Prognostic Fifteen-Gene Signature for Early Stage Pancreatic Ductal Adenocarcinoma

Prognostic implications and characterization of tumor-associated tertiary lymphoid structures genes in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is among the most aggressive cancers, with rising incidence and limited responsiveness to immunotherapy due to its highly suppressive tumor microenvironment (TME). Tertiary lymphoid structures (TLS), ectopic formation structures of immune cells, are linked to better prognosis and improved immunotherapy responses in PDAC. Understanding TLS's role in PDAC could enhance immunotherapy effectiveness.

METHODS

This study integrated transcriptomic and clinical data from 310 PDAC patients in GEO database. We performed consensus clustering using tumor-associated TLS (TA-TLS) genes, identifying three distinct molecular subtypes. Single-sample gene set enrichment analysis (ssGSEA) was then employed to calculate a TLS score for each patient, allowing for TLS-based evaluation. Key prognostic genes were identified using an iterative LASSO method, leading to the construction of a risk assessment model, which was validated across independent cohorts. We further analyzed the TLS score using single-cell RNA sequencing (scRNA-seq), visualized key gene expression, and validated protein expression through immunohistochemistry (IHC). Additionally, we explored the effects of DNASE1L3 on cell proliferation and migration, and its immune-related functions using Gene Set Enrichment Analysis (GSEA) and multiplex cytokine analysis.

RESULTS

Consensus clustering revealed three PDAC molecular subtypes with significant differences in prognosis, TA-TLS gene expression, and TME features. The TLS score effectively stratified patients into high and low groups, correlating with survival outcomes and TME characteristics. Our risk model, validated across cohorts, reliably predicted patient outcomes. Validation studies showed lower expression of DNASE1L3 and IL33 in tumor tissues. scRNA-seq confirmed TLS score associations with immune cells. DNASE1L3 overexpression inhibited PDAC cell proliferation and migration, with cytokine analysis indicating increased immune activity.

CONCLUSIONS

This study elucidated the expression profile of TA-TLS genes in PDAC, constructed a TLS gene-based scoring system, and developed a related risk model. We also explored the functions and potential antitumor mechanisms of key genes, providing evidence and new insights for enhancing TLS-targeted immunotherapy strategies in PDAC.

LPA released from dying cancer cells after chemotherapy inactivates Hippo signaling and promotes pancreatic cancer cell repopulation

PURPOSE

The Hippo pathway in the tumorigenesis and progression of PDAC, with lysophosphatidic acid (LPA) regulating the Hippo pathway to facilitate cancer progression. However, the impact of the Hippo signaling pathway on tumor repopulation in PDAC remains unreported.

METHODS

Direct and indirect co-culture models to investigate gemcitabine-induced apoptotic cells can facilitate the repopulation of residual tumor cells. Mass spectrometry analysis was conducted to assess the impact of gemcitabine treatment on the lipid metabolism of pancreatic cancer cells. ELISA assays confirmed gemcitabine promotes the release of LPA from apoptotic pancreatic cancer cells. The expression of Yes-associated protein 1 (YAP1) elucidated the underlying mechanism by which dying cells induce tumor repopulation using qRT-PCR and Western blot. We studied the biological function of pancreatic cancer cells using CCK-8, colony formation, and transwell invasion assays in vitro. Co-culture models were used to validate the impact of Hippo pathway on tumor repopulation, while flow cytometry was employed to assess the sensitivity of pancreatic cancer cells to gemcitabine in the context of Hippo pathway.

RESULTS

Gemcitabine-induced dying cells released LPA in a dose-dependent manner, which promoted the proliferation, clonal formation, and invasion of pancreatic cancer cells. Mechanistic studies showed that gemcitabine and LPA facilitated the translocation of YAP1 and induced the inactivation of the Hippo pathway. YAP1 overexpression significantly enhanced the activity of autotaxin, leading to stimulated pancreatic cancer cells to secrete LPA. This mechanism orchestrated a self-sustaining LPA-Hippo feedback loop, which drove the repopulation of residual tumor cells. Simultaneously, it was observed that suppressing LPA and YAP1 expression enhanced the sensitivity of pancreatic cancer cells to gemcitabine.

CONCLUSION

Our investigation indicated that targeting the LPA-YAP1 signaling pathway could serve as a promising strategy to augment the overall therapeutic efficacy against PDAC.

A generative deep neural network for pan-digestive tract cancer survival analysis

BACKGROUND

The accurate identification of molecular subtypes in digestive tract cancer (DTC) is crucial for making informed treatment decisions and selecting potential biomarkers. With the rapid advancement of artificial intelligence, various machine learning algorithms have been successfully applied in this field. However, the complexity and high dimensionality of the data features may lead to overlapping and ambiguous subtypes during clustering.

RESULTS

In this study, we propose GDEC, a multi-task generative deep neural network designed for precise digestive tract cancer subtyping. The network optimization process involves employing an integrated loss function consisting of two modules: the generative-adversarial module facilitates spatial data distribution understanding for extracting high-quality information, while the clustering module aids in identifying disease subtypes. The experiments conducted on digestive tract cancer datasets demonstrate that GDEC exhibits exceptional performance compared to other advanced methodologies and can separate different cancer molecular subtypes that possess both statistical and biological significance. Subsequently, 21 hub genes related to pan-DTC heterogeneity and prognosis were identified based on the subtypes clustered by GDEC. The following drug analysis suggested Dasatinib and YM155 as potential therapeutic agents for improving the prognosis of patients in pan-DTC immunotherapy, thereby contributing to the enhancement of cancer patient survival.

CONCLUSIONS

The experiment indicate that GDEC outperforms better than other deep-learning-based methods, and the interpretable algorithm can select biologically significant genes and potential drugs for DTC treatment.

Revealing the role of cancer-associated fibroblast senescence in prognosis and immune landscape in pancreatic cancer

Cancer-associated fibroblasts (CAFs) represent a major contributor to tumor growth. Cellular senescence is a state of cell-cycle arrest characterized by a pro-inflammatory phenotype. The potential impact of CAF senescence on tumor progression and the tumor microenvironment (TME) remains to be elucidated. Here, we systematically investigated the relationship between CAF senescence and the TME of pancreatic ductal adenocarcinoma (PDAC) based on multi-omics analysis and functional experiments. CAF senescence promotes tumor progression in vitro and in vivo and contributes to the formation of immunosuppressive TME. A CAF-senescence-related risk score was developed to predict overall survival, immune landscape, and treatment sensitivity in patients with PDAC. Further experiments revealed that plasminogen activator urokinase (PLAU) derived from senescent CAFs (SCAFs) promoted PDAC progression and was involved in immunosuppression. Together, these findings suggested that CAF senescence was correlated with tumor progression, and the CAF-senescence-based machine learning model could potentially predict prognosis in patients with PDAC.

DAMPs prognostic signature predicts tumor immunotherapy, and identifies immunosuppressive mechanism of pannexin 1 channels in pancreatic ductal adenocarcinoma

Background

Damage-associated molecular patterns (DAMPs) induced by immunogenic cell death (ICD) may be useful for the immunotherapy to patients undergoing pancreatic ductal adenocarcinoma (PDAC). The aim of this study is to predict the prognosis and immunotherapy responsiveness of PDAC patients using DAMPs-related genes.

Methods

K-means analysis was used to identify the DAMPs-related subtypes of 175 PDAC cases. The significance of gene mutation and immune status in different subtypes was detected. LASSO regression was used to construct a DAMPs-related prognostic signature to predict the immunotherapy responsiveness of PDAC. Subsequently, in vivo and in vitro experiments and Bulk-RNA seq were used to verify the effect of hub gene pannexin 1 (PANX1) on PDAC.

Results

Two subtypes were clustered based on the expression levels of DAMPs genes from 175 PDAC patients. Besides, the prognosis and immune landscape in up-regulated DAMPs expression subtypes was poor. In addition, we constructed a DAMPs-related prognostic signature that correlated with immune cell infiltration and predicted immunotherapy or chemotherapy responsiveness of patients with PDAC. Mechanically, through Bulk-RNA sequencing and experiments, we found that PANX1 promoted tumor progression and immune regulation via the ATP release to active NOD1/NFκB signaling pathway in PDAC.

Conclusion

Our in silico analyses established a classification system based on ICD-related DAMPs genes in PDAC, and constructed a DAMPs-related prognostic model to predict the efficacy of immunotherapy. This study will provide a new perspective for targeting the DAMPs-related molecule PANX1 in the treatment of PDAC.

Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer

As one of the most destructive and invasive cancers, pancreatic cancer exhibits complex tumor heterogeneity, which has been a major challenge for clinicians in terms of patient treatment and prognosis. The toll-like receptor (TLR) pathway is closely related to the immune microenvironment within various cancer tissues. To explore the development pattern of pancreatic cancer and find an ideal biomarker, our research has explored the mechanism of the TLR pathway in pancreatic cancer. We collected single-cell expression data from 57,024 cells and transcriptomic data from 945 pancreatic cancer patients, and conducted a series of analyses at both the single-cell and transcriptomic levels. By calculating the TLR pathway score, we clustered pancreatic cancer patients and conducted a series of analyses including metabolic pathways, immune microenvironment, drug sensitivity and so on. In the process of building prognostic models, we screened 33 core genes related to the prognosis of pancreatic cancer, and combined a series of machine learning algorithms to build the prognosis model of pancreatic cancer. We used single cell sequencing to clarify the complex intrinsic relationship between TLR pathway and pancreatic cancer. The strongest TLR signals were observed in macrophages and endothelial cells. With the occurrence of pancreatic cancer, the TLR signal of various cell types gradually increased, but with the increase of the malignant degree of ductal epithelial cells, the TLR signal gradually weakened. Cluster analysis showed that patients with the most active TLR pathway had severe dysregulation of immune microenvironment and the worst prognosis. Finally, we combined a series of machine learning algorithms to build a pancreatic cancer prognosis model that includes four genes (NT5E, TGFBI, ANLN, and FAM83A). The model showed strong performance in predicting the survival state of pancreatic cancer samples. We explored the important role of TLR pathway in pancreatic cancer and established and validated a new prognosis model for pancreatic cancer based on TLR-related genes.

Molecular Pathway and Immune Profile Analysis of IPMN-Derived Versus PanIN-Derived Pancreatic Ductal Adenocarcinomas

Intraductal papillary mucinous neoplasms (IPMN) are commonly detected pancreatic cysts that may transform into pancreatic ductal adenocarcinoma (PDAC). Predicting which IPMNs will progress to PDAC remains a clinical challenge. Moreover, identifying those clinically evident IPMNs for which a surveillance approach is best is a dire clinical need. Therefore, we aimed to identify molecular signatures that distinguished between PDAC with and without clinical evidence of an IPMN to identify novel molecular pathways related to IPMN-derived PDAC that could help guide biomarker development. Data from the Oncology Research Information Exchange Network (ORIEN) multi-institute sequencing project were utilized to analyze 66 PDAC cases from Moffitt Cancer Center and The Ohio State University Wexner Medical Center, for which tumor whole transcriptome sequencing datasets were generated. Cases were classified based on whether a tumor had originated from an IPMN (n = 16) or presumably through the pancreatic intraepithelial neoplasia (PanIN) pathway (n = 50). We then performed differential expression and pathway analysis using Gene-Set Enrichment Analysis (GSEA) and Pathway Analysis with Down-weighted Genes (PADOG) algorithms. We also analyzed immune profiles using the Tumor-Immune Microenvironment Deconvolution web portal for Bulk Transcriptomics (TIMEx). Both GSEA and TIMEx indicate that PanIN-derived PDAC tumors enrich inflammatory pathways (complement, hedgehog signaling, coagulation, inflammatory response, apical surface, IL-2/STAT5, IL-6/STAT3, EMT, KRAS signaling, apical junction, IFN-gamma, allograft rejection) and are comparatively richer in almost all immune cell types than those from IPMN-derived PDAC. IPMN-derived tumors were enriched for metabolic and energy-generating pathways (oxidative phosphorylation, unfolded protein response, pancreas beta cells, adipogenesis, fatty acid metabolism, protein secretion), and the most significantly upregulated genes (padj < 0.001) included mucin 2 (MUC2) and gastrokine-2 (GKN2). Further, the metabolic-linked gene signature enriched in the IPMN-derived samples is associated with a cluster of early-stage and long-survival (top 4th quartile) PDAC cases from The Cancer Genome Atlas (TCGA) expression database. Our data suggest that IPMN-derived and PanIN-derived PDACs differ in the expression of immune profiles and metabolic pathways. These initial findings warrant validation and follow-up to develop biomarker-based strategies for early PDAC detection and treatment.

Construction of a prognostic model with exosome biogenesis- and release-related genes and identification of RAB27B in immune infiltration of pancreatic cancer

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and fatal disease. Exosomes are extracellular vesicles that plays a vital rule in the progression and metastasis of PDAC. However, the specific mechanism of exosome biogenesis and release in the tumorigenesis and development of pancreatic cancer remains elusive. The aim of this study is to develop novel biomarkers and construct a reliable prognostic signature to accurately stratify patients and optimize clinical decision-making.

Methods

Gene expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis, random forest analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were used to construct the risk signature. The effectiveness of the model was validated by survival point plot, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curve in training, testing and entire cohorts. Meanwhile, single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithm were utilized to assess the association of the risk signature with the immune status in the PDAC tumor microenvironment. We also performed functional enrichment, tumor mutation analysis, and DNA methylation analyses based on the risk signature. The function of the core gene was further verified by polymerase chain reaction (PCR), western blot, bicinchoninic acid (BCA), immunohistochemistry (IHC) and in vitro experiments including cell proliferation, migration, and apoptosis experiments.

Results

We constructed an exosome biogenesis- and release-related risk model which could serve as an effective and independent prognosis predictor for PDAC patients. The immune infiltration analysis revealed that our signature was related to the PDAC immune microenvironment, mainly associated with a lower proportion of natural killer (NK) cells and CD8+ T cells. Tissue microarray IHC confirmed the association of RAB27B with poor prognosis in PDAC. Knockdown of RAB27B expression promoted PDAC cells' apoptosis, while decreased cellular proliferation and migration. Also, knockdown of RAB27B expression led to reduced exosome secretion, while RAB27B overexpression promoted exosome secretion.

Conclusions

The predictive signature can predict overall survival, help elucidate the mechanism of exosome biogenesis and release, and provide immunotherapy guidance for PDAC patients.

Prognostic and therapeutic value of the Eph/Ephrin signaling pathway in pancreatic cancer explored based on bioinformatics

Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract and has a very high mortality rate worldwide. Different PC patients may respond differently to therapy and develop therapeutic resistance due to the complexity and variety of the tumor microenvironment. The Eph/ephrin signaling pathway is extensively involved in tumor-related biological functions. However, the key function of the Eph/ephrin signaling pathway in PC has not been fully elucidated. We first explored a pan-cancer overview of Eph/ephrin signaling pathway genes (EPGs). Then we grouped the PC patients into 3 subgroups based on EPG expression levels. Significantly different prognoses and tumor immune microenvironments between different subtypes further validate Eph/ephrin's important role in the pathophysiology of PC. Additionally, we estimated the IC50 values for several commonly used molecularly targeted drugs used to treat PC in the three clusters, which could help patients receive a more personalized treatment plan. Following a progressive screening of optimal genes, we established a prognostic signature and validated it in internal and external test sets. The receiver operating characteristic (ROC) curves of our model exhibited great predictive performance. Meanwhile, we further validated the results through qRT-PCR and immunohistochemistry. Overall, this research provides fresh clues on the prognosis and therapy of PC as well as the theoretical groundwork for future Eph/ephrin signaling pathway research.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

An E3 ubiquitin-proteasome gene signature for predicting prognosis in patients with pancreatic cancer

Pancreatic cancer is the seventh leading cause of cancer death worldwide, which is demonstrated with remarkable resistance to radiotherapy and chemotherapy. The identification of prognosis signature and novel prognostic markers will facilitate patient stratification and an individualized precision therapy strategy. In this study, TCGA-PAAD was used to screen prognostic E3 ubiquitin ligases and establish prognostic signatures, and GEO database was used to verify the accuracy of prognostic signatures. Functional analysis, in vitro experiments and clinical cohort studies were used to analyze the function and prognostic efficacy of the target gene. An E3 ligase-based signature of 9 genes and the nomogram were developed, and the signature was proved to accurately predict the prognosis of patients with pancreatic cancer. WDR37 might be the most prognostic E3 ubiquitin ligase in pancreatic cancer, and the clinical cohort analyses suggested a tumor-suppressive role. The results of functional analysis and in vitro experiments indicated that WDR37 may promote the degradation of TCP1 complex to inhibit tumor and improve immune cell infiltration. The E3 ligase-based signature accurately predicted the prognosis of patients with pancreatic cancer, so it can be used as a decision-making tool to guide the treatment of patients with pancreatic cancer. At the same time, WDR37, the main gene in E3PMP signature, can be used as the most prognostic E3 ubiquitin ligase in the treatment of pancreatic cancer.

