prognosis. The poor effect of surgery and chemotherapy makes the research of immunotherapy target molecules significant. Therefore, identifying the new molecular targets of PC is important for patients. In our study, we systematically analyzed molecular correlates of pancreatic cancer by bioinformatic analysis. We characterized differentially expressed analysis based on the TCGA pancreatic cancer dataset. Then, univariate Cox regression was employed to screen out overall survival- (OS-) related DEGs. Based on these genes, we established a risk signature by the multivariate Cox regression model. The ICGC cohort and GSE62452 cohort were used to validate the reliability of the risk signature. The impact of T lymphocyte-related genes from risk signature was confirmed in PC. Here, we observed the correlation between the T lymphocyte-related genes and the expression level of targeted therapy. We established a five-mRNA (LY6D, ANLN, ZNF488, MYEOV, and SCN11A) prognostic risk signature. Next, we identified ANLN and MYEOV that were associated with T lymphocyte infiltrations (P < 0.05). High ANLN and MYEOV expression levels had a poorer prognosis in decreased T lymphocyte subgroup in PC. Correlation analysis between ANLN and MYEOV and immunomodulators showed that ANLN and MYEOV may have potential value in pancreatic cancer immunotherapy.

O1 Elevated expression level of capillary morphogenesis gene 2 in pancreatic ductal adenocarcinoma cell is associated with distant metastasis and poor prognosis

Abstract Introduction Emerging evidence revealed the active role played by capillary morphogenesis gene 2 (CMG2) during the disease progression and metastasis of some cancers. It was shown that CMG2 was increased in pancreatic tumours in our previous research. The current study further validated this finding for the expression of CMG2 in pancreatic cancer and also dissected its clinical implication. Method Immunochemical staining of CMG2 was performed on a pancreatic cancer tissue microarray (PA2081, Biomax, n = 104). Clinical relevance of CMG2 was analysed in TCGA pancreatic cancer cohort and gene array data (GSE71729) using ANOVA and Kaplan-Meier analyses. Influence of CMG2 on adhesion to a mesothelial cell monolayer was determined using both Mia-PaCa-2 and PANC-1 cell lines with CMG2 knockdown. Result CMG2 is increased significantly in pancreatic ductal adenomas, P &amp;lt; 0.001 compared with adjacent non-tumour tissues. Higher levels of CMG2 were also revealed in the distant metastases of pancreatic cancer, P &amp;lt; 0.001 compared with both primary tumuors and distant metastasis. Elevated expression CMG2 protein in pancreatic cancers was also observed on the tissue microarray. Patients with higher CMG2 expression tumours had shorter overall survival (median = 15.8 months), P &amp;lt; 0.001 compared with those patients with lower CMG2 expressing tumours (median = 30.4 months). Knockdown of the CMG2 significantly decreased the number of cells which adhere to the mesothelial cells (P &amp;lt; 0.001). Conclusion Elevated CMG2 expression in pancreatic ductal adenocarcinomas is associated with distant metastasis and shorter survival which requires further investigation to shed light on its therapeutic potential. Take-home Message Elevated CMG2 expression in pancreatic ductal adenocarcinomas is associated with distant metastasis and shorter survival.

NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer.

Pancreatic cancer (PC) is a highly fatal disease, yet its causes remain unclear. Comprehensive analysis of different types of PC genetic data plays a crucial role in understanding its pathogenic mechanisms. Currently, non-negative matrix factorization (NMF)-based methods are widely used for genetic data analysis. Nevertheless, it is a challenge for them to integrate and decompose different types of genetic data simultaneously. In this paper, a non-NMF network analysis method, NMFNA, is proposed, which introduces a graph-regularized constraint to the NMF, for identifying modules and characteristic genes from two-type PC data of methylation (ME) and copy number variation (CNV). Firstly, three PC networks, i.e., ME network, CNV network, and ME–CNV network, are constructed using the Pearson correlation coefficient (PCC). Then, modules are detected from these three PC networks effectively due to the introduced graph-regularized constraint, which is the highlight of the NMFNA. Finally, both gene ontology (GO) and pathway enrichment analyses are performed, and characteristic genes are detected by the multimeasure score, to deeply understand biological functions of PC core modules. Experimental results demonstrated that the NMFNA facilitates the integration and decomposition of two types of PC data simultaneously and can further serve as an alternative method for detecting modules and characteristic genes from multiple genetic data of complex diseases.

