Pancreatic cancer survival analysis defines a signature that predicts outcome

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Keratin 6A Is Expressed at the Invasive Front and Enhances the Progression of Colorectal Cancer

Keratins (KRTs) are intermediate filament proteins in epithelial cells, and they are important for cytoskeletal organization. KRT6A, classified as a type II KRT, is normally expressed in stratified squamous epithelium and squamous cell carcinomas. Little is known about the expression and role of KRT6A in adenocarcinomas. We investigated the clinicopathologic and molecular biological significance of KRT6A in colorectal adenocarcinoma. Immunostaining of colorectal adenocarcinoma cases treated at our institution demonstrated that KRT6A showed significantly stronger expression at the invasive front than that at the tumor center (P < .0001). The high KRT6A-expression cases (n = 47) tended to have a high budding grade associated with significantly worse prognoses. A multivariate analysis revealed that the KRT6A expression status was an independent prognostic factor for overall survival (P = .0004), disease-specific survival (P = .0097), and progression-free survival (P = .0033). The correlation between KRT6A and patient prognoses was also validated in an external cohort from a published data set. To determine the function of KRT6A in vitro, KRT6A was overexpressed in 3 colon cancer cell lines: DLD-1, SW620, and HCT 116. KRT6A overexpression increased migration and invasion in DLD-1 but did not in SW620 and HCT116. In 3-dimensional sphere-forming culture, KRT6A expression enhanced the irregular protrusion around the spheroid in DLD-1. Our findings in this study indicated that KRT6A expression is a valuable prognostic marker of colorectal cancer and KRT6A may be involved the molecular mechanism in the progression of invasive areas of colorectal cancer.

ECM1 and KRT6A are involved in tumor progression and chemoresistance in the effect of dexamethasone on pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) has a very poor prognosis. Neoadjuvant chemotherapy is an effective PDAC treatment option, but chemotherapy causes unfavorable side effects. Glucocorticoids (e.g., dexamethasone [DEX]) are administered to reduce side effects of chemotherapy for solid tumors, including pancreatic cancer. Glucocorticoids have both beneficial and detrimental effects, however. We investigated the functional changes and gene-expression profile alterations induced by DEX in PDAC cells. PDAC cells were treated with DEX, and the cell proliferation, migration, invasion, and chemosensitivity to gemcitabine (GEM) were evaluated. The results demonstrated decreased cell proliferative capacity, increased cell migration and invasion, and decreased sensitivity to GEM. A comprehensive genetic analysis revealed marked increases in ECM1 and KRT6A in DEX-treated PDAC cells. We evaluated the effects of ECM1 and KRT6A expression by using PDAC cells transfected with those genes. Neither ECM1 nor KRT6A changed the cells' proliferation, but each enhanced cell migration and invasion. ECM1 decreased sensitivity to GEM. We also assessed the clinicopathological significance of the expressions of ECM1 and KRT6A in 130 cases of PDAC. An immunohistochemical analysis showed that KRT6A expression dominated the poorly differentiated areas. High expressions of these two proteins in PDAC were associated with a poorer prognosis. Our results thus demonstrated that DEX treatment changed PDAC cells' functions, resulting in decreased cell proliferation, increased cell migration and invasion, and decreased sensitivity to GEM. The molecular mechanisms of these changes involve ECM1 and KRT6A, whose expressions are induced by DEX.

Lipid Metabolism-Related Gene Markers Used for Prediction Prognosis, Immune Microenvironment, and Tumor Stage of Pancreatic Cancer

Recently, more and more evidence shows that lipid metabolism disorder has been observed in tumor, which impacts tumor cell proliferation, survival, invasion, metastasis, and response to the tumor microenvironment (TME) and tumor treatment. However, hitherto there has not been sufficient research to demonstrate the role of lipid metabolism in pancreatic cancer. This study contrives to get an insight into the relationship between the characteristics of lipid metabolism and pancreatic cancer. We collected samples of patients with pancreatic cancer from the Gene Expression Omnibus (GEO), the Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the International Cancer Genome Consortium (ICGC) databases. Firstly, we implemented univariate regression analysis to get prognosis-related lipid metabolism genes screened and a construction of protein-protein interaction (PPI) network ensued. Then, contingent on our screening results, we explored the molecular subtypes mediated by lipid metabolism-related genes and the correlated TME cell infiltration. Additionally, we studied the disparately expressed genes among disparate lipid metabolism subtypes and established a scoring model of lipid metabolism-related characteristics using the least absolute shrinkage and selection operator (LASSO) regression analysis. At last, we explored the relationship between the scoring model and disease prognosis, tumor stage, tumor microenvironment, and immunotherapy. Two subtypes, C1 and C2, were identified, and lipid metabolism-related genes were studied. The result indicated that the patients with subtype C2 have a significantly lower survival rate than that of the patients with subtype C1, and we found difference in abundance of different immune-infiltrating cells. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed the association of these differentially expressed genes with functions and pathways related to lipid metabolism. Finally, we established a scoring model of lipid metabolism-related characteristics based on the disparately expressed genes. The results show that our scoring model have a substantial effect on forecasting the prognosis of patients with pancreatic cancer. The lipid metabolism model is an important biomarker of pancreatic cancer. Using the model, the relationship between disease prognosis, molecular subtypes, TME cell infiltration characteristics, and immunotherapy in pancreatic cancer patients could be explored.