Platelet-related gene risk score: a predictor for pancreatic cancer microenvironmental signature, chemosensitivity and prognosis

Recent studies have indicated that platelets may play a role in the advancement of pancreatic cancer by supporting tumor growth and increasing resistance to chemotherapy. This study aims to develop a prognostic model for pancreatic cancer using a platelet-related gene risk score. Prognostic platelet-related genes (PRGs) were identified from public databases and analyzed using cluster analysis. We investigated the microenvironment signatures and gene mutation patterns across different PRG-based molecular subtypes of pancreatic cancer. A prognostic model based on PRGs was developed using LASSO-Cox Regression Analysis. Additionally, we examined the correlation between the risk score and tumor clinical characteristics, as well as drug sensitivity. Two molecular subtypes, cluster C1 and C2, were identified. Cluster C2 was associated with a poorer prognosis compared to Cluster C1. The C1 group exhibited higher scores for activated CD8+ T cells, central memory CD4+ T cells, and natural killer T cells. The C2 group demonstrated a higher frequency of gene mutations. We established and validated a novel prognostic prediction model and platelet-related gene risk score for pancreatic cancer. The risk score was positively correlated with T stage, N stage, and tumor grade, and it presented a significant prognostic value compared to other clinical factors. In conclusion, a novel prognostic prediction model focusing on platelet involvement in pancreatic cancer has been developed, offering potential benefits for future drug therapies and clinical prognostic assessments.

Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis

OBJECTIVE

The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD).

METHODS

The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication. Prognostic EALMRGs were screened through univariate COX regression analysis, and a prognostic model was constructed based on these genes by least absolute shrinkage and selection operator (LASSO) regression. The prognostic value of EALMRGs was then validated in pan-cancer data. The time characteristics ROC curve analysis was performed to evaluate the effectiveness of the prognostic genes.

RESULTS

We identified 5 hub genes (ABCB1, CAP1, EGFR, PPARG, SNCA) according to high and low-risk groups of prognoses. The risk formula was verified in three other cohort of pancreatic cancer patients and was explored in pan-cancer data. Additionally, T cell and dendritic cell infiltration was significantly increased in low-risk group. The expression of the 5 hub genes was also identified in single-cell sequencing data of pancreatic cancer with pivotal pathways. Additionally, functional enrichment analysis based on pancreatic cancer data in pancreatic cancer showed that protein serine/threonine kinase activity, focal adhesion, actin binding, cell-substrate junction, organic acid transport, and regulation of transporter activity were significant related to the expression of genes in EALMRGs.

CONCLUSIONS

Our risk formula shows potential prognostic value in multiple cancers and manifest pivotal alterations in immune infiltration and biological pathway in pancreatic cancer.

Integrated single-cell and bulk RNA sequencing revealed the molecular characteristics and prognostic roles of neutrophils in pancreatic cancer

Pancreatic cancer, one of the most prevalent tumors of the digestive system, has a dismal prognosis. Cancer of the pancreas is distinguished by an inflammatory tumor microenvironment rich in fibroblasts and different immune cells. Neutrophils are important immune cells that infiltrate the microenvironment of pancreatic cancer tumors. The purpose of this work was to examine the complex mechanism by which neutrophils influence the carcinogenesis and development of pancreatic cancer and to construct a survival prediction model based on neutrophil marker genes. We incorporated the GSE111672 dataset, comprising RNA expression data from 27,000 cells obtained from 3 patients with PC, and conducted single-cell data analysis. Thorough investigation of pancreatic cancer single-cell RNA sequencing data found 350 neutrophil marker genes. Using The Cancer Genome Atlas (TCGA), GSE28735, GSE62452, GSE57495, and GSE85916 datasets to gather pancreatic cancer tissue transcriptome data, and consistent clustering was used to identify two categories for analyzing the influence of neutrophils on pancreatic cancer. Using the Random Forest algorithm and Cox regression analysis, a survival prediction model for pancreatic cancer was developed, the model showed independent performance for survival prognosis, clinic pathological features, immune infiltration, and drug sensitivity. Multivariate Cox analysis findings revealed that the risk scores derived from predictive models is independent prognostic markers for pancreatic patients. In conclusion, based on neutrophil marker genes, this research created a molecular typing and prognostic grading system for pancreatic cancer, this system was very accurate in predicting the prognosis, tumor immune microenvironment status, and pharmacological treatment responsiveness of pancreatic cancer patients.

Characterization of anoikis-based molecular heterogeneity in pancreatic cancer and pancreatic neuroendocrine tumor and its association with tumor immune microenvironment and metabolic remodeling

Background

Accumulating evidence suggests that anoikis plays a crucial role in the onset and progression of pancreatic cancer (PC) and pancreatic neuroendocrine tumors (PNETs); nevertheless, the prognostic value and molecular characteristics of anoikis in cancers are yet to be determined.

Materials and methods

We gathered and collated the multi-omics data of several human malignancies using the TCGA pan-cancer cohorts. We thoroughly investigated the genomics and transcriptomics features of anoikis in pan-cancer. We then categorized a total of 930 patients with PC and 226 patients with PNETs into distinct clusters based on the anoikis scores computed through single-sample gene set enrichment analysis. We then delved deeper into the variations in drug sensitivity and immunological microenvironment between the various clusters. We constructed and validated a prognostic model founded on anoikis-related genes (ARGs). Finally, we conducted PCR experiments to explore and verify the expression levels of the model genes.

Results

Initially, we identified 40 differentially expressed anoikis-related genes (DE-ARGs) between pancreatic cancer (PC) and adjacent normal tissues based on the TCGA, GSE28735, and GSE62452 datasets. We systematically explored the pan-cancer landscape of DE-ARGs. Most DE-ARGs also displayed differential expression trends in various tumors, which were strongly linked to favorable or unfavorable prognoses of patients with cancer, especially PC. Cluster analysis successfully identified three anoikis-associated subtypes for PC patients and two anoikis-associated subtypes for PNETs patients. The C1 subtype of PC patients showed a higher anoikis score, poorer prognosis, elevated expression of oncogenes, and lower level of immune cell infiltration, whereas the C2 subtype of PC patients had the exact opposite characteristics. We developed and validated a novel and accurate prognostic model for PC patients based on the expression traits of 13 DE-ARGs. In both training and test cohorts, the low-risk subpopulations had significantly longer overall survival than the high-risk subpopulations. Dysregulation of the tumor immune microenvironment could be responsible for the differences in clinical outcomes between low- and high-risk groups.

Conclusions

These findings provide fresh insights into the significance of anoikis in PC and PNETs. The identification of subtypes and construction of models have accelerated the progress of precision oncology.

Determination and characterization of molecular heterogeneity and precision medicine strategies of patients with pancreatic cancer and pancreatic neuroendocrine tumor based on oxidative stress and mitochondrial dysfunction-related genes

Background

Mitochondria are significant both for cellular energy production and reactive oxygen/nitrogen species formation. However, the significant functions of mitochondrial genes related to oxidative stress (MTGs-OS) in pancreatic cancer (PC) and pancreatic neuroendocrine tumor (PNET) are yet to be investigated integrally. Therefore, in pan-cancer, particularly PC and PNET, a thorough assessment of the MTGs-OS is required.

Methods

Expression patterns, prognostic significance, mutation data, methylation rates, and pathway-regulation interactions were studied to comprehensively elucidate the involvement of MTGs-OS in pan-cancer. Next, we separated the 930 PC and 226 PNET patients into 3 clusters according to MTGs-OS expression and MTGs-OS scores. LASSO regression analysis was utilized to construct a novel prognostic model for PC. qRT-PCR(Quantitative real-time PCR) experiments were performed to verify the expression levels of model genes.

Results

The subtype associated with the poorest prognosis and lowerest MTGs-OS scores was Cluster 3, which could demonstrate the vital function of MTGs-OS for the pathophysiological processes of PC. The three clusters displayed distinct variations in the expression of conventional cancer-associated genes and the infiltration of immune cells. Similar molecular heterogeneity was observed in patients with PNET. PNET patients with S1 and S2 subtypes also showed distinct MTGs-OS scores. Given the important function of MTGs-OS in PC, a novel and robust MTGs-related prognostic signature (MTGs-RPS) was established and identified for predicting clinical outcomes for PC accurately. Patients with PC were separated into the training, internal validation, and external validation datasets at random; the expression profile of MTGs-OS was used to classify patients into high-risk (poor prognosis) or low-risk (good prognosis) categories. The variations in the tumor immune microenvironment may account for the better prognoses observed in high-risk individuals relative to low-risk ones.