Screening and Validation of Independent Predictors of Poor Survival in Pancreatic Cancer.

Pancreatic cancer is a digestive system malignant tumor with high mortality and poor prognosis, but the mechanisms of progression remain unclear in pancreatic cancer. It’s necessary to identify the hub genes in pancreatic cancer and explore the novel potential predictors in the prognosis of pancreatic cancer. We downloaded two mRNA expression profiles from Gene Expression Omnibus and The Cancer Genome Atlas Pancreatic Cancer (TCGA-PAAD) datasets to screen the commonly differentially expressed genes in pancreatic cancer by limma package in R. Subsequently, measurement of the functional similarity among the 38 DEGs in common was performed to identify the hub genes using GOSemSim package. Then, survival analysis and Cox regression were applied to explore prognosis-related hub genes using the survival package. Statistics analysis by two-tailed Student’s t-test or one-way based on TCGA-PAAD datasets and qPCR detection in clinical samples were performed to explore the correlations between expression of hub genes in pancreatic cancer tissues and clinical parameters. Based on integrated analysis of TCGA and GEO datasets, we screened 38 DEGs in common, which were all up-regulated. The functional similarity results showed that 10 DEGs including TSPAN1, MSLN, C1orf116, PKP3, CEACAM6, BAIAP2L1, PPL, RAB25, ERBB3, and AP1M2 in the DEGs in common, which had the higher average functional similarity, were considered as the hub genes. Survival analysis results and Cox regression analysis showed that TSPAN1, CEACAM6, as well as ERBB3 were all associated with poor overall survival of PC. qPCR results showed that the expression levels of TSPAN1 and ERBB3 were significantly upregulated in the PC tissues. The statistical analysis results revealed that TSPAN1 expression correlated significantly with histologic grade, T stage, clinical stage, and vital status by two-tailed Student’s t-test or one-way ANOVA; ERBB3 expression correlated significantly with T stage, clinical stage, and vital status by two-tailed Student’s t-test or one-way ANOVA. We found that TSPAN1 and ERBB3 could be independent predictors of poor survival in pancreatic cancer.

Abstract 1411: LAMC2 promotes progression and gemcitabine resistance through modulation of EMT and ATP-binding transporters in pancreatic ductal adenocarcinoma

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor prognosis. Gemcitabine-based therapeutic options remain the first treatment choice for the majority of PDAC patients. Unfortunately, however, currently there is lack of availability of robust prognostic and predictive biomarkers in patients with PDAC. Laminin γ2 (LAMC2) is frequently overexpressed in several cancers, and its high expression facilitates cancer development and chemoresistance. The role of LAMC2 in disease pathogenesis, as well as its clinical significance, if any, in PDAC remains unclear. Considering that a better understanding of its molecular function will likely help improve the prognosis in PDAC patients, herein, we undertook this study to elucidate the clinical and biological function of LAMC2 in PDAC. Methods: LAMC2 expression was assessed in tissue specimens from a cohort of 114 PDAC patients who received surgical treatment. This was followed by examining the clinical significance of this gene in multiple patient cohorts. The qRT-PCR based gene expression data were subsequently analyzed in the context of prognosis of PDAC patients. Finally, functional characterization of the role of LAMC2 in PDAC was investigated in a series of experiments following small interfering RNA (siRNA)- based knockdown of this gene in pancreatic cancer (PC) cells. Results: Higher expression of LAMC2 significantly correlated with poor survival in PDAC patient cohort (overall survival [OS]: 23.0 vs 14.2 months, Hazard ratio [HR]=1.61; 95% confidence interval [CI], 1.09-2.79, P &amp;lt; 0.01, relapse-free survival [RFS]: 9.0 vs 6.2 months, HR=1.61, 95%CI, 0.96-2.71, P &amp;lt; 0.05). In multivariate analysis which included several clinical factors, elevated LAMC2 expression served as an independent prognostic factor for OS (HR=1.71; 95% CI, 1.09-2.68). Moreover, higher LAMC2 expression levels predicted response to gemcitabine-based adjuvant therapy in patients with PDAC (HR=1.66, 95%CI, 0.94-2.93). The inhibition of LAMC2 expression enhanced gemcitabine sensitivity and induction of apoptosis in PC cell lines. Moreover, siRNA mediated suppression of LAMC2 expression led to inhibited colony formation ability, migration and invasion potential. Furthermore, LAMC2 was found to regulate the expression of various epithelial-mesenchymal transition (EMT) associated genes. In addition, LAMC2 expression significantly correlated with genes associated with the several key ATP-binding cassette (ABC) transporters in both PC cell lines and PDAC tissues. Conclusion: We conclude that LAMC2 regulates gemcitabine sensitivity through EMT and ABC transporter pathways in PDAC, and this gene may be a potential therapeutic target in patients with PDAC. Citation Format: Yasuyuki Okada, Satoshi Nishiwada, Naoki Takahashi, Tetsuji Takayama, Ajay Goel. LAMC2 promotes progression and gemcitabine resistance through modulation of EMT and ATP-binding transporters in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1411.