Comprehensive analyses and experimental verification of NETs and an EMT gene signature for prognostic prediction, immunotherapy, and chemotherapy in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is an aggressive malignancy with high mortality and poor prognosis. Neutrophil extracellular traps (NETs) and the epithelial-mesenchymal transition (EMT) significantly influence on the progression of various cancers. However, the underlying relevance of NETs- and EMT-associated genes on the outcomes of patients with PAAD remains to be elucidated. Transcriptome RNA sequencing data, together with clinical information and single-cell sequencing data of PAAD were collected from public databases. In the TCGA-PAAD cohort, ssGSEA was used to calculate NET and EMT scores. WGCNA was used to determine the key gene modules. A risk model with eight NET- and EMT-related genes (NERGs) was established using LASSO and multivariate Cox regression analysis. Patients in the reduced risk (RR) group showed better prognostic values compared with those in the elevated risk (ER) group. The prognostic model exhibited reliable and robust prediction when validated using an external database. The distributions of risk genes were explored in a single-cell sequencing data set. Immune infiltration, immune cycle, and immune checkpoints were compared between the RR and ER groups. Moreover, potential chemotherapeutic drugs were examined. DCBLD2 was identified as a key gene in PAAD cell lines by qRT-PCR, and was highly expressed in PAAD tissues. GSEA demonstrated that DCBLD2 induced the EMT. Transwell assays and western blotting showed that cell invasion and EMT induction were significantly reduced after DCBLD2 knockdown. Collectively, we constructed a prognosis model based on a NET and EMT gene signature, providing a valuable perspective for the prognostic evaluation and management of PAAD patient.

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

The weight of BMI in impacting postoperative and oncologic outcomes in pancreaticoduodenectomy is attenuated by a robotic approach

This study was undertaken to observe the effect of body mass index (BMI) on perioperative outcomes and survival when comparing robotic vs 'open' pancreaticoduodenectomy. With IRB approval, we prospectively followed 505 consecutive patients who underwent either robotic or 'open' pancreaticoduodenectomy from 2012 to 2021. For illustrative purposes, patients were separated based on the Center for Disease Control and Prevention BMI table but regression analysis was utilized to identify significant relationships involving BMI. Data are presented as median (mean ± SD). Significance was determined at p ≤ 0.05. 205 and 300 patients underwent 'open' and robotic pancreaticoduodenectomy, respectively. Neither sex nor age correlated with BMI in patients undergoing 'open' nor robotic operation. Operative duration correlated with increasing BMI in each operational approach, which was statistically significant for those receiving the 'open' operation (p = 0.02). There were statistically significantly fewer lymph nodes harvested with rising BMI in patients that had an 'open' operation (p = 0.01), but no such difference was found in patients undergoing the robotic approach. Length of stay (LOS) and in-hospital mortality were statistically significantly associated with rising BMI when an 'open' operation was undertaken (p = 0.02 and p = 0.0002, respectively) but not when the robotic platform was utilized. Patients with higher BMI had significantly longer operative duration, smaller lymph node harvest, greater LOS, and increased in-hospital mortality rate when undergoing 'open' pancreaticoduodenectomy, but not robotic pancreaticoduodenectomy. Thus, the robotic platform may attenuate the increased technical and oncologic difficulties associated with a greater BMI in patients undergoing pancreaticoduodenectomy.

Analysis of a large cohort of pancreatic cancer transcriptomic profiles to reveal the strongest prognostic factors

Pancreatic adenocarcinoma remains a leading cause of cancer-related deaths. In order to develop appropriate therapeutic and prognostic tools, a comprehensive mapping of the tumor's molecular abnormalities is essential. Here, our aim was to integrate available transcriptomic data to uncover genes whose elevated expression is simultaneously linked to cancer pathogenesis and inferior survival. A comprehensive search was performed in GEO to identify clinical studies with transcriptome-level gene expression data of pancreatic carcinoma with overall survival data and normal pancreatic tissues. After quantile normalization, the entire database was used to identify genes with altered expression. Cox proportional hazard regression was employed to uncover genes most strongly correlated with survival with a Bonferroni corrected p < 0.01. Perturbed biological processes and molecular pathways were identified to enable the understanding of underlying processes. A total of 16 available datasets were combined. The aggregated database comprised data of 1640 samples for 20,443 genes. When comparing with normal pancreatic tissues, a total of 2612 upregulated and 1977 downregulated genes were uncovered in pancreatic carcinoma. Among these, we found 24 genes with higher expression which significantly correlated with overall survival length also. The most significant genes were ANXA8, FAM83A, KRT6A, MET, MUC16, NT5E, and SLC2A1. These genes remained significant after a multivariate analysis also including grade and stage. Here, we assembled a large-scale database of pancreatic carcinoma samples and used this cohort to identify carcinoma-specific genes linked to altered survival outcomes. As our analysis focused on genes with higher expression, these could serve as future therapy targets.

Dysfunctional epigenetic protein-coding gene-related signature is associated with the prognosis of pancreatic cancer based on histone modification and transcriptome analysis

Emerging evidence suggests that epigenetic alterations are responsible for the oncogenesis and progression of cancer. However, the role of epigenetic reprogramming in pancreatic cancer is still not clear. In this study, we used the limma R package to identify differentially expressed protein-coding genes (PCGs) between pancreatic cancer tissues and normal control tissues. The cell-type identification by the estimating relative subsets of RNA transcripts (CIBERSORT) package was used to quantify relative cell fractions in tumors. Prognostic molecular clusters were constructed using ConsensusClusterPlus analysis. Furthermore, the least absolute shrinkage and selection operator and stepAIC methods were used to construct a risk model. We identified 2351 differentially expressed PCGs between pancreatic cancer and normal control tissues in The cancer genome atlas dataset. Combined with histone modification data, we identified 363 epigenetic PCGs (epi-PCGs) and 19,010 non-epi-PCGs. Based on the epi-PCGs, we constructed three molecular clusters characterized by different expression levels of chemokines and immune checkpoint genes and distinct abundances of various immune cells. Furthermore, we generated a 9-gene model based on dysfunctional epi-PCGs. Additionally, we found that patients with high risk scores showed poorer prognoses than patients with low risk scores (p < 0.0001). Further analysis showed that the risk score was significantly related to survival and was an independent risk factor for pancreatic cancer patients. In conclusion, we constructed a 9-gene prognostic risk model based on epi-PCGs that might serve as an effective classifier to predict overall survival and the response to immunotherapy in pancreatic cancer patients.