Conclusions

Overall, our study for the first time identified and validated eleven MTGs-OS remarkably linked to the progression of PC and PNET, and elaborated the biological function and prognostic value of MTGs-OS. Most importantly, we established a novel protocol for the prognostic evaluation and individualized treatment for patients with PC.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

Clustering analysis revealed the autophagy classification and potential autophagy regulators' sensitivity of pancreatic cancer based on multi-omics data

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy and is unresponsive to conventional therapeutic modalities due to its high heterogeneity, expounding the necessity, and priority of searching for effective biomarkers and drugs. Autophagy, as an evolutionarily conserved biological process, is upregulated in PDAC and its regulation is linked to a poor prognosis. Increased autophagy sequestered MHC-I on PDAC cells and weaken the antigen presentation and antitumor immune response, indicating the potential therapeutic strategies of autophagy inhibitors.

METHODS

By performing 10 state-of-the-art multi-omics clustering algorithms, we constructed a robust PDAC classification model to reveal the autophagy-related genes among different subgroups.

OUTCOMES

After building a more comprehensive regulating network for potential autophagy regulators exploration, we concluded the top 20 autophagy-related hub genes (GAPDH, MAPK3, RHEB, SQSTM1, EIF2S1, RAB5A, CTSD, MAP1LC3B, RAB7A, RAB11A, FADD, CFKN2A, HSP90AB1, VEGFA, RELA, DDIT3, HSPA5, BCL2L1, BAG3, and ERBB2), six miRNAs, five transcription factors, and five immune infiltrated cells as biomarkers. The drug sensitivity database was screened based on the biomarkers to predict possible drug-targeting signal pathways, hoping to yield novel insights, and promote the progress of the anticancer therapeutic strategy.

CONCLUSION

We succefully constructed an autophagy-related mRNA/miRNA/TF/Immune cells network based on a 10 state-of art algorithm multi-omics analysis, and screened the drug sensitivity dataset for detecting potential signal pathway which might be possible autophagy modulators' targets.

Immune response and drug therapy based on ac4C-modified gene in pancreatic cancer typing

N-4 cytidine acetylation (ac4C) is an epitranscriptome modification catalyzed by N-acetyltransferase 10 (NAT10) and is essential for cellular mRNA stability, rRNA biosynthesis, cell proliferation, and epithelial-mesenchymal transition (EMT). Numerous studies have confirmed the inextricable link between NAT10 and the clinical characteristics of malignancies. It is unclear, however, how NAT10 might affect pancreatic ductal adenocarcinoma. We downloaded pancreatic ductal adenocarcinoma patients from the TCGA database. We obtained the corresponding clinical data for data analysis, model construction, differential gene expression analysis, and the GEO database for external validation. We screened the published papers for NAT10-mediated ac4C modifications in 2156 genes. We confirmed that the expression levels and genomic mutation rates of NAT10 differed significantly between cancer and normal tissues. Additionally, we constructed a NAT10 prognostic model and examined immune infiltration and altered biological pathways across the models. The NAT10 isoforms identified in this study can effectively predict clinical outcomes in pancreatic ductal adenocarcinoma. Furthermore, our study showed that elevated levels of NAT10 expression correlated with gemcitabine resistance, that aberrant NAT10 expression may promote the angiogenic capacity of pancreatic ductal adenocarcinoma through activation of the TGF-β pathway, which in turn promotes distal metastasis of pancreatic ductal adenocarcinoma, and that NAT10 knockdown significantly inhibited the migration and clonogenic capacity of pancreatic ductal adenocarcinoma cells. In conclusion, we proposed a predictive model based on NAT10 expression levels, a non-invasive predictive approach for genomic profiling, which showed satisfactory and effective performance in predicting patients' survival outcomes and treatment response. Medicine and electronics will be combined in more interdisciplinary areas in the future.

Identification of pyroptosis-related gene signature for predicting prognosis of patients with pancreatic cancer using bioinformatics

Pancreatic cancer, a common digestive system malignancy, is dubbed the "king of cancers". The role of pyrophosis-related genes (PRGs) in pancreatic cancer prognosis is yet unknown. In pancreatic cancer and normal tissue, we discovered 9 PRGs that are expressed differently in pancreatic cancer and healthy tissue. Based on the differential expression of PRGs, 2 clusters of pancreatic cancer cases could be identified. The 2 groups had significant disparities in total survival time. The prognostic model of a 5-PRGs signature was created using least absolute shrinkage and selection operator (LASSO) method. The median risk score was used to split pancreatic cancer patients in The Cancer Genome Atlas (TCGA) cohort into 2 groups: low risk and high risk. Patients classified as low-risk had significantly higher survival rates than those classified as high-risk (P < .01). The same results were obtained by validating them against the Gene Expression Omnibus database (P = .030). Cox regression statistical analysis showed that risk score was an independent predictor of overall survival in pancreatic cancer patients. Functional enrichment analysis revealed that apoptosis, cell proliferation, and cell cycle-related biological processes and signaling pathways were enriched. Additionally, the immunological status of the high-risk group worsened. In conclusion, a novel pyroptosis-related gene signature can be used to predict pancreatic cancer patient prognosis.

NEUROD1 predicts better prognosis in pancreatic cancer revealed by a TILs-based prognostic signature

It has been well-defined that tumor-infiltrating lymphocytes (TILs) play critical roles in pancreatic cancer (PaCa) progression. This research aimed to comprehensively explore the composition of TILs in PaCa and their potential clinical significance. A total of 178 samples from the TCGA and 63 samples from the GSE57495 dataset were enrolled in our study. ImmuCellAI was applied to calculate the infiltrating abundance of 24 immune cell types in PaCa and further survival analysis revealed the prognostic values of TILs in PaCa. Moreover, the Hallmark enticement analysis of differentially expressed genes (DEGs) between low- and high-risk groups was performed as well. Immunohistochemistry staining was used to evaluate NEUROD1 expression. As result, different kinds of TILs had distinct infiltrating features. In addition, Specific TILs subsets had notable prognostic values in PaCa. We further established a 6-TILs signature to assess the prognosis of PaCa patients. Kaplan-Meier and Cox regression analyses both suggested the significant prognostic value of the signature in PaCa. Based on the prognostic signature, we screened a great deal of potential prognostic biomarkers and successfully validated NEUROD1 as a novel prognostic biomarker in PaCa. Overall, the current study illuminated the immune cells infiltrating the landscape in PaCa and identified a TILs-dependent signature and NEUROD1 for prognostic prediction in PaCa patients.

Single Cell RNA-Seq Identifies Immune-Related Prognostic Model and Key Signature-SPP1 in Pancreatic Ductal Adenocarcinoma

There are no reliable biomarkers for early diagnosis or prognosis evaluation in pancreatic ductal adenocarcinoma (PDAC). Multiple scRNA-seq datasets for PDAC were retrieved from online databases and combined with scRNA-seq results from our previous study. The malignant ductal cells were identified through calculating copy number variation (CNV) scores. The robust markers of malignant ductal cells in PDAC were found. Five immune-related signatures, including SPP1, LINC00683, SNHG10, LINC00237, and CASC19, were used to develop a risk score formula to predict the overall survival of PDAC patients. We also constructed an easy-to-use nomogram, combining risk score, N stage, and margin status. The expression level of SPP1 was related to the prognosis and immune regulators. We found that SPP1 was mainly expressed in ductal cells and macrophages in PDAC. In conclusion, we constructed a promising prognostic model based on immune-related signatures for PDAC using scRNA-seq and TCGA_PAAD datasets.

A universal co-expression gene network and prognostic model for hepatic-biliary-pancreatic cancers identified by integrative analyses

Hepatic, biliary and pancreatic cancers are a diverse set of malignancies with poor prognoses. It is possible that common molecular mechanisms are involved in the carcinogenesis of these cancers. Here, we identified LINC01537 and seven protein-coding genes by integrative analysis of transcriptomes of mRNAs, microRNAs and long non-coding RNAs from cholangiocarcinoma, hepatocellular carcinoma and pancreatic adenocarcinoma cohorts in TCGA. A predictive model constructed from seven biomarkers was established to successfully predict the survival rate of patients, which was then further verified in external cohorts. Additionally, patients with high-risk scores in our model were prone to epithelial-mesenchymal transition. Finally, activation of the biomarker PDE2A significantly attenuated migration and epithelial-mesenchymal transition in the HepG2 liver cancer cell line.