Abstract 2064: Homologous recombination repair somatic/germline mutation detection in plasma by next-generation sequencing in Chinese solid tumor patients

Abstract Purpose: PARP inhibitors (PARPis) have been approved for germline and/or somatic BRCA-mutated ovarian cancer, breast cancer, pancreatic cancer and homologous recombination repair gene (HRR)-mutated prostate cancer. Emerging evidence has shown that antitumor activity of PARPis could expand to other HRR-mutated solid tumors. Therefore, efficient and timely methods to detect both somatic and germline HRR mutations using tissue or blood are urgently needed. Here, we first specially reported the prevalence of HRR mutation in plasma samples using Next-generation sequencing (NGS). Methods: A total of 1601 paired plasma-white blood cell samples and 3404 paired tumor-white blood from Chinese solid tumor patients were collected between October 2019 and October 2020. The samples were assessed with targeted NGS of circulating tumor DNA (ctDNA) or tumor tissue DNA (ttDNA) to detect deleterious somatic and germline mutations in 16 core HR genes in a CLIA-certified laboratory. Results: Overall, 165/1601 (10.3%) patients had at least one somatic or germline HRR gene mutations by ctDNA-based NGS analysis. The most commonly mutated tumor types were endometrium (42.9%, n=7), ovary (38.5%, n=13), prostate (27.8%, n=108), neuroendocrine (20%, n=5), breast (19%, n=42), melanoma (16.7%, n=6), stomach (15.8%, n=101), biliary tract (15.6%, n=64), cervix (12.5%, n=8), pancreas (11.4%, n=70), colorectum (9.8%, n=174) and bladder urinary tract (9.5%, n=21). Except for the four tumor types (endometrium, neuroendocrine, melanoma and cervix) with less than ten samples, the prevalence of HRR mutation was comparable with the ttDNA-based NGS data. Among HRR mutations, BRCA2 was the most commonly mutated gene (3.2%), followed by ATM (2.4%), BRCA1 (1.4%), CHEK2 (1.2%), BARD1 (0.7%), PALB2 (0.7%), CDK12 (0.7%) and FANCA (0.5%). Most of these data was similar to the results of ttDNA-based NGS. Moreover, slightly higher prevalence was found in BRCA2 (3.2% vs 2.5%) and CHEK2 (1.2% vs 0.7%), and slightly lower prevalence was found in ATM (2.4% vs 3.2%) and CDK12 (0.7% vs 1.4%). Conclusion: We first described the prevalence of somatic and/or germline HRR mutations detected by ctDNA-based NGS in a Chinese solid tumor cohort. Further prospective study is needed to compare the impact of the HRR status identified by ctDNA and ttDNA NGS on PARPis efficacy. Citation Format: Wei Zhu, Hui Chen, Bei Zhang, Xiaochen Zhao, Yuezong Bai. Homologous recombination repair somatic/germline mutation detection in plasma by next-generation sequencing in Chinese solid tumor patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2064.