Long Non-Coding RNAs Associated with Mitogen-Activated Protein Kinase in Human Pancreatic Cancer

Long non-coding RNAs (lncRNAs) have emerged as a significant player in various cancers, including pancreatic cancer. However, how lncRNAs are aberrantly expressed in cancers is largely unknown. We hypothesized that lncRNAs would be regulated by signaling pathways and contribute to malignant phenotypes of cancer. In this study, to understand the significance of mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK), which is a major aberrant signaling pathway in pancreatic cancer, for the expression of lncRNAs, we performed comparative transcriptome analyses between pancreatic cancer cell lines with or without activation of MAPK. We identified 45 lncRNAs presumably associated with MAPK in pancreatic cancer cells; among these, LINC00941 was consistently upregulated by MAPK. The immediate genomic upstream region flanking LINC00941 was identified as a promoter region, the activity of which was found to be preferentially associated with MAPK activity via ETS-1 binding site. LINC00941 promoted cell proliferation in vitro. Moreover, TCGA data analysis indicated that high expression of LINC00941 was associated with poor prognosis of patients with pancreatic cancer. Transcriptomes comparing transcriptions between cells with and without LINC00941 knockdown revealed 3229 differentially expressed genes involved in 44 biological processes, including the glycoprotein biosynthetic process, beta-catenin-TCF complex assembly, and histone modification. These results indicate that MAPK mediates the aberrant expression of lncRNAs. LINC00941 is the lncRNA by MAPK most consistently promoted, and is implicated in the dismal prognosis of pancreatic cancer. MAPK-associated lncRNAs may play pivotal roles in malignant phenotypes of pancreatic cancer, and as such might represent both potentially valid therapeutic targets and diagnostic biomarkers.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer

Early-stage non-small cell lung cancer (NSCLC) patients are at substantial risk of poor prognosis. We attempted to develop a reliable metabolic gene-set-based signature that can predict prognosis accurately for early-stage patients. Least absolute shrinkage and selection operator method Cox regression models were performed to filter the most useful prognostic genes, and a metabolic gene-set-based signature was constructed. Forty-two metabolism-related genes were finally identified, and with specific risk score formula, patients were classified into high-risk and low-risk groups. Overall survival was significantly different between the two groups in discovery (HR: 5.050, 95% CI: 3.368-7.574, P < 0.001), internal validation series (HR: 6.044, 95% CI: 3.918-9.322, P < 0.001), GSE30219 (HR: 2.059, 95% CI: 1.510-2.808, P < 0.001), and GSE68456 (HR: 2.448, 95% CI: 1.723-3.477, P < 0.001). Survival receiver operating characteristic curve at the 5 years suggested that the metabolic signature (area under the curve [AUC] = 0.805) had better prognostic accuracy than any other clinicopathological factors. Further analysis revealed the distinct differences in immune cell infiltration and tumor purity reflected by an immune and stromal score between high- and low-risk patients. In conclusion, the novel metabolic signature developed in our study shows robust prognostic accuracy in predicting prognosis for early-stage NSCLC patients and may function as a reliable marker for guiding more effective immunotherapy strategies.

Preoperative data-based deep learning model for predicting postoperative survival in pancreatic cancer patients

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis even after curative resection. A deep learning-based stratification of postoperative survival in the preoperative setting may aid the treatment decisions for improving prognosis. This study was aimed to develop a deep learning model based on preoperative data for predicting postoperative survival.

METHODS

The patients who underwent surgery for PDAC between January 2014 and May 2015. Clinical data-based machine learning models and computed tomography (CT) data-based deep learning models were developed separately, and ensemble learning was utilized to combine two models. The primary outcomes were the prediction of 2-year overall survival (OS) and 1-year recurrence-free survival (RFS). The model's performance was measured by area under the receiver operating curve (AUC) and was compared with that of American Joint Committee on Cancer (AJCC) 8th stage.

RESULTS

The median OS and RFS were 23 and 10 months in training dataset (n = 229), and 22 and 11 months in test dataset (n = 53), respectively. The AUC of the ensemble model for predicting 2-year OS and 1-year RFS in the test dataset was 0.76 and 0.74, respectively. The performance of the ensemble model was comparable to that of the AJCC in predicting 2-year OS (AUC, 0.67; P = 0.35) and superior to the AJCC in predicting 1-year RFS (AUC, 0.54; P = 0.049).

CONCLUSION

and relevance: Our ensemble model based on routine preoperative variables showed good performance for predicting prognosis for PDAC patients after surgery.

Cytokeratin 10 (CK10) expression in cancer: A tissue microarray study on 11,021 tumors

Cytokeratin 10 (CK10) is a type I acidic low molecular weight cytokeratin which is mainly expressed in keratinizing squamous epithelium of the skin. Variable levels of CK10 protein have been described in squamous carcinomas of different sites and in some other epithelial neoplasms. To comprehensively determine the prevalence of CK10 expression in normal and neoplastic tissues, a tissue microarray containing 11,021 samples from 131 different tumor types and subtypes was analyzed by immunohistochemistry. CK10 immunostaining was detectable in 41 (31.3 %) of 131 tumor categories, including 18 (13.7 %) tumor types with at least one strongly positive case. The highest rate of positive staining was found in squamous cell carcinomas from various sites of origin (positive in 18.6 %-66.1 %) and in Warthin tumors of salivary glands (47.8 %), followed by various tumor entities known to potentially exhibit areas with squamous cell differentiation such as teratomas (33.3 %), basal cell carcinomas of the skin (14.3 %), adenosquamous carcinomas of the cervix (11.1 %), and several categories of urothelial neoplasms (3.1 %-16.8 %). In a combined analysis of 956 squamous cell carcinomas from 11 different sites of origin, reduced CK10 staining was linked to high grade (p < 0.0001) and advanced stage (p = 0.0015) but unrelated to HPV infection. However, CK10 staining was not statistically related to grade (p = 0.1509) and recurrence-free (p = 0.5247) or overall survival (p = 0.5082) in 176 cervical squamous cell carcinomas. In the urinary bladder, CK10 staining occurred more commonly in muscle-invasive (17.7 %) than in non-invasive urothelial carcinomas (4.0 %-6.0 %; p < 0.0001). In summary, our data corroborate a role of CK10 as a suitable marker for mature, keratinizing squamous cell differentiation in epithelial tissues. CK10 immunohistochemistry may thus be instrumental for a more objective evaluation of the clinical significance of focal squamous differentiation in cancer.