CD9 mediates the uptake of extracellular vesicles from cancer-associated fibroblasts that promote pancreatic cancer cell aggressiveness

In pancreatic ductal adenocarcinoma (PDAC), signaling from stromal cells is implicated in metastatic progression. Tumor-stroma cross-talk is often mediated through extracellular vesicles (EVs). We previously reported that EVs derived from cancer-associated stromal fibroblasts (CAFs) that are abundant in annexin A6 (ANXA6⁺ EVs) support tumor cell aggressiveness in PDAC. Here, we found that the cell surface glycoprotein and tetraspanin CD9 is a key component of CAF-derived ANXA6⁺ EVs for mediating this cross-talk. CD9 was abundant on the surface of ANXA6⁺ CAFs isolated from patient PDAC samples and from various mouse models of PDAC. CD9 colocalized with CAF markers in the tumor stroma, and CD9 abundance correlated with tumor stage. Blocking CD9 impaired the uptake of ANXA6⁺ EVs into cultured PDAC cells. Signaling pathway arrays and further analyses revealed that the uptake of CD9⁺ANXA6⁺ EVs induced mitogen-activated protein kinase (MAPK) pathway activity, cell migration, and epithelial-to-mesenchymal transition (EMT). Blocking either CD9 or p38 MAPK signaling impaired CD9⁺ANXA6⁺ EV-induced cell migration and EMT in PDAC cells. Analysis of bioinformatic datasets indicated that CD9 abundance was an independent marker of poor prognosis in patients with PDAC. Our findings suggest that CD9-mediated stromal cell signaling promotes PDAC progression.

Hypoxia-Related Signature Is a Prognostic Biomarker of Pancreatic Cancer

Background

Hypoxia plays a significant role in the pathogenesis of pancreatic cancer, but the effect of hypoxia-related genes in pancreatic cancer remains to be elucidated. This study aimed to identify hypoxia-related genes related to pancreatic cancer and construct a prognostic signature.

Methods

Pancreatic cancer datasets were retrieved from TCGA database. Cox regression analyses were used to identify hypoxia-related genes and construct a prognostic signature. Datasets from International Cancer Genome Consortium and GEO databases were used as validated cohorts. The CIBERSORT method was applied to estimate the fractions of immune cell types. DNA methylation and protein levels of the genes in pancreatic cancer were examined.

Results

Three hypoxia-related genes (TES, LDHA, and ANXA2) were identified as associated with patient survival and selected to construct a prognostic signature. Patients were divided into high- and low-risk groups based on the signature. Those in the high-risk group showed worse survival than those in the low-risk group. The signature was shown to be involved in the HIF-1 signaling pathway. The time-dependent ROC analyses of three independent validated cohorts further revealed that this signature had a better prognostic value in the prediction of the survival of pancreatic cancer patients. Immune cells analysis for three datasets demonstrated that high-risk signature was significantly associated with macrophages and T cells. DNA methylation and protein levels of the three genes validated their aberrant expression in pancreatic cancer.

Conclusions

Our research provided a novel and reliable prognostic signature that composes of three hypoxia-related genes to estimate the prognosis of pancreatic cancer.

Discovering a Four-Gene Prognostic Model Based on Single-Cell Data and Gene Expression Data of Pancreatic Adenocarcinoma

Background

Pancreatic cancer has a 5-year overall survival lower than 8%. Pancreatic adenocarcinoma (PAAD) is the most common type. This study attempted to explore novel molecular subtypes and a prognostic model through analyzing tumor microenvironment (TME).

Materials and Methods

Single-cell RNA sequencing (scRNA-seq) data and expression profiles from public databases were downloaded. Three PAAD samples with single-cell data and 566 samples with gene expression data were included. Seurat was used to identify cell subsets. SVA merged and removed batch effects from multichip datasets. CIBERSORT was used to evaluate the components of different cells in transcriptome, ConsensusClusterPlus was used to identify molecular subtypes, and gene set enrichment analysis was used for functional enrichment analysis. LASSO Cox was performed to construct dimensionality reduction and prognosis model.

Results

Memory B cells (MBCs) were identified to be significantly with PAAD prognosis. Two immune subtypes (IS1 and IS2) with distinct overall survival were constructed. Forty-one DEGs were identified between IS1 and IS2. Four prognostic genes (ANLN, ARNTL2, SERPINB5, and DKK1) were screened to develop a prognostic model. The model was effective in classifying samples into high-risk and low-risk groups with distinct prognosis. Three subgroups of MBCs were identified, where MBC_0 and MBC_1 were differentially distributed between IS1 and IS2, high-risk and low-risk groups.

Conclusions

MBCs were closely involved in PAAD progression, especially MBC_0 and MBC_1 subgroups. The four-gene prognostic model was predictive of overall survival and could guide immunotherapy for patients with PAAD.

BUB1 Is Identified as a Potential Therapeutic Target for Pancreatic Cancer Treatment

Pancreatic cancer is one of the most challenging cancer types in clinical treatment worldwide. This study aimed to understand the tumorigenesis mechanism and explore potential therapeutic targets for patients with pancreatic cancer. Single-cell data and expression profiles of pancreatic cancer samples and normal tissues from multiple databases were included. Comprehensive bioinformatics analyses were applied to clarify tumor microenvironment and identify key genes involved in cancer development. Immense difference of cell types was shown between tumor and normal samples. Four cell types (B cell_1, B cell_2, cancer cell_3, and CD1C+_B dendritic cell_3) were screened to be significantly associated with prognosis. Three ligand-receptor pairs, including CD74-MIF, CD74-COPA, and CD74-APP, greatly contributed to tumorigenesis. High expression of BUB1 (BUB1 Mitotic Checkpoint Serine/Threonine Kinase) was closely correlated with worse prognosis. CD1C+_B dendritic cell_3 played a key role in tumorigenesis and cancer progression possibly through CD74-MIF. BUB1 can serve as a prognostic biomarker and a therapeutic target for patients with pancreatic cancer. The study provided a novel insight into studying the molecular mechanism of pancreatic cancer development and proposed a potential strategy for exploiting new drugs.

Systematic analysis on expression quantitative trait loci identifies a novel regulatory variant in ring finger and WD repeat domain 3 associated with prognosis of pancreatic cancer

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is an extremely lethal malignancy. Identification of the functional genes and genetic variants related to PAAD prognosis is important and challenging. Previously identified prognostic genes from several expression profile analyses were inconsistent. The regulatory genetic variants that affect PAAD prognosis were largely unknown.

METHODS

Firstly, a meta-analysis was performed with seven published datasets to systematically explore the candidate prognostic genes for PAAD. Next, to identify the regulatory variants for those candidate genes, expression quantitative trait loci analysis was implemented with PAAD data resources from The Cancer Genome Atlas. Then, a two-stage association study in a total of 893 PAAD patients was conducted to interrogate the regulatory variants and find the prognostic locus. Finally, a series of biochemical experiments and phenotype assays were carried out to demonstrate the biological function of variation and genes in PAAD progression process.

RESULTS

A total of 128 genes were identified associated with the PAAD prognosis in the meta-analysis. Fourteen regulatory loci in 12 of the 128 genes were discovered, among which, only rs4887783, the functional variant in the promoter of Ring Finger and WD Repeat Domain 3 ( RFWD3 ), presented significant association with PAAD prognosis in both stages of the population study. Dual-luciferase reporter and electrophoretic mobility shift assays demonstrated that rs4887783-G allele, which predicts the worse prognosis, enhanced the binding of transcript factor REST, thus elevating RFWD3 expression. Further phenotypic assays revealed that excess expression of RFWD3 promoted tumor cell migration without affecting their proliferation rate. RFWD3 was highly expressed in PAAD and might orchestrate the genes in the DNA repair process.

CONCLUSIONS

RFWD3 and its regulatory variant are novel genetic factors for PAAD prognosis.

Pyroptosis-related genes regulate proliferation and invasion of pancreatic cancer and serve as the prognostic signature for modeling patient survival

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and poor prognosis. Pyroptosis can influence the prognosis of patients by regulating the proliferation, invasion, and metastasis of cancer cells. However, the role of pyroptosis-related genes (PRGs) in PDAC remains unclear.

METHODS

In this study, based on the Cancer Genome Atlas (TCGA) cohort of PDAC samples, univariate Cox analysis and LASSO regression analysis were used to screen the prognostic PRGs and establish the gene signature. To further evaluate the functional significance of CASP4 and NLRP1 in PDAC, we also conducted an in vitro study to explore the mechanism of CASP4 and NLRP1 regulating the occurrence and development of PDAC. Finally, we investigated the relationship between CASP4 and NLRP1 expression levels and drug sensitivity in pancreatic cancer cells.

RESULTS

A risk prediction model based on CASP4 and NLRP1 was established, which can distinguish high-risk patients from low-risk patients (P < 0.001). Both internal validation and external GEO data sets validation demonstrate good predictive capability of the model (AUC = 0.732, AUC = 0.802, AUC = 0.632, P < 0.05). In vitro, CCK8 and Transwell assay suggested that CASP4 may accelerate the progression of PDAC by promoting proliferation and migration of pancreatic cancer cells, while NLRP1 has been found to have tumor suppressive effect. It should be noted that knockdown of CASP4 reduced the level of coke death, the expression levels of acetyl-CoA carboxylase, FASN, SREBP-1 and SREBP-2 were decreased, and the number of lipid droplets was also significantly reduced. Moreover, the enrichment of signaling pathways showed that NLRP1 was significantly correlated with MAPK and RAS/ERK signaling pathways, and knocking down NLRP1 could indeed up-regulate p-ERK expression. Finally, high expression of CASP4 and low expression of NLRP1 increased the sensitivity of pancreatic cancer cells to ERK inhibitors.

CONCLUSIONS

In especial, CASP4 can promote tumor progression by promoting the synthesis and accumulation of fatty acids, while NLRP1 acts on RAS/ERK signaling pathway. Both of genes play an important role in the diagnosis and treatment of PDAC, which may also affect the inhibitors of MAPK/ERK efficiency.

Development and validation of prognostic and diagnostic model for pancreatic ductal adenocarcinoma based on scRNA-seq and bulk-seq datasets

The 5-year overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC) is only 10%, partly owing to the lack of reliable diagnostic and prognostic biomarkers. The raw gene-cell matrix for single-cell RNA-seq (scRNA-seq) analysis was downloaded from the GSA database. We drew cell atlas for PDAC and normal pancreatic tissues. The inferCNV analysis was used to distinguish tumor cells from normal ductal cells. We identified differential expression genes (DEGs) by comparing tumor cells and normal ductal cells. The common DEGs were used to conduct prognostic and diagnostic model using univariate and multivariate Cox or logistic regression analysis. Four genes, MET, KLK10, PSMB9 and ITGB6, were utilized to create risk score formula to predict OS and to establish diagnostic model for PDAC. Finally, we drew an easy-to-use nomogram to predict 2-year and 3-year OSs. In conclusion, we developed and validated the prognostic and diagnostic model for PDAC based on scRNA-seq and bulk-seq datasets.

Molecular signatures of tumor progression in pancreatic adenocarcinoma identified by energy metabolism characteristics

BACKGROUND

In this study, we performed a molecular evaluation of primary pancreatic adenocarcinoma (PAAD) based on the comprehensive analysis of energy metabolism-related gene (EMRG) expression profiles.

METHODS

Molecular subtypes were identified by nonnegative matrix clustering of 565 EMRGs. An overall survival (OS) predictive gene signature was developed and internally and externally validated based on three online PAAD datasets. Hub genes were identified in molecular subtypes by weighted gene correlation network analysis (WGCNA) coexpression algorithm analysis and considered as prognostic genes. LASSO cox regression was conducted to establish a robust prognostic gene model, a four-gene signature, which performed better in survival prediction than four previously reported models. In addition, a novel nomogram constructed by combining clinical features and the 4-gene signature showed high-confidence clinical utility. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group participate in the neuroactive ligand receptor interaction pathway.

CONCLUSIONS

In summary, EMRG-based molecular subtypes and prognostic gene models may provide a novel research direction for patient stratification and trials of targeted therapies.

Immunological Classification of Pancreatic Carcinomas to Identify Immune Index and Provide a Strategy for Patient Stratification

Background

Cancer immunotherapy has produced significant positive clinical effects in a variety of tumor types. However, pancreatic ductal adenocarcinoma (PDAC) is widely considered to be a "cold" cancer with poor immunogenicity. Our aim is to determine the detailed immune features of PDAC to seek new treatment strategies.

Methods

The immune cell abundance of PDAC patients was evaluated with the single-sample gene set enrichment analysis (ssGSEA) using 119 immune gene signatures. Based on these data, patients were classified into different immune subtypes (ISs) according to immune gene signatures. We analyzed their response patterns to immunotherapy in the datasets, then established an immune index to reflect the different degrees of immune infiltration through linear discriminant analysis (LDA). Finally, potential prognostic markers associated with the immune index were identified based on weighted correlation network analysis (WGCNA) that was functionally validated in vitro.

Results

Three ISs were identified in PDAC, of which IS3 had the best prognosis across all three cohorts. The different expressions of immune profiles among the three ISs indicated a distinct responsiveness to immunotherapies in PDAC subtypes. By calculating the immune index, we found that the IS3 represented higher immune infiltration, while IS1 represented lower immune infiltration. Among the investigated signatures, we identified ZNF185, FANCG, and CSTF2 as risk factors associated with immune index that could potentially facilitate diagnosis and could be therapeutic target markers in PDAC patients.

Conclusions

Our findings identified immunologic subtypes of PDAC with distinct prognostic implications, which allowed us to establish an immune index to represent the immune infiltration in each subtype. These results show the importance of continuing investigation of immunotherapy and will allow clinical workers to personalized treatment more effectively in PDAC patients.

Development of a Gene Risk Signature for Patients of Pancreatic Cancer

BACKGROUND

Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment.

METHODS

Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan-Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses.

RESULT

A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival.

CONCLUSION

This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.

Systematic Analysis of an Invasion-Related 3-Gene Signature and Its Validation as a Prognostic Model for Pancreatic Cancer

Background

Pancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system that is associated with a poor prognosis in patients owing to its rapid progression and high invasiveness.

Methods

Ninety-seven invasive-related genes obtained from the CancerSEA database were clustered to obtain the molecular subtype of pancreatic cancer based on the RNA-sequencing (RNA-seq) data of The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) between subtypes were obtained using the limma package in R, and the multi-gene risk model based on DEGs was constructed by Lasso regression analysis. Independent datasets GSE57495 and GSE62452 were used to validate the prognostic value of the risk model. To further explore the expression of the hub genes, immunohistochemistry was performed on PAAD tissues obtained from a large cohort.

Results

The TCGA-PAAD samples were divided into two subtypes based on the expression of the invasion-related genes: C1 and C2. Most genes were overexpressed in the C1 subtype. The C1 subtype was mainly enriched in tumor-related signaling pathways, and the prognosis of patients with the C1 subtype was significantly worse than those with the C2 subtype. A 3-gene signature consisting of LY6D, BCAT1, and ITGB6 based on 538 DEGs between both subtypes serves as a stable prognostic marker in patients with pancreatic cancer across multiple cohorts. LY6D, BCAT1, and ITGB6 were over-expressed in 120 PAAD samples compared to normal samples.

Conclusions

The constructed 3-gene signature can be used as a molecular marker to assess the prognostic risk in patients with PAAD.

Development and validation of a four-lipid metabolism gene signature for diagnosis of pancreatic cancer

Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Such abnormal lipid metabolism provides energy for rapid proliferation, and certain genes related to lipid metabolism encode important components of tumor signaling pathways. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. A risk score model was built and verified using the GSE57495 dataset and International Cancer Genome Consortium dataset. Four molecular subtypes and 4249 differentially expressed genes (DEGs) were identified. The DEGs obtained by Weighted Gene Coexpression Network Construction analysis were intersected with 4249 DEGs to obtain a total of 1340 DEGs. The final prognosis model included CA8, CEP55, GNB3 and SGSM2, and these had a significant effect on overall survival. The area under the curve at 1, 3 and 5 years was 0.72, 0.79 and 0.87, respectively. These same results were obtained using the validation cohort. Survival analysis data showed that the model could stratify the prognosis of patients with different clinical characteristics, and the model has clinical independence. Functional analysis indicated that the model is associated with multiple cancer-related pathways. Compared with published models, our model has a higher C-index and greater risk value. In summary, this four-gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator.

Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO

Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC).

A Novel Cox Proportional Hazards Model for High-Dimensional Genomic Data in Cancer Prognosis

The Cox proportional hazards model is a popular method to study the connection between feature and survival time. Because of the high-dimensionality of genomic data, existing Cox models trained on any specific dataset often generalize poorly to other independent datasets. In this paper, we suggest a novel strategy for the Cox model. This strategy is included a new learning technique, self-paced learning (SPL), and a new gene selection method, SCAD-Net penalty. The SPL method is adopted to aid to build a more accurate prediction with its built-in mechanism of learning from easy samples first and adaptively learning from hard samples. The SCAD-Net penalty has fixed the problem of the SCAD method without an inherent mechanism to fuse the prior graphical information. We combined the SPL with the SCAD-Net penalty to the Cox model (SSNC). The simulation shows that the SSNC outperforms the benchmark in terms of prediction and gene selection. The analysis of a large-scale experiment across several cancer datasets shows that the SSNC method not only results in higher prediction accuracies but also identifies markers that satisfactory stability across another validation dataset. The demo code for the proposed method is provided in supplemental file.

G2M checkpoint pathway alone is associated with drug response and survival among cell proliferation-related pathways in pancreatic cancer

Given the severe side effects of the treatments and poor survival, prognostic and predictive biomarkers to guide management of pancreatic cancer are in critical need. We hypothesized that cell proliferation-related pathways are associated with drug response and survival in pancreatic cancer. Six Hallmark cell proliferation-related gene sets (G2M Checkpoint, E2F Targets, MYC Targets V1 and V2, Mitotic Spindle, p53 pathway) defined by MSigDB in gene set variant analysis were evaluated in 3 independent cohorts- TCGA-PAAD (n = 176), GSE57495 (n = 63), and GSE62452 (n = 69). G2M and E2F, as well as Mitotic and p53 pathway correlated highly with other gene sets. All pathways were significantly correlated with MKI67 expression and its proliferation score, but none with cytolytic activity and the rate of pathologically complete resection (R0). All pathways were significantly associated with high alteration and expression of KRAS gene except for MYC v1. G2M, E2F, and p53 pathway were significantly associated with high alteration of TP53 gene. Interestingly, different pathways correlated with the AUC of different cancer therapeutics, such as Gemcitabine (Mitotic: r = 0.706 [P = 0.01]), Paclitaxel (MYC v2: r = -0.636 [P < 0.05]), Apatinib (Mitotic: r = -0.556 [P = 0.03]), Palbociclib (E2F: r = 0.675 [P < 0.01]), and Sorafenib (G2M: r = -0.593 [P = 0.03]). Among all six pathways, only G2M was consistently associated with worse patient survival in all three cohorts. In conclusion, each cell proliferation-related pathway was predictive of a unique agent, and the G2M score alone predicts survival in pancreatic cancer.

Schlafen 5 as a novel therapeutic target in pancreatic ductal adenocarcinoma

We provide evidence that a member of the human Schlafen (SLFN) family of proteins, SLFN5, is overexpressed in human pancreatic ductal adenocarcinoma (PDAC). Targeted deletion of SLFN5 results in decreased PDAC cell proliferation and suppresses PDAC tumorigenesis in in vivo PDAC models. Importantly, high expression levels of SLFN5 correlate with worse outcomes in PDAC patients, implicating SLFN5 in the pathophysiology of PDAC that leads to poor outcomes. Our studies establish novel regulatory effects of SLFN5 on cell cycle progression through binding/blocking of the transcriptional repressor E2F7, promoting transcription of key genes that stimulate S phase progression. Together, our studies suggest an essential role for SLFN5 in PDAC and support the potential for developing new therapeutic approaches for the treatment of pancreatic cancer through SLFN5 targeting.

Seven Glycolysis-Related Genes Predict the Prognosis of Patients With Pancreatic Cancer

Objectives

To identify the key glycolysis-related genes (GRGs) in the occurrence and development of pancreatic ductal carcinoma (PDAC), and to construct a glycolysis-related gene model for predicting the prognosis of PDAC patients.

Methodology

Pancreatic ductal carcinoma (PDAC) data and that of normal individuals were downloaded from the TCGA database and Genotype-Tissue Expression database, respectively. GSEA analysis of glycolysis-related pathways was then performed on PDAC data to identify significantly enriched GRGs. The genes were combined with other patient’s clinical information and used to construct a glycolysis-related gene model using cox regression analysis. The model was further evaluated using data from the validation group. Mutations in the model genes were subsequently identified using the cBioPortal. In the same line, the expression levels of glycolysis related model genes in PDAC were analyzed and verified using immunohistochemical images. Model prediction for PDAC patients with different clinical characteristics was then done and the relationship between gene expression level, clinical stage and prognosis further discussed. Finally, a nomogram map of the predictive model was constructed to evaluate the prognosis of patients with PDAC.

Results

GSEA results of the training set revealed that genes in the training set were significantly related to glycolysis pathway and iconic glycolysis pathway. There were 108 differentially expressed GRGs. Among them, 29 GRGs were closely related to prognosis based on clinical survival time. Risk regression analysis further revealed that there were seven significantly expressed glycolysis related genes. The genes were subsequently used to construct a predictive model. The model had an AUC value of more than 0.85. It was also significantly correlated with survival time. Further expression analysis revealed that CDK1, DSC2, ERO1A, MET, PYGL, and SLC35A3 were highly expressed in PDAC and CHST12 was highly expressed in normal pancreatic tissues. These results were confirmed using immunohistochemistry images of normal and diseases cells. The model could effectively evaluate the prognosis of PDAC patients with different clinical characteristics.

Conclusion

The constructed glycolysis-related gene model effectively predicts the occurrence and development of PDAC. As such, it can be used as a prognostic marker to diagnose patients with PDAC.

Annexin A1 Expression Is Associated with Epithelial-Mesenchymal Transition (EMT), Cell Proliferation, Prognosis, and Drug Response in Pancreatic Cancer

Annexin A1 (ANXA1) is a calcium-dependent phospholipid-binding protein overexpressed in pancreatic cancer (PC). ANXA1 expression has been shown to take part in a wide variety of cancer biology, including carcinogenesis, cell proliferation, invasion, apoptosis, and metastasis, in addition to the initially identified anti-inflammatory effect in experimental settings. We hypothesized that ANXA1 expression is associated with cell proliferation and survival in PC patients. To test this hypothesis, we analyzed 239 PC patients in The Cancer Genome Atlas (TCGA) and GSE57495 cohorts. ANXA1 expression correlated with epithelial-mesenchymal transition (EMT) but weakly with angiogenesis in PC patients. ANXA1-high PC was significantly associated with a high fraction of fibroblasts and keratinocytes in the tumor microenvironment. ANXA1 high PC enriched multiple malignant gene sets, including hypoxia, tumor necrosis factor (TNF)-α signaling via nuclear factor-kappa B (NF-kB), and MTORC1, as well as apoptosis, protein secretion, glycolysis, and the androgen response gene sets consistently in both cohorts. ANXA1 expression was associated with TP53 mutation alone but associated with all KRAS, p53, E2F, and transforming growth factor (TGF)-β signaling pathways and also associated with homologous recombination deficiency in the TCGA cohort. ANXA1 high PC was associated with a high infiltration of T-helper type 2 cells in the TME, with advanced histological grade and MKI67 expression, as well as with a worse prognosis regardless of the grade. ANXA1 expression correlated with a sensitivity to gemcitabine, doxorubicin, and 5-fluorouracil in PC cell lines. In conclusion, ANXA1 expression is associated with EMT, cell proliferation, survival, and the drug response in PC.

A new 7-gene survival score assay for pancreatic cancer patient prognosis prediction

Gene expression features that are valuable for pancreatic ductal adenocarcinoma (PDAC) prognosis are still largely unknown. We aimed to explore pivotal molecular signatures for PDAC progression and establish an efficient survival score to predict PDAC prognosis. Overall, 163 overlapping genes were identified from three statistical methods, including differentially expressed genes (DEGs), coexpression network analysis (WGCNA), and target genes for miRNAs that were significantly related to PDAC patients' overall survival (OS). Then, according to the optimal value of the cross-validation curve (lambda = 0.031), 7 non-zero coefficients (ARNTL2, DSG3, PTPRR, ANLN, S100A14, ANKRD22, and TSPAN7) were selected to establish a prognostic prediction model of PDAC patients. We further confirmed the expression level of 7 genes using RT-PCR, western blot, and immunohistochemistry staining in PDAC patients' tissues. Our results showed that the ROC curve of the 7-mRNA model indicated good predictive ability for 1- and 2-year OS in three datasets (TCGA: 0.71, 0.69; ICGC: 0.8, 0.74; GEO batch: 0.61, 0.7, respectively). The hazard ratio (HR) of the low-risk group had a similar significant result (TCGA: HR = 0.3723; ICGC: HR = 0.2813; GEO batch: HR = 0.4999; all P < 0.001). Furthermore, Log-rank test results in three cohorts showed that the 7-mRNA assay excellently predicted the prognosis and metastasis, especially in TNM stage I&II subgroups of PDAC. In conclusion, the strong validation of our 7-mRNA signature indicates the promising effectiveness of its clinical application, especially in patients with TNM stages I&II.

A Novel Four-Gene Score to Predict Pathologically Complete (R0) Resection and Survival in Pancreatic Cancer

Pathologically complete (R0) resection is essential for prolonged survival in pancreatic cancer. Survival depends not only on surgical technique, but also on cancer biology. A biomarker to predict survival is a critical need in pancreatic treatment. We hypothesized that this 4-gene score, which was reported to reflect cell proliferation, is a translatable predictive biomarker for pancreatic cancer. A total of 954 pancreatic cancer patients from multiple cohorts were analyzed and validated. Pancreatic cancer had the 10th highest median score of 32 cancers in The Cancer Genome Atlas (TCGA) cohort. The four-gene score significantly correlated with pathological grade and MKI67 expression. The high four-gene score enriched cell proliferation-related and cancer aggressiveness-related gene sets. The high score was associated with activation of KRAS, p53, transforming growth factor (TGF)-β, and E2F pathways, and with high alteration rate of KRAS and CDKN2A genes. The high score was also significantly associated with reduced CD8+ T cell infiltration of tumors, but with high levels of interferon-γ and cytolytic activity in tumors. The four-gene score correlated with the area under the curve of irinotecan and sorafenib in primary pancreatic cancer, and with paclitaxel and doxorubicin in metastatic pancreatic cancer. The high four-gene score was associated with significantly fewer R0 resections and worse survival. The novelty of the study is in the application of the four-gene score to pancreatic cancer, rather than the bioinformatics technique itself. Future analyses of inoperable lesions are expected to clarify the utility of our score as a predictive biomarker of systemic treatments.

Unsupervised Hierarchical Clustering of Pancreatic Adenocarcinoma Dataset from TCGA Defines a Mucin Expression Profile that Impacts Overall Survival

Simple Summary

Pancreatic cancer has a dramatic outcome (survival curve < 6 months) that is the consequence of late diagnosis and the lack of efficient therapy. We investigated the relationship between the 22 mucin gene expression and the patient survival in pancreatic cancer datasets that provide a comprehensive mapping of transcriptomic alterations occurring during carcinogenesis. Using unsupervised hierarchical clustering analysis of mucin gene expression patterns, we identified two major clusters of patients: atypical mucin signature (#1; MUC15, MUC14/EMCN, and MUC18/MCAM) and membrane-bound mucin signature (#2; MUC1, -4, -16, -17, -20, and -21). The signature #2 is associated with shorter overall survival, suggesting that the pattern of membrane-bound mucin expression could be a new prognostic marker for PDAC patients.

Abstract

Mucins are commonly associated with pancreatic ductal adenocarcinoma (PDAC) that is a deadly disease because of the lack of early diagnosis and efficient therapies. There are 22 mucin genes encoding large O-glycoproteins divided into two major subgroups: membrane-bound and secreted mucins. We investigated mucin expression and their impact on patient survival in the PDAC dataset from The Cancer Genome Atlas (PAAD-TCGA). We observed a statistically significant increased messenger RNA (mRNA) relative level of most of the membrane-bound mucins (MUC1/3A/4/12/13/16/17/20), secreted mucins (MUC5AC/5B), and atypical mucins (MUC14/18) compared to normal pancreas. We show that MUC1/4/5B/14/17/20/21 mRNA levels are associated with poorer survival in the high-expression group compared to the low-expression group. Using unsupervised clustering analysis of mucin gene expression patterns, we identified two major clusters of patients. Cluster #1 harbors a higher expression of MUC15 and atypical MUC14/MUC18, whereas cluster #2 is characterized by a global overexpression of membrane-bound mucins (MUC1/4/16/17/20/21). Cluster #2 is associated with shorter overall survival. The patient stratification appears to be independent of usual clinical features (tumor stage, differentiation grade, lymph node invasion) suggesting that the pattern of membrane-bound mucin expression could be a new prognostic marker for PDAC patients.

Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting

PURPOSE

Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory.

EXPERIMENTAL DESIGN

We performed an in silico experiment to build an optimal CMS classifier using a training set of 1,329 samples from 12 studies and validation set of 1,329 samples from 14 studies. We constructed an assay on the basis of NanoString CodeSets for the top 472 genes, and performed analyses on paired flash-frozen (FF)/FFPE samples from 175 colorectal cancers to adapt the classifier to FFPE samples using a subset of genes found to be concordant between FF and FFPE, tested the classifier's reproducibility and repeatability, and validated in a CLIA-certified laboratory. We assessed prognostic significance of CMS in 345 patients pooled across three clinical trials.

RESULTS

The best classifier was weighted support vector machine with high accuracy across platforms and gene lists (>0.95), and the 472-gene model outperforming existing classifiers. We constructed subsets of 99 and 200 genes with high FF/FFPE concordance, and adapted FFPE-based classifier that had strong classification accuracy (>80%) relative to "gold standard" CMS. The classifier was reproducible to sample type and RNA quality, and demonstrated poor prognosis for CMS1-3 and good prognosis for CMS2 in metastatic colorectal cancer (P < 0.001).

CONCLUSIONS

We developed and validated a colorectal cancer CMS assay that is ready for use in clinical trials, to assess prognosis in standard-of-care settings and explore as predictor of therapy response.

High G2M Pathway Score Pancreatic Cancer is Associated with Worse Survival, Particularly after Margin-Positive (R1 or R2) Resection

Pancreatic cancer is highly mortal due to uncontrolled cell proliferation. The G2M checkpoint pathway is an essential part of the cell cycle. We hypothesized that a high G2M pathway score is associated with cell proliferation and worse survival in pancreatic cancer patients. Gene set variation analysis using the Hallmark G2M checkpoint gene set was used as a score to analyze a total of 390 human pancreatic cancer patients from 3 cohorts (TCGA, GSE62452, GSE57495). High G2M score tumors enriched other cell proliferation genes sets as well as MKI67 expression, pathological grade, and proliferation score. Independent of other prognostic factors, G2M score was predictive of disease-specific survival in pancreatic cancer. High G2M tumor was associated with high mutation rate of KRAS and TP53 and significantly enriched these pathway gene sets, as well as high infiltration of Th2 cells. High G2M score consistently associated with worse overall survival in 3 cohorts, particularly in R1/2 resection, but not in R0. High G2M tumor in R1/2 highly enriched metabolic and cellular components' gene sets compared to R0. To our knowledge, this is the first study to use gene set variation analysis as a score to examine the clinical relevancy of the G2M pathway in pancreatic cancer.

Immunological Gene Signature Associated With the Tumor Microenvironment of Pancreatic Cancer After Neoadjuvant Chemotherapy

OBJECTIVES

Neoadjuvant chemotherapy (NAC) has improved overall survival in patients with pancreatic ductal adenocarcinoma (PDAC), but its effects on immune gene signatures are unknown. Here, we examined the immune transcriptome after NAC for PDAC.

METHODS

Resected tumor specimens were obtained from 140 patients with PDAC who received surgery first (n = 93) or NAC (n = 47). Six patients were randomly selected from each group, and RNA was extracted from tumor tissues. We compared 770 immune-related genes among the 2 groups using nCounterPanCancer Immune Profiling (NanoString Technologies, Seattle, Wash). Gene clusters were classified into 14 immune function groups based on gene ontology argolism by nSolver 4.0 software (NanoString Technologies), and corresponding immune cell function scores were compared.

RESULTS

Eleven genes (LY86, SH2D1A, CD247, TIGIT, CR2, CD83, LAMP3, CXCR4, DUSP4, SELL, and IL2RA) were significantly downregulated in the NAC group. Gene expression analysis showed that the functions of regulatory T cells, B cells, and natural killer CD56 dim cells were significantly decreased in the NAC group.

CONCLUSIONS

Neoadjuvant chemotherapy may suppress regulatory T cells and B-cell function in the PDAC microenvironment. The 11 identified genes could be useful for predicting the efficacy of NAC and could be therapeutic targets for PDAC.

Revisiting the Concept of Stress in the Prognosis of Solid Tumors: A Role for Stress Granules Proteins?

Simple Summary

Stress Granules (SGs) were discovered in 1999 and while the first decade of research has focused on some fundamental questions, the field is now investigating their role in human pathogenesis. Since then, evidences of a link between SGs and cancerology are accumulating in vitro and in vivo. In this work we summarized the role of SGs proteins in cancer development and their prognostic values. We find that level of expression of protein involved in SGs formation (and not mRNA level) could serve a prognostic marker in cancer. With this review we strongly suggest that SGs (proteins) could be targets of choice to block cancer development and counteract resistance to improve patients care.

Abstract

Cancer treatments are constantly evolving with new approaches to improve patient outcomes. Despite progresses, too many patients remain refractory to treatment due to either the development of resistance to therapeutic drugs and/or metastasis occurrence. Growing evidence suggests that these two barriers are due to transient survival mechanisms that are similar to those observed during stress response. We review the literature and current available open databases to study the potential role of stress response and, most particularly, the involvement of Stress Granules (proteins) in cancer. We propose that Stress Granule proteins may have prognostic value for patients.