Bioinformatics-Based Identification of Tumor Microenvironment-Related Prognostic Genes in Pancreatic Cancer.

ObjectiveGrowing evidence has highlighted that the immune and stromal cells that infiltrate in pancreatic cancer microenvironment significantly influence tumor progression. However, reliable microenvironment-related prognostic gene signatures are yet to be established. The present study aimed to elucidate tumor microenvironment-related prognostic genes in pancreatic cancer.MethodsWe applied the ESTIMATE algorithm to categorize patients with pancreatic cancer from TCGA dataset into high and low immune/stromal score groups and determined their differentially expressed genes. Then, univariate and LASSO Cox regression was performed to identify overall survival-related differentially expressed genes (DEGs). And multivariate Cox regression analysis was used to screen independent prognostic genes and construct a risk score model. Finally, the performance of the risk score model was evaluated by Kaplan-Meier curve, time-dependent receiver operating characteristic and Harrell’s concordance index.ResultsThe overall survival analysis demonstrated that high immune/stromal score groups were closely associated with poor prognosis. The multivariate Cox regression analysis indicated that the signatures of four genes, including TRPC7, CXCL10, CUX2, and COL2A1, were independent prognostic factors. Subsequently, the risk prediction model constructed by those genes was superior to AJCC staging as evaluated by time-dependent receiver operating characteristic and Harrell’s concordance index, and both KRAS and TP53 mutations were closely associated with high risk scores. In addition, CXCL10 was predominantly expressed by tumor associated macrophages and its receptor CXCR3 was highly expressed in T cells at the single-cell level.ConclusionsThis study comprehensively investigated the tumor microenvironment and verified immune/stromal-related biomarkers for pancreatic cancer.

KDM1A and KDM3A promote tumor growth by upregulating cell cycle-associated genes in pancreatic cancer.

Pancreatic cancer is a highly malignant cancer of the pancreas with a very poor prognosis. Methylation of histone lysine residues is essential for regulating cancer physiology and pathophysiology, mediated by a set of methyltransferases (KMTs) and demethylases (KDMs). This study surveyed the expression of methylation regulators functioning at lysine 9 of histone 3 (H3K9) in pancreatic lesions and explored the underlying mechanisms. We analyzed KDM1A and KDM3A expression in clinical samples by immunohistochemical staining and searching the TCGA PAAD program and GEO datasets. Next, we identified the variation in tumor growth in vitro and in vivo after knockdown of KDM1A or KDM3A and explored the downstream regulators of KDM1A and KDM3A via RNA-seq, and gain- and loss-of-function assays. Eleven H3K9 methylation regulators were highly expressed in pancreatic cancer, and only KDM1A and KDM3A expression positively correlated with the clinicopathological characteristics in pancreatic cancer. High expression of KDM1A or KDM3A positively correlated with pathological grade, lymphatic metastasis, invasion, and clinical stage. Kaplan-Meier analysis indicated that a higher level of KDM1A or KDM3A led to a shorter survival period. Knockdown of KDM1A or KDM3A led to markedly impaired tumor growth in vitro and in vivo. Mechanistically, CCNA2, a cell cycle-associated gene was partially responsible for KDM1A knockdown-mediated effect and CDK6, also a cell cycle-associated gene was partially responsible for KDM3A knockdown-mediated effect on pancreatic cancer cells. Our study demonstrates that KDM1A and KDM3A are highly expressed in pancreatic cancer and are intimately correlated with clinicopathological factors and prognosis. The mechanism of action of KDM1A or KDM3A was both linked to the regulation of cell cycle-associated genes, such as CCNA2 or CDK6, respectively, by an H3K9-dependent pathway.

Hypoxia-Related Gene FUT11 Promotes Pancreatic Cancer Progression by Maintaining the Stability of PDK1.

BackgroundHypoxia is associated with the development of pancreatic cancer (PC). However, genes associated with hypoxia response and their regulatory mechanism in PC cells were unclear. The current study aims to investigate the role of the hypoxia associated gene fucosyltransferase 11 (FUT11) in the progression of PC.MethodsIn the preliminary study, bioinformatics analysis predicted FUT11 as a key hypoxia associated gene in PC. The expression of FUT11 in PC was evaluated using quantitative real-time PCR (qRT-PCR), Western blot and immunohistochemistry. The effects of FUT11 on PC cells proliferation and migration under normoxia and hypoxia were evaluated using Cell Counting Kit 8, 5-ethynyl-2’-deoxyuridine (EDU) assay, colony formation assay and transwell assay. The effects of FUT11 in vivo was examined in mouse tumor models of liver metastasis and subcutaneous xenograft. Furthermore, Western blot, luciferase assay and immunoprecipitation were performed to explore the regulatory relationship among FUT11, hypoxia-inducible factor 1α (HIF1α) and pyruvate dehydrogenase kinase 1 (PDK1) in PC.ResultsFUT11 was markedly increased of PC cells with hypoxia, upregulated in the PC clinical tissues, and predicted a poor outcome of PC patients. Inhibition of FUT11 reduced PC cell growth and migratory ability of PC cells under normoxia and hypoxia conditions in vitro, and growth and tumor cell metastasis in vivo. FUT11 bound to PDK1 and regulated the expression PDK1 under normoxia and hypoxia. FUT11 interacted with PDK1 and decreased the ubiquitination of PDK1, lead to the activation of AKT/mTOR signaling pathway. FUT11 knockdown significantly increased the degradation of PDK1 under hypoxia, while treatment with MG132 can relieve the degradation of PDK1 induced by FUT11 knockdown. Overexpression of PDK1 in PC cells under hypoxia conditions reversed the suppressive impacts of FUT11 knockdown on PC cell growth and migration. In addition, HIF1α bound to the promoter of FUT11 and increased its expression, as well as co-expressed with FUT11 in PC tissues. Furthermore, overexpression of FUT11 partially rescued the suppressive effects of HIF1α knockdown on PC cell growth and migration in hypoxia condition.ConclusionOur data implicate that hypoxia-induced FUT11 contributes to proliferation and metastasis of PC by maintaining the stability of PDK1, thus mediating activation of AKT/mTOR signaling pathway, and suggest that FUT11 could be a novel and effective target for the treatment of pancreatic cancer.

Gene expression of cytokinesis regulators PRC1, KIF14 and CIT has no prognostic role in colorectal and pancreatic cancer.

Colorectal cancer is one of the most common cancers and pancreatic cancer is among the most fatal and difficult to treat. New prognostic biomarkers are urgently needed to improve the treatment of colorectal and pancreatic cancer. Protein regulating cytokinesis 1 (PRC1), kinesin family member 14 (KIF14) and citron Rho-interacting serine/threonine kinase (CIT) serve important roles in cytokinesis, are strongly associated with cancer progression and have prognostic potential. The present study aimed to investigate the prognostic relevance of the PRC1, KIF14 and CIT genes in colorectal and pancreatic cancer. PRC1, KIF14 and CIT transcript expression was assessed by reverse transcription-quantitative PCR in tumors and paired distant unaffected mucosa from 67 patients with colorectal cancer and tumors and paired non-neoplastic control tissues from 48 patients with pancreatic cancer. The extent of transcript dysregulation between tumor and control tissues and between groups of patients divided by main clinical characteristics, namely patients' age and sex, disease stage, localization and grade, was determined. Finally, the associations of transcript levels in tumors with disease-free interval and overall survival time were evaluated. PRC1, KIF14 and CIT transcripts were upregulated in tumors compared with control tissues. PRC1, KIF14 and CIT levels strongly correlated to each other in both colorectal and pancreatic tumor and control tissues after correction for multiple testing. However, no significant associations were found among the transcript levels of PRC1, KIF14 and CIT and disease-free interval or overall survival time. In summary, the present study demonstrated mutual correlation of PRC1, KIF14 and CIT cytokinesis regulators with no clear prognostic value in pancreatic and colorectal cancers. Hence, according to the results of the present study, transcript levels of these genes cannot be clinically exploited as prognostic biomarkers in colorectal or pancreatic cancer patients.

Identification of prognostic lipid droplet-associated genes in pancreatic cancer patients via bioinformatics analysis

BackgroundPancreatic cancer is the fourth leading cause of cancer deaths in the United States both in females and in males, and is projected to become the second deadliest cancer by 2030. The overall 5-year survival rate remains at around 10%. Cancer metabolism and specifically lipid metabolism plays an important role in pancreatic cancer progression and metastasis. Lipid droplets can not only store and transfer lipids, but also act as molecular messengers, and signaling factors. As lipid droplets are implicated in reprogramming tumor cell metabolism and in invasion and migration of pancreatic cancer cells, we aimed to identify lipid droplet-associated genes as prognostic markers in pancreatic cancer.MethodsWe performed a literature search on review articles related to lipid droplet-associated proteins. To select relevant lipid droplet-associated factors, bioinformatics analysis on the GEPIA platform (data are publicly available) was carried out for selected genes to identify differential expression in pancreatic cancer versus healthy pancreatic tissues. Differentially expressed genes were further analyzed regarding overall survival of pancreatic cancer patients.Results65 factors were identified as lipid droplet-associated factors. Bioinformatics analysis of 179 pancreatic cancer samples and 171 normal pancreatic tissue samples on the GEPIA platform identified 39 deferentially expressed genes in pancreatic cancer with 36 up-regulated genes (ACSL3, ACSL4, AGPAT2, BSCL2, CAV1, CAV2, CAVIN1, CES1, CIDEC, DGAT1, DGAT2, FAF2, G0S2, HILPDA, HSD17B11, ICE2, LDAH, LIPE, LPCAT1, LPCAT2, LPIN1, MGLL, NAPA, NCEH1, PCYT1A, PLIN2, PLIN3, RAB5A, RAB7A, RAB8A, RAB18, SNAP23, SQLE, VAPA, VCP, VMP1) and 3 down-regulated genes (FITM1, PLIN4, PLIN5). Among 39 differentially expressed factors, seven up-regulated genes (CAV2, CIDEC, HILPDA, HSD17B11, NCEH1, RAB5A, and SQLE) and two down-regulation genes (BSCL2 and FITM1) were significantly associated with overall survival of pancreatic cancer patients. Multivariate Cox regression analysis identified CAV2 as the only independent prognostic factor.ConclusionsThrough bioinformatics analysis, we identified nine prognostic relevant differentially expressed genes highlighting the role of lipid droplet-associated factors in pancreatic cancer.

Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer.

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.

A Pan-Cancer Analysis of the Oncogenic Role of Twinfilin Actin Binding Protein 1 in Human Tumors.

BackgroundUnderstanding common and unique mechanisms driving oncogenic processes in human tumors is indispensable to develop efficient therapies. Recent studies have proposed Twinfilin Actin Binding Protein 1 (TWF1) as a putative driver gene in lung cancer, pancreatic cancer and breast cancer, however a systematic pan-cancer analysis has not been carried out.MethodsHere, we set out to explore the role of TWF1 in 33 tumor types using TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus) dataset, Human Protein Atlas (HPA), and several bioinformatic tools.ResultsAs part of our analysis, we have assessed TWF1 expression across tumors. We found that over-expression of TWF1 generally predicted poor OS for patients with tumors with high TWF1 expression, such as mesothelioma, lung adenocarcinoma, cervical cancer and pancreatic adenocarcinoma. We also assessed the mutation burden of TWF1 in cancer and the TWF1-associated survival of cancer patients, compared the phosphorylation of TWF1 between normal and primary tumor tissues and explored putative functional mechanisms in TWF1-mediated oncogenesis.ConclusionsOur pan-cancer analysis provides a comprehensive overview of the oncogenic roles of TWF1 in multiple human cancers.

Bioinformatics Analysis of a Prognostic miRNA Signature and Potential Key Genes in Pancreatic Cancer.

BackgroundIn this study, miRNAs and their critical target genes related to the prognosis of pancreatic cancer were screened based on bioinformatics analysis to provide targets for the prognosis and treatment of pancreatic cancer.MethodsR software was used to screen differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. A miRNA Cox proportional hazards regression model was constructed based on the miRNAs, and a miRNA prognostic model was generated. The target genes of the prognostic miRNAs were predicted using TargetScan and miRDB and then intersected with the DEGs to obtain common genes. The functions of the common genes were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. A protein-protein interaction (PPI) network of the common genes was constructed with the STRING database and visualized with Cytoscape software. Key genes were also screened with the MCODE and cytoHubba plug-ins of Cytoscape. Finally, a prognostic model formed by the key gene was also established to help evaluate the reliability of this screening process.ResultsA prognostic model containing four downregulated miRNAs (hsa-mir-424, hsa-mir-3613, hsa-mir-4772 and hsa-mir-126) related to the prognosis of pancreatic cancer was constructed. A total of 118 common genes were enriched in two KEGG pathways and 33 GO functional annotations, including extracellular matrix (ECM)-receptor interaction and cell adhesion. Nine key genes related to pancreatic cancer were also obtained: MMP14, ITGA2, THBS2, COL1A1, COL3A1, COL11A1, COL6A3, COL12A1 and COL5A2. The prognostic model formed by nine key genes also possessed good prognostic ability.ConclusionsThe prognostic model consisting of four miRNAs can reliably predict the prognosis of patients with pancreatic cancer. In addition, the screened nine key genes, which can also form a reliable prognostic model, are significantly related to the occurrence and development of pancreatic cancer. Among them, one novel miRNA (hsa-mir-4772) and two novel genes (COL12A1 and COL5A2) associated with pancreatic cancer have great potential to be used as prognostic factors and therapeutic targets for this tumor.

Screening and Identifying m6A Regulators as an Independent Prognostic Biomarker in Pancreatic Cancer Based on The Cancer Genome Atlas Database.

Background Pancreatic cancer is one of the most malignant tumors of the digestive system, and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas (TCGA) portal. Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Additionally, we validated the prognostic value of m6A regulators in the International Cancer Genome Consortium (ICGC). Results The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways in the TCGA database. Conclusion Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

Comprehensive analysis of clinical prognosis and molecular immune characterization of tropomyosin 4 in pancreatic cancer.

Pancreatic cancer (PC) is one of the most lethal human solid malignancies with devastating prognosis, making biomarker detection considerably important. Immune infiltrates in microenvironment is associated with patients' survival in PC. The role of Tropomyosin 4 (TPM4) gene in PC has not been reported. Our study first identifies TPM4 expression and its potential biological functions in PC. The potential oncogenic roles of TPM4 was examined using the datasets of TCGA (The cancer genome atlas) and GEO (Gene expression omnibus). We investigated the clinical significance and prognostic value of TPM4 gene based on The Gene Expression Profiling Interactive Analysis (GEPIA) and survival analysis. TIMER and TISIDB databases were used to analyze the correlations between TPM4 gene and tumor-infiltrating immune cells. We found that the expression level of TPM4 was upregulated in PC malignant tissues with the corresponding normal tissues as controls. High TPM4 expression was correlated with the worse clinicopathological features and poor prognosis in PC cohorts. The positive association between TPM4 expression and tumor-infiltrating immune cells was identified in tumor microenvironment (TME). Moreover, functional enrichment analysis suggested that TPM4 might participate in cell adhesion and promote tumor cell migration. This is the first comprehensive study to disclose that TPM4 may serve as a novel prognostic biomarker associating with immune infiltrates and provide a potential therapeutic target for the treatment of PC.

The Prognostic Impact of HER2 Genetic and Protein Expression in Pancreatic Carcinoma-HER2 Protein and Gene in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal and clinically heterogeneous disease with a limited benefit from human epidermal growth factor receptor 2 (HER2)-targeted therapy. Recently, some studies have addressed the antitumoral effect of novel anti-HER2 drugs in HER2 low-expressing tumors. However, there have been few studies on the significance of low HER2 expression and genetic heterogeneity in PDAC. Using immunohistochemistry and dual-color silver-enhanced in situ hybridization based on the Trastuzumab for a gastric cancer scoring scheme, we evaluated HER2 protein expression, gene amplification, and genetic heterogeneity in three groups (HER2-neg, HER2-low, HER2-pos) of 55 patients. Among the 55 cases, 41.8% (23/55) showed HER2 expression of any intensity. HER2 amplification independent of HER2 expression was 25.5% (14/55). Patients in both these groups had a shorter overall survival than did patients in the HER2-neg group. HER2 genetic heterogeneity was identified in 37 (70.9%) of the 55 cases, mainly in HER2-neg and HER2-low groups. HER2 genetic heterogeneity significantly correlated with worse survival in the HER2-low and HER2-neg groups of PDAC. These findings support the hypothesis that low-level HER2 expression and heterogeneity have significant clinical implications in PDAC. HER2 heterogeneity might indicate the best strategies of combination therapies to prevent the development of subdominant clones with resistance potential.

MicroRNA-216a targets WT1 expression and regulates KRT7 transcription to mediate the progression of pancreatic cancer-A transcriptome analysis.

Gene expression profiling has been broadly performed in the field of cancer research. This study aims to explore the key gene regulatory network and focuses on the functions of microRNA (miR)-216a in pancreatic cancer (PC). PC datasets GSE15471, GSE16515, and GSE32676 were used to screen the differentially expressed genes (DEGs) in PC. A miRNA microarray analysis and gene oncology analysis suggested miR-216a as an important differentially expressed miRNA in PC. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that miR-216a and the DEGs are largely enriched on the phosphatidyl inositol 3-kinase/protein kinase B (PI3K/AKT) signaling pathway. miR-216a targeted Wilms Tumor 1 (WT1), while WT1 promoted transcription activity of keratin 7 (KRT7). Upregulation of miR-216a reduced proliferation and invasiveness of PC cells, while further upregulation of WT1 blocked the functions of miR-216a. Silencing of KRT7 diminished the oncogenic role of WT1. The in vitro results were reproduced in vivo. High expression of miR-216a while poor expression of WT1 indicated better prognosis of PC patients. The miR-216a/WT1/KRT7 axis influenced the activity of the PI3K/AKT pathway. To conclude, this study evidenced that miR-216a suppressed WT1 expression and blocked KRT7 transcription, which inactivated the PI3K/AKT signaling and reduced PC progression.

Aberrantly DNA Methylated-Differentially Expressed Genes in Pancreatic Cancer Through an Integrated Bioinformatics Approach.

Pancreatic cancer remains one of the chief contributors to cancer related deaths on a global scale, with its diagnosis often associated with poor prognosis and high mortality. Accumulating literature continues to highlight the role of aberrant DNA methylation in relation to pancreatic cancer progression. Integrated bioinformatics approaches in the characterization of methylated-differentially expressed genes (MeDEGs) in pancreatic cancer were employed to enhance our understanding of the potential underlying molecular mechanisms of this cancer. We initially identified differentially expressed genes (DEGs) between 178 pancreatic cancer samples and 4 normal samples and differentially methylated genes (DMGs) based on 185 pancreatic cancer samples as well as 10 normal samples by analyzing RNA sequencing data in the TCGA database. Eventually, 31 MeDEGs including 5 hypomethylated/upregulated genes and 26 hypermethylated/downregulated genes were identified. Univariate Cox model and Kaplan–Meier method revealed that, among 31 MeDEGs, 5 hypermethylated/downregulated genes (ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42) were correlated with poor survival of patients with pancreatic cancer. KEGG pathway enrichment analysis by GSEA 3.0 and the protein–protein interaction (PPI) network revealed that these 5 MeDEGs were enriched in numerous cancer-related pathways in addition to interacting with each other, highlighting a significant role in the development of pancreatic cancer. Taken together, the key findings of the current study demonstrate that ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42 are hypermethylated/downregulated genes in pancreatic cancer and may be associated, through their modulation of specific pathways, with unfavorable pancreatic cancer prognosis.