Pan-cancer analyses identify DCBLD2 as an oncogenic, immunological, and prognostic biomarker

Discoidin, CUB, and LCCL domain-containing protein 2 (DCBLD2) is a two-domain transmembrane protein-coding gene located on chromosome 3, the protein expressed by which acts as the membrane receptor of semaphorin and vascular endothelial growth factor during the development of axons and blood vessels. Although several research evidences at the cellular and clinical levels have associated DCBLD2 with tumorigenesis, nothing is known regarding this gene from a pan-cancer standpoint. In this study, we systematically analyzed the influence of DCBLD2 on prognosis, cancer staging, immune characteristics, and drug sensitivity in a variety of cancers based on a unified and standardized pan-cancer dataset. In addition, we performed GO enrichment analyses and KEGG analyses of DCBLD2-related genes and DCBLD2-binding proteins. Our results showed that DCBLD2 is a potential oncogenic, immunological as well as a prognostic biomarker in terms of pan-cancer, and is expected to contribute to the improvement of tumor prognosis and the development of targeted therapy.

lncRNAs Functioned as ceRNA to Sponge miR-15a-5p Affects the Prognosis of Pancreatic Adenocarcinoma and Correlates With Tumor Immune Infiltration

Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with poor prognosis worldwide. Mounting evidence suggests that the expression of lncRNAs and the infiltration of immune cells have prognostic value for patients with PAAD. We used Gene Expression Omnibus (GEO) database and identified six genes (COL1A2, ITGA2, ITGB6, LAMA3, LAMB3, and LAMC2) that could affect the survival rate of pancreatic adenocarcinoma patients. Based on a series of in silico analyses for reverse prediction of target genes associated with the prognosis of PAAD, a ceRNA network of mRNA (COL1A2, ITGA2, LAMA3, LAMB3, and LAMC2)–microRNA (miR-15a-5p)–long non-coding RNA (LINC00511, LINC01578, PVT1, and TNFRSF14-AS1) was constructed. We used the algorithm “CIBERSORT” to assess the proportion of immune cells and found three overall survival (OS)–associated immune cells (monocytes, M1 macrophages, and resting mast cell). Moreover, the OS-associated gene level was significantly positively associated with immune checkpoint expression and biomarkers of immune cells. In summary, our results clarified that ncRNA-mediated upregulation of OS-associated genes and tumor-infiltration immune cells (monocytes, M1 macrophages M1, and resting mast cell resting) correlated with poor prognosis in PAAD.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

An Inflammatory Response-Related Gene Signature Reveals Distinct Survival Outcome and Tumor Microenvironment Characterization in Pancreatic Cancer

Background: Desmoplasia or rich fibrotic stroma is a typical property of pancreatic cancer (PC), with a significant impact on tumor progression, metastasis, and chemotherapy response. Unusual inflammatory responses are considered to induce fibrosis of tissue, but the expression and clinical significance of inflammatory response-related genes in PC have not been clearly elucidated. Methods: Prognosis-related differentially expressed genes (DEGs) between tumor and normal tissues were identified by comparing the transcriptome data of PC samples based on The Cancer Genome Atlas (TCGA) portal and the Genotype Tissue Expression (GTEx) databases. Samples from the ArrayExpress database were used as an external validation cohort. Results: A total of 27 inflammatory response-related DEGs in PC were identified. Least absolute shrinkage and selection operator (LASSO) analysis revealed three core genes that served as an inflammatory response gene signature (IRGS), and a risk score was calculated. The diagnostic accuracy of the IRGS was validated in the training (n = 176) and validation (n = 288) cohorts, which reliably predicted the overall survival (OS) and disease-free survival (DFS) of patients with PC. Furthermore, multivariate analysis identified the risk score as an independent risk factor for OS and DFS. The comprehensive results suggested that a high IRGS score was correlated with decreased CD8⁺ T-cell infiltration, increased M2 macrophage infiltration, increased occurrence of stroma-activated molecular subtype and hypoxia, enriched myofibroblast-related signaling pathways, and greater benefit from gemcitabine. Conclusion: The IRGS was able to promisingly distinguish the prognosis, the tumor microenvironment characteristics, and the benefit from chemotherapy for PC.

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.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Background

Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods

In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results

Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion

These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00969-3.

Overexpression of abnormal spindle-like microcephaly-associated (ASPM) increases tumor aggressiveness and predicts poor outcome in patients with lung adenocarcinoma

Background

Cumulative evidence points to abnormal spindle-like microcephaly-associated (ASPM) protein being overexpressed in various cancers, and the aberrant expression of ASPM has been shown to promote cancer tumorigenicity and progression. However, its role and clinical significance in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the expression patterns of ASPM and its clinical significance in LUAD.

Methods

In total, 4 original worldwide LUAD microarray mRNA expression datasets (N=1,116) with clinical and follow-up annotations were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The expression of ASPM protein in LUAD patients was detected by immunohistochemistry. Survival analysis and Cox regression analysis were used to examine the prognostic value of ASPM expression. Gene set enrichment analysis (GSEA) was performed to investigate the relationship between ASPM and LUAD.

Results

Dataset analyses and immunohistochemistry revealed that ASPM expression was significantly higher in the LUAD tissues compared with normal lung tissues, especially in the advanced tumor stage. Additionally, overexpression of ASPM was significantly correlated with shorter overall survival (OS) and relapse-free survival (RFS) in LUAD. Univariate and multivariate Cox regression analyses revealed that the overexpression of ASPM was a potential independent predictor of poor OS and RFS. However, ASPM overexpression was not significantly associated with predicting OS in lung squamous cell carcinoma. GSEA analysis demonstrated that ASPM was significantly enriched in the cell cycle, DNA replication, homologous recombination, RNA degradation, mismatch repair, and p53 signaling pathways.

Conclusions

These findings demonstrate the important role of ASPM in the tumorigenesis and progression of LUAD.

Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas

BACKGROUND

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). IPMNs are generally associated with high risk of developing malignancy and therefore need to be diagnosed and assessed accurately, once detected. Existing diagnostic methods are inadequate, and identification of efficient biomarker capable of detecting high-risk IPMNs is necessitated. Moreover, the mechanism of development of malignancy in IPMNs is also elusive.

METHODS

Gene expression meta-analysis conducted using 12 low-risk IPMN and 23 high-risk IPMN tissue samples. We have also listed all the altered miRNAs and long noncoding RNAs (lncRNAs), identified their target genes, and performed pathway analysis. We further enlisted cyst fluid proteins detected to be altered in high-risk or malignant IPMNs and compared them with fraction of differentially expressed genes secreted into cyst fluid.

RESULTS

Our meta-analysis identified 270 upregulated and 161 downregulated genes characteristically altered in high-risk IPMNs. We further identified 61 miRNAs and 14 lncRNAs and their target genes and key pathways contributing towards understanding of the gene regulation during the progression of the disease. Most importantly, we have detected 12 genes altered significantly both in cystic lesions and cyst fluid.

CONCLUSION

Our study reports, for the first time, a meta-analysis identifying key changes in gene expression between low-risk and high-risk IPMNs and also explains the regulatory aspect through construction of a miRNA-lncRNA-mRNA interaction network. The 12-gene-signature could function as potential biomarker in cyst fluid for detection of IPMN with a high risk of developing malignancy.

Screening for the Key Proteins Associated with Rete Testis Invasion in Clinical Stage I Seminoma via Label-Free Quantitative Mass Spectrometry

Rete testis invasion (RTI) is an unfavourable prognostic factor for the risk of relapse in clinical stage I (CS I) seminoma patients. Notably, no evidence of difference in the proteome of RTI-positive vs. -negative CS I seminomas has been reported yet. Here, a quantitative proteomic approach was used to investigate RTI-associated proteins. 64 proteins were differentially expressed in RTI-positive compared to -negative CS I seminomas. Of them, 14-3-3γ, ezrin, filamin A, Parkinsonism-associated deglycase 7 (PARK7), vimentin and vinculin, were validated in CS I seminoma patient cohort. As shown by multivariate analysis controlling for clinical confounders, PARK7 and filamin A expression lowered the risk of RTI, while 14-3-3γ expression increased it. Therefore, we suggest that in real clinical biopsy specimens, the expression level of these proteins may reflect prognosis in CS I seminoma patients.

Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

Background

The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC.

Methods

The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation.

Results

A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively.

Conclusions

In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08097-w.

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.

Comprehensive Analysis of E2F Family Members in Human Gastric Cancer

Gastric cancer (GC) is the second most common cancer and the third most frequent cause of cancer-related deaths in China. E2Fs are a family of transcription factors reported to be involved in the tumor progression of various cancer types; however, the roles of individual E2Fs are still not known exactly in tumor progression of GC. In this study, we examined the expression of E2Fs to investigate their roles in tumor progression in GC patients using multiple databases, including ONCOMINE, GEPIA2, Kaplan-Meier plotter, cBioPortal, Metascape, LinkedOmics, GeneMANIA, STRING and UCSC Xena. We also performed real-time polymerase chain reaction (RT-PCR) to validate the expression levels of individual E2Fs in several GC cell lines. Our results demonstrated that the mRNA levels of E2F1/2/3/5/8 were significantly higher both in GC tissues and cell lines. The expression levels of E2F1 and E2F4 were correlated with poor overall survival (OS), decreased post-progression survival (PPS), and decreased progression-free survival (FP) in patients with GC. However, overexpression of E2F2, E2F5, E2F7 and E2F8 is significantly associated with disease-free survival and overall survival in patients with GC. In addition, higher E2F3 and E2F6 mRNA expression was found to increase GC patients' OS and PPS. 224 of 415 patients with STAD (54%) had gene mutations that were associated with longer disease-free survival (DFS) but not OS. Cell cycle pathway was closely associated with mRNA level of more than half of E2Fs (E2F1/2/3/7/8). There were close and complicated interactions among E2F family members. Finally, our results indicated the gene expressions of E2Fs had a positive relationship with its copy numbers. Taken together, E2F1/2/3/5/8 can serve as biomarkers for GC patients with high prognostic value for OS of GC patients or therapeutic targets for GC.

Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.

Method

In this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan-Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.

Results

A four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.

Conclusion

We constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.

Methods

A comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan–Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.

Results

We identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.

Conclusion

This study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Background

Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.

Methods

Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.

Results

There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.

Conclusion

The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

Transmembrane protein DCBLD2 is correlated with poor prognosis and affects phenotype by regulating epithelial-mesenchymal transition in human glioblastoma cells

OBJECTIVE

We attempt to investigate the biological function of the discoidin, complement C1r/C1s,Uegf, and Bmp1 and Limulus factor C, Coch, and Lgl domain-containing 2 (DCBLD2) in glioblastoma, as well as its effect on the epithelial-mesenchymal transition (EMT) process.

METHODS

The public expression data of glioblastoma samples and normal brain samples from The Cancer Genome Atlas database, Genotype-Tissue Expression database and Chinese Glioma Genome Atlas database were used to analyze the expression of DCBLD2 and its relationship with the survival of patients with glioblastoma. Quantitative real-time PCR and western blot were used to evaluate mRNA and protein levels of DCBLD2. Cell viabilities were tested using Cell Counting Kit-8 and clone formation assays. Cell invasive and migratory abilities were measured by transwell assays.

RESULTS

DCBLD2 expression was upregulated in glioblastoma and has a significantly positive correlation with the WHO classification. In addition, high expression of DCBLD2 was closely correlated with poor prognosis in primary and recurrent patients with glioblastoma. What is more, we found that knockdown of DCBLD2 notably reduced the cell proliferative, invasive and migratory capacities by elevating the expression of E-cadherin and inhibiting the expression of vimentin, snail, slug and twist. However, overexpression of DCBLD2 presented the opposite results.

CONCLUSION

The current study reveals that high expression of DCBLD2 is closely related to poor prognosis in glioblastoma and can significantly enhance the tumor cell viability and metastasis by activating the EMT process, suggesting that DCBLD2 may be a possible biomarker for glioblastoma treatment.

Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma

Background: Accumulating evidence shows that the elevated expression of DCBLD2 (discoidin, CUB and LCCL domain-containing protein 2) is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K–M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC. Results: Univariate analysis, K–M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both DCBLD2 expression in tissue and extracellular vesicles had significant diagnostic value. Besides, DCBLD2 expression was correlated with infiltrating level of CD8⁺ T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 might be involved in cell motility, angiogenesis, and cancer-associated pathways. Conclusion: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 might play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.

A 14-gene gemcitabine resistance gene signature is significantly associated with the prognosis of pancreatic cancer patients

To identify a gemcitabine resistance-associated gene signature for risk stratification and prognosis prediction in pancreatic cancer. Pearson correlation analysis was performed with gemcitabine half maximal inhibitory concentration (IC50) data of 17 primary pancreatic cancer lines from Genomics of Drug Sensitivity in Cancer (GDSC) and the transcriptomic data from GDSC and Broad Institute Cancer Cell Line Encyclopedia, followed by risk stratification, expression evaluation, overall survival (OS) prediction, clinical data validation and nomogram establishment. Our biomarker discovery effort identified a 14-gene signature, most of which featured differential expression. The 14-gene signature was associated with poor OS in E-MTAB-6134 (HR 2.37; 95% CI 1.75–3.2; p < 0.0001), pancreatic cancer-Canada (PACA-CA) (HR 1.76; 95% CI 1.31–2.37; p = 0.00015), and 4 other independent validation cohorts: pancreatic cancer-Australia (PACA-AU) (HR 1.9; 95% CI 1.38–2.61; p < 0.0001), The Cancer Genome Atlas (TCGA) (HR 1.73; 95% CI 1.11–2.69; p = 0.014), GSE85916 (HR 1.97; 95% CI 1.14–3.42; p = 0.014) and GSE62452 (HR 1.82; 95% CI 1.02–3.24; p = 0.039). Multivariate analysis revealed that the 14-gene risk score was an independent pancreatic cancer outcome predictor in E-MTAB-6134 (p < 0.001) and TCGA (p = 0.006). A nomogram including the 14-gene was established for eventual clinical translation. We identified a novel gemcitabine resistance gene signature for risk stratification and robust categorization of pancreatic cancer patients with poor prognosis.

Clinical Perspective on Proteomic and Glycomic Biomarkers for Diagnosis, Prognosis, and Prediction of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo)adjuvant chemotherapy regimens. Early detection of PDAC may increase the overall survival. It is furthermore foreseen that precision medicine will provide improved prognostic stratification and prediction of therapeutic response. In this review, omics-based discovery efforts are presented that aim for novel diagnostic and prognostic biomarkers of PDAC. For this purpose, we systematically evaluated the literature published between 1999 and 2020 with a focus on protein- and protein-glycosylation biomarkers in pancreatic cancer patients. Besides genomic and transcriptomic approaches, mass spectrometry (MS)-based proteomics and glycomics of blood- and tissue-derived samples from PDAC patients have yielded new candidates with biomarker potential. However, for reasons discussed in this review, the validation and clinical translation of these candidate markers has not been successful. Consequently, there has been a change of mindset from initial efforts to identify new unimarkers into the current hypothesis that a combination of biomarkers better suits a diagnostic or prognostic panel. With continuing development of current research methods and available techniques combined with careful study designs, new biomarkers could contribute to improved detection, prognosis, and prediction of pancreatic cancer.

Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort

Pancreatic adenocarcinoma (PAAD) is the most serious solid tumor type throughout the world. The present study aimed to identify novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. A total of 4777 differentially expressed genes (DEGs) were filtered, 2536 upregulated DEGs and 2241 downregulated DEGs. Weighted gene co-expression network analysis was then used and identified 12 modules, of which, blue module with the most significant enrichment result was selected. KEGG and GO enrichment analyses showed that all DEGs of blue module were enriched in EMT and PI3K/Akt pathway. Three hub genes (ITGB1, ITGB5, and OSMR) were determined as key genes with higher expression levels, significant prognostic value and excellent diagnostic efficiency for PAAD. Additionally, some small molecule drugs that possess the potential to treat PAAD were screened out, including thapsigargin (TG). Functional in vitro experiments revealed that TG repressed cell viability via inactivating the PI3K/Akt pathway in PAAD cells. Totally, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat PAAD.

Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma

The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.

The value of a metabolic reprogramming-related gene signature for pancreatic adenocarcinoma prognosis prediction

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. Extensive enhancement of glycolysis and reprogramming of lipid metabolism are both associated with the development and progression of PDAC. Previous studies have suggested that various gene signatures could convey prognostic information about PDAC. However, the use of these signatures has some limitations, perhaps because of a lack of knowledge regarding the genetic and energy supply backgrounds of PDAC. Therefore, we conducted multi-mRNA analysis based on metabolic reprogramming to identify novel signatures for accurate prognosis prediction in PDAC patients. In this study, a three-gene signature comprising MET, ENO3 and CD36 was established to predict the overall survival of PDAC patients. The three-gene signature could divide patients into high- and low-risk groups by disparities in overall survival verified by log-rank test in two independent validation cohorts and could differentiate tumors from normal tissues with excellent accuracy in four Gene Expression Omnibus (GEO) cohorts. We also found a positive correlation between the risk score of the gene signature and inherited germline mutations in PDAC predisposition genes. A glycolysis and lipid metabolism-based gene nomogram and corresponding calibration curves showed significant performance for survival prediction in the TCGA-PDAC dataset. The high-risk designation was closely connected with oncological signatures and multiple aggressiveness-related pathways, as determined by gene set enrichment analysis (GSEA). In summary, our study developed a three-gene signature and established a prognostic nomogram that objectively predicted overall survival in PDAC. The findings could provide a reference for the prediction of overall survival and could aid in individualized management for PDAC patients.

A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer

Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of OC patients. In this study, samples from 367 patients in The Cancer Genome Atlas (TCGA) database were subjected to mRNA expression profiling. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with epithelial–mesenchymal transition (EMT) and assess their prognostic power with a Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, after which the patients were divided into high-risk and low-risk groups. The six-gene signature was an independent prognostic biomarker of OS for OC patients based on the multivariate Cox regression analysis. In addition, the six-gene model was validated with samples from the Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of OC, which might become a therapeutic tool with clinical applications in the future.

MiR-10b inhibits migration and invasion of pancreatic ductal adenocarcinoma via regulating E2F7

BACKGROUND

Abnormal microRNAs (miRNAs) expression is closely related to the development and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). We aimed to elucidate the invasive mechanism and clinical significance of miR-10b in PDAC.

METHODS

The RNA sequence data of pancreatic cancer were extracted from the TCGA database. R packages were performed to analyze the differential expression of RNAs. TargetScan, picTar, and miRanda were used to predict the target gene of miRNA. The expression level of the selected candidate was tested by western blot and RT-PCR in PDAC cells and tissues. Scrape and Transwell assays were determined the effect of candidate molecules on cell migration and invasion. The gain of function and loss of function was achieved by co-culture with mimics and vector. Luciferase reporters were generated based on the psiCHECK2 vector. The relative luciferase activity was measured with the Dual-Luciferase Reporter Assay System and Infinate M200 PRO microplate reader.

RESULTS

Based on the TCGA data and bioinformatics analysis, we obtained seven differentially expressed miRNAs. Both TCGA data and our center clinical date indicated that miR-10b was contributed to the poor survival of PDAC. Based on the target gene prediction database, we found that E2F7 was a target mRNA of miR-10b. In subsequent experiments in molecular biology, miR-10b expression was downregulated in PDAC cells and tissues, while E2F7 was upregulated. Scrape and Transwell assay indicated that miR-10b could inhibit the invasion and migration of PDAC. MiR-10b was confirmed to be by the E2F7 targeting site by dual-luciferase report. Moreover, rescue experiments prove that miR-10b could inhibit the invasion and migration of PDAC cells by regulating E2F7 expression.

CONCLUSION

Our results suggest that miR-10b could inhibit the progression of PDAC by regulating E2F7 expression and acts as an independent prognostic risk factor for PDAC.

DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients

Pancreatic cancer (PC) is associated with a high mortality rate. We explored the interindividual variation of cancer outcomes, attributable to DNA methylation, gene expression, and clinical factors among PC patients. We aim to determine whether we could differentiate subjects with greater nodal involvement, higher cancer staging, and subsequent survival. We modeled every response variable as a function of a linear predictor involving the effects of clinical variables, methylation, and gene expression in a Bayesian framework. Our results highlight the overall importance of wide-spread alterations in methylation and gene expression patterns associated with survival, nodal metastasis, and staging.

Serum N-Glycome analysis reveals pancreatic cancer disease signatures

Background &Aims

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type with loco‐regional spread that makes the tumor surgically unresectable. Novel diagnostic tools are needed to improve detection of PDAC and increase patient survival. In this study we explore serum protein N‐glycan profiles from PDAC patients with regard to their applicability to serve as a disease biomarker panel.

Methods

Total serum N‐glycome analysis was applied to a discovery set (86 PDAC cases/84 controls) followed by independent validation (26 cases/26 controls) using in‐house collected serum specimens. Protein N‐glycan profiles were obtained using ultrahigh resolution mass spectrometry and included linkage‐specific sialic acid information. N‐glycans were relatively quantified and case‐control classification performance was evaluated based on glycosylation traits such as branching, fucosylation, and sialylation.

Results

In PDAC patients a higher level of branching (OR 6.19, P‐value 9.21 × 10⁻¹¹) and (antenna)fucosylation (OR 13.27, P‐value 2.31 × 10⁻⁹) of N‐glycans was found. Furthermore, the ratio of α2,6‐ vs α2,3‐linked sialylation was higher in patients compared to healthy controls. A classification model built with three glycosylation traits was used for discovery (AUC 0.88) and independent validation (AUC 0.81), with sensitivity and specificity values of 0.85 and 0.71 for the discovery set and 0.75 and 0.72 for the validation set.

Conclusion

Serum N‐glycome analysis revealed glycosylation differences that allow classification of PDAC patients from healthy controls. It was demonstrated that glycosylation traits rather than single N‐glycan structures obtained in this clinical glycomics study can serve as a basis for further development of a blood‐based diagnostic test.

Methylation of immune-regulatory cytokine genes and pancreatic cancer outcomes

Aim: Given the immunosuppressive nature of pancreatic cancer, we investigated the relationship between epigenetic modification of immune-regulatory cytokine genes and pancreatic cancer outcomes. Materials & methods: We evaluated DNA methylation of 184 pancreatic tumor samples from The Cancer Genome Atlas for 111 CpG loci in seven cytokine genes: IL10, IL6, IL8, TGFβ1, TGFβ2, TGFβ3 and TNF. We used Cox regression to evaluate the associations between methylation and overall survival, disease-specific survival and disease progression (α = 0.05). Results: Poorer survival was associated with increased methylation in fifteen CpG probes in TGFβ1, TGFβ2, TGFβ3 and TNF. We also detected improved outcomes for three loci in IL10, IL8 and IL6. Conclusion: Epigenetic regulation of cytokine-related gene expression may be associated with pancreatic cancer outcomes.

Construction of a Competitive Endogenous RNA Network for Pancreatic Adenocarcinoma Based on Weighted Gene Co-expression Network Analysis and a Prognosis Model

Pancreatic adenocarcinoma (PAAD) is a pancreatic disease with considerable mortality worldwide. Because of a lack of obvious symptoms at the early stage, most PAAD patients are diagnosed at the terminal stage and prognosis is usually poor. In this study, we firstly obtained RNA sequencing data of 181 patients with PAAD from The Cancer Genome Atlas (TCGA) database to identify early diagnostic biomarkers for PAAD. Survival-related mRNAs were identified using a weighted gene co-expression network analysis (WGCNA), and then a linear prognostic model of seven long non-coding RNAs (lncRNAs) was established using univariate and multivariate Cox proportional hazards regression analyses, which is verified using a time-dependent receiver operating characteristic (ROC) curve analysis. Finally, according to the survival analysis, we constructed a survival-related competing endogenous RNA (ceRNA) network. Our results showed that: (1) The upregulated genes related to cell cycle-related pathway (including homologous recombination, DNA replication and mismatch repair) in PAAD can increase the proliferation ability of cancer cells; (2) The 7-lncRNA signature can predict the overall survival (OS) of PAAD patients; and (3) The key mRNAs and lncRNAs are involved in mutual regulation in the ceRNA network.

A novel 20-gene prognostic score in pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers. Known risk factors for this disease are currently insufficient in predicting mortality. In order to better prognosticate patients with PDAC, we identified 20 genes by utilizing publically available high-throughput transcriptomic data from GEO, TCGA and ICGC which are associated with overall survival and event-free survival. A score generated based on the expression matrix of these genes was validated in two independent cohorts. We find that this “Pancreatic cancer prognostic score 20 –PPS20” is independent of the confounding factors in multivariate analyses, is dramatically elevated in metastatic tissue compared to primary tumor, and is higher in primary tumors compared to normal pancreatic tissue. Transcriptomic analyses show that tumors with low PPS20 have overall more immune cell infiltration and a higher CD8 T cell/Treg ratio when compared to those with high PPS20. Analyses of proteomic data from TCGA PAAD indicated higher levels of Cyclin B1, RAD51, EGFR and a lower E-cadherin/Fibronectin ratio in tumors with high PPS20. The PPS20 score defines not only prognostic and biological sub-groups but can predict response to targeted therapy as well. Overall, PPS20 is a stronger and more robust transcriptomic signature when compared to similar, previously published gene lists.

Anti-tumour effects of Keratin 6A in lung adenocarcinoma

BACKGROUND

To examine the effects of Keratin 6A (KRT6A) protein on the proliferation, migration and invasion abilities of lung adenocarcinoma cells, and to analyse the relationship between the expression level of KRT6A protein and the survival prognosis of lung adenocarcinoma patients.

METHODS

Western Blot was used to detect the expression of KRT6A protein in lung adenocarcinoma cell lines. CCK-8 experiment and colony formation assays were performed to detect the proliferation ability. Wound healing assay and transwell migration assay were conducted to detect the migration ability. Transwell invasion assay was conducted to detect the invasion ability. Immunohistochemistry was used to detect the expression of KRT6A protein in lung adenocarcinoma tissues.

RESULTS

We first found that the expression of KRT6A protein in lung adenocarcinoma cell lines was low. After overexpressed KRT6A protein in lung adenocarcinoma cells, we then found that KRT6A protein could not only inhibit the proliferation ability of lung adenocarcinoma cells but also inhibit them migration and invasion abilities. In addition, we also found that there had obvious difference in the expression of KRT6A protein in between patients. And through further analysis, we finally discovered that high expression of KRT6A protein was related to favourable prognosis in lung adenocarcinoma patients.

CONCLUSIONS

KRT6A protein inhibits the proliferation, migration and invasion abilities of lung adenocarcinoma cells, and high expression of KRT6A protein is a predictor of good prognosis in patients with lung adenocarcinoma.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Identification of a robust functional subpathway signature for pancreatic ductal adenocarcinoma by comprehensive and integrated analyses

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy and its mortality continues to rise globally. Because of its high heterogeneity and complex molecular landscapes, published gene signatures have demonstrated low specificity and robustness. Functional signatures containing a group of genes involved in similar biological functions may display a more robust performance.

METHODS

The present study was designed to excavate potential functional signatures for PDAC by analyzing maximal number of datasets extracted from available databases with a recently developed method of FAIME (Functional Analysis of Individual Microarray Expression) in a comprehensive and integrated way.

RESULTS

Eleven PDAC datasets were extracted from GEO, ICGC and TCGA databases. By systemically analyzing these datasets, we identified a robust functional signature of subpathway (path:00982_1), which belongs to the drug metabolism-cytochrome P450 pathway. The signature has displayed a more powerful and robust capacity in predicting prognosis, drug response and chemotherapeutic efficacy for PDAC, particularly for the classical subtype, in comparison with published gene signatures and clinically used TNM staging system. This signature was verified by meta-analyses and validated in available cell line and clinical datasets with chemotherapeutic efficacy.

CONCLUSION

The present study has identified a novel functional PDAC signature, which has the potential to improve the current systems for predicting the prognosis and monitoring drug response, and to serve a linkage to therapeutic options for combating PDAC. However, the involvement of path:00982_1 subpathway in the metabolism of anti-PDAC chemotherapeