Key genes associated with pancreatic cancer and their association with outcomes: A bioinformatics analysis

Cancer-associated fibroblast-related prognostic signature predicts prognosis and immunotherapy response in pancreatic adenocarcinoma based on single-cell and bulk RNA-sequencing

Cancer-associated fibroblasts (CAFs) influence many aspects of pancreatic adenocarcinoma (PAAD) carcinogenesis, including tumor cell proliferation, angiogenesis, invasion, and metastasis. A six-gene prognostic signature was constructed for PAAD based on the 189 CAF marker genes identified in single-cell RNA-sequencing data. Multivariate analyses showed that the risk score was independently prognostic for survival in the TCGA (P < 0.001) and ICGC (P = 0.004) cohorts. Tumor infiltration of CD8 T (P = 0.005) cells and naïve B cells (P = 0.001) was greater in the low-risk than in the high-risk group, with infiltration of these cells negatively correlated with risk score. Moreover, the TMB score was lower in the low-risk than in the high-risk group (P = 0.0051). Importantly, patients in low-risk group had better immunotherapy responses than in the high-risk group in an independent immunotherapy cohort (IMvigor210) (P = 0.039). The CAV1 and SOD3 were highly expressed in CAFs of PAAD tissues, which revealed by immunohistochemical staining. In summary, this comprehensive analysis resulted in the development of a novel prognostic signature, which was associated with immune cell infiltration, drug sensitivity, and TMB, and could predict the prognosis and immunotherapy response of patients with PAAD.

Pharmacological inhibition and reversal of pancreatic acinar ductal metaplasia

Pancreatic acinar cells display a remarkable degree of plasticity and can dedifferentiate into ductal-like progenitor cells by a process known as acinar ductal metaplasia (ADM). ADM is believed to be one of the earliest precursor lesions toward the development of pancreatic ductal adenocarcinoma and maintaining the pancreatic acinar cell phenotype suppresses tumor formation. The effects of a novel pStat3 inhibitor (LLL12B) and the histone deacetylase (HDAC) inhibitor trichostatin A (TSA) were investigated using 3-D cultures from p48Cre/+ and p48Cre/+LSL-KrasG12D/+ (KC) mice. LLL12B and TSA inhibited ADM in both KC and p48Cre/+ mouse pancreatic organoids. Furthermore, treatment with LLL12B or TSA on dedifferentiated acini from p48Cre/+ and KC mice that had undergone ADM produced morphologic and gene expression changes that suggest a reversal of ADM. Validation experiments using qRT-PCR (p48Cre/+ and KC) and RNA sequencing (KC) of the LLL12B and TSA treated cultures showed that the ADM reversal was more robust for the TSA treatments. Pathway analysis showed that TSA inhibited Spink1 and PI3K/AKT signaling during ADM reversal. The ability of TSA to reverse ADM was also observed in primary human acinar cultures. We report that pStat3 and HDAC inhibition can attenuate ADM in vitro and reverse ADM in the context of wild-type Kras. Our findings suggest that pharmacological inhibition or reversal of pancreatic ADM represents a potential therapeutic strategy for blocking aberrant ductal reprogramming of acinar cells.

Exome Sequencing Reveals Genetic Variability and Identifies Chronic Prognostic Loci in Chinese Sarcoidosis Patients

Background

Sarcoidosis is an inflammatory disease characterized by non-caseating granuloma formation in various organs, with several recognized genetic and environmental risk factors. Despite substantial progress, the genetic determinants associated with its prognosis remain largely unknown.

Objectives

This study aimed to identify the genetic changes involved in sarcoidosis and evaluate their clinical relevance.

Methods

We performed whole-exome sequencing (WES) in 116 sporadic sarcoidosis patients (acute sarcoidosis patients, n=58; chronic sarcoidosis patients, n=58). In addition, 208 healthy controls were selected from 1000 G East Asian population data. To identify genes enriched in sarcoidosis, Fisher exact tests were performed. The identified genes were included for further pathway analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, we used the STRING database to construct a protein network of rare variants and Cytoscape to identify hub genes of signaling pathways.

Results

WES and Fisher’s exact test identified 1,311 variants in 439 protein-coding genes. A total of 135 single nucleotide polymorphisms (SNPs) on 30 protein-coding genes involved in the immunological process based on the GO and KEGG enrichment analysis. Pathway enrichment analysis showed osteoclast differentiation and cytokine–cytokine receptor interactions. Three missense mutations (rs76740888, rs149664918, and rs78251590) in two genes (PRSS3 and CNN2) of immune-related genes showed significantly different mutation frequencies between the disease group and healthy controls. The correlation of genetic abnormalities with clinical outcomes using multivariate analysis of the clinical features and mutation loci showed that the missense variant (rs76740888, Chr9:33796673 G>A) of PRSS3 [p=0.04, odds ratio (OR) = 2.49] was significantly associated with chronic disease prognosis. Additionally, the top two hub genes were CCL4 and CXCR4 based on protein–protein interaction (PPI) network analysis.

Conclusion

Our study provides new insights into the molecular pathogenesis of sarcoidosis and identifies novel genetic alterations in this disease, especially PRSS3, which may be promising targets for future therapeutic strategies for chronic sarcoidosis.

Integrated Analysis of ECT2 and COL17A1 as Potential Biomarkers for Pancreatic Cancer

Background

Pancreatic cancer (PC) is a malignant tumor of the digestive tract. It presents with atypical clinical symptoms and lacks specific diagnostic indicators. This study is aimed at exploring the potential biomarkers of PC.

Methods

TCGA database pancreatic cancer dataset was normalized and used to identify differentially expressed genes (DEGs). Survival, independent prognostic, and clinical correlation analyses were performed on DEGs to screen for key genes. DNA methylation, mutation, and copy number variation (CNV) analyses were used to analyze genetic variants in key genes. GSEA was performed to explore the functional enrichment of the key genes. Based on the expression of key genes, construction of a competing endogenous RNA (ceRNA) network, analysis of the tumor microenvironment (TME), and prediction of chemotherapeutic drug sensitivity were performed. Furthermore, the GEO database was used to validate the reliability of key genes.

Results

Two key genes (ECT2 and COL17A1) were identified, which were highly expressed in PC. The mRNA expression of ECT2 and COL17A1 was associated with DNA methylation and CNV. The cell cycle, proteasome, and pathways in cancer were enriched in the high-COL17A1 and ECT2 groups. The TME results showed that immune scores were decreased in the high-ECT2 group. CeRNA network results showed that there were eleven miRNAs were involved in the regulation of ECT2 and COL17A1. Moreover, pRRophetic analysis showed that 20 chemotherapeutic drugs were associated with ECT2 and COL17A1 expression.

Conclusions

Collectively, ECT2 and COL17A1 may be potential biomarkers for PC, providing a new direction for clinical diagnosis and treatment.

Specificity protein 1-activated bone marrow stromal cell antigen 2 accelerates pancreatic cancer cell proliferation and migration

Bone marrow stromal cell antigen 2 (BST2) has been reported to act as an oncogene in the tumorigenesis of numerous types of cancer. Bioinformatics analysis has predicted the binding interaction between BST2 and specificity protein 1 (SP1) and the involvement of SP1 in pancreatic cancer. Therefore, the present study set out to verify this interaction and determine how it may affect pancreatic cancer progression. Normal human pancreatic duct epithelial cells (HPDE6-C7) and pancreatic cancer cell lines (SW1990, BxPC3, PANC1 and PSN-1) were selected for western blotting and reverse transcription-quantitative PCR detection of BST2 expression. Colony formation, Cell Counting Kit-8 and wound healing assays were performed to detect the proliferative and migratory abilities of PANC1 cells following transfection with small interfering RNA against BST2. The expression of proliferation and migration markers were assayed using western blotting. Chromatin immunoprecipitation and luciferase reporter assays were employed to verify the bioinformatics prediction of BST2-SP1 binding. PANC1 cell proliferation and migration were analyzed following BST2 knockdown and SP1 overexpression. In comparison with HPDE6-C7 cells, all four pancreatic cancer cell lines were found to exhibit increased BST2 expression levels to varying degrees, with the highest levels observed in PANC1 cells. BST2 knockdown inhibited PANC1 cell colony formation, proliferation and migration. Additionally, SP1 was shown to bind to the BST2 promoter and could promote PANC1 cell proliferation and migration when overexpressed. However, BST2 knockdown rescued SP1 overexpression-induced PANC1 cell colony formation, proliferation and migration. In conclusion, activation of BST2 by the transcription factor SP1 was shown to accelerate pancreatic cancer cell proliferation and migration, suggesting that BST2 and SP1 may be plausible therapeutic targets in targeted therapy for pancreatic cancer.

Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics

Pancreatic cancer (PC) is a highly lethal malignancy with a 5-year survival rate of 10%. The occurrence of metastasis, among other hallmarks, is the main contributor to its poor prognosis. Consequently, the elucidation of metastatic genes involved in the aggressive nature of the disease and its poor prognosis will result in the development of new treatment modalities for improved management of PC. There is a deep interest in understanding underlying disease pathology, identifying key prognostic genes, and genes associated with metastasis. Computational approaches, which have become increasingly relevant over the last decade, are commonly used to explore such interests. This review aims to address global studies that have employed global approaches to identify prognostic and metastatic genes, while highlighting their methods and limitations. A panel of 48 prognostic genes were identified across these studies, but only five, including ANLN, ARNTL2, PLAU, TOP2A, and VCAN, were validated in multiple studies and associated with metastasis. Their association with metastasis has been further explored here, and the implications of these genes in the metastatic cascade have been interpreted.

A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer

BACKGROUND

Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer.

METHODS

Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores.

RESULTS

TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) - (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823.

CONCLUSIONS

The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

Identification of novel hub genes and lncRNAs related to the prognosis and progression of pancreatic cancer by microarray and integrated bioinformatics analysis

BACKGROUND

Pancreatic cancer (PC) is one of the most invasive and metastatic neoplasms among the fatal malignancies of the digestive system. Abnormal expression of genes and long non-coding RNAs (lncRNAs) are reportedly linked to multiple cancers. However, the lncRNA-mRNA expression profiles and their molecular mechanisms in PC progression are poorly known. This study aimed to map the hub genes and lncRNAs which might play core roles in the development of PC.

METHODS

This study used microarray expression analysis to screen for both differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between PC and matched adjacent non-tumor (AN) tissues. In order to clarify the functional classification of DEGs, we conducted GO and KEGG pathway enrichment analyses via the Enrichr database. LncRNA-mRNA co-expressed networks were also constructed to explore the probable core regulating DEGs and DElncRNAs. Subsequently, the hub genes and lncRNAs were validated via the ONCOMINE and GEPIA databases and the co-expressed networks.

RESULTS

By analyzing an mRNA-lncRNA microarray, we identified 943 mRNAs and 1,138 lncRNAs differentially expressed in PC tumors compared with the matched AN tissues. GO analysis confirmed that both up-regulated and down-regulated DEGs were enriched in multiple terms. The KEGG pathways enrichment analyses revealed that DEGs were mostly enriched in the focal adhesion and glutathione metabolism pathways, amongst others. Co-expressed networks were established to reveal the differential interactions between DEGs and DElncRNAs, and to indicate the core regulatory factors located at the core nodes of the co-expressed networks. The expression levels of potential core-regulating DEGs were validated by the GEPIA and ONCOMINE databases, and the relationship between overall survival and tumor stage and the potential core-regulating DEGs was analyzed using the GEPIA database. As a result, five genes and sixteen lncRNAs were finally considered as the hub transcripts in PC.

CONCLUSIONS

This study identified DEGs and DElncRNAs between PC tumors and matched AN tissues, and these transcripts were connected with malignant phenotypes in PC through different BPs and signaling pathways. Furthermore, five hub genes and sixteen lncRNAs were identified, which are expected to represent candidate diagnostic biomarkers or potential therapeutic targets for PC.

Cell Fitness: More Than Push-Ups

Cell competition (CC) is a feature that allows tumor cells to outcompete and eliminate adjacent cells that are deemed less fit. Studies of CC, first described in Drosophila melanogaster, reveal a diversity of underlying mechanisms. In this review, we will discuss three recent studies that expand our understanding of the molecular features governing CC. In particular, we will focus on a molecular fitness fingerprint, oncogenic pathways, and the importance of cell junction stability. A fitness fingerprint, mediated by flower (hFWE) protein isoforms, dictates that cells expressing the flower-win isoforms will outcompete adjacent flower-loss-expressing cells. The impact of the flower protein isoforms is seen in cancer progression and may have diagnostic potential. The yes-associated protein (YAP) and TAZ transcription factors, central mediators of the oncogenic Hippo pathway, elevate peritumoral fitness thereby protecting against tumor progression and provide a suppressive barrier. Similarly, COL17A1 is a key component in hemidesmosome stability, and its expression in epidermal stem cells contributes to fitness competition and aging characteristics. The contributions of these pathways to disease development and progression will help define how CC is hijacked to favor cancer growth. Understanding these features will also help frame the diagnostic and therapeutic possibilities that may place CC in the crosshairs of cancer therapeutics.

Development of a four-gene prognostic model for pancreatic cancer based on transcriptome dysregulation

We systematically developed a prognostic model for pancreatic cancer that was compatible across different transcriptomic platforms and patient cohorts. After performing quality control measures, we used seven microarray datasets and two RNA sequencing datasets to identify consistently dysregulated genes in pancreatic cancer patients. Weighted gene co-expression network analysis was performed to explore the associations between gene expression patterns and clinical features. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct a prognostic model. We tested the predictive power of the model by determining the area under the curve of the risk score for time-dependent survival. Most of the differentially expressed genes in pancreatic cancer were enriched in functions pertaining to the tumor immune microenvironment. The transcriptome profiles were found to be associated with overall survival, and four genes were identified as independent prognostic factors. A prognostic risk score was then proposed, which displayed moderate accuracy in the training and self-validation cohorts. Furthermore, patients in two independent microarray cohorts were successfully stratified into high- and low-risk prognostic groups. Thus, we constructed a reliable prognostic model for pancreatic cancer, which should be beneficial for clinical therapeutic decision-making.

Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer

Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment. Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. GSE62452 and GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset. Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4⁺ T cell infiltration. Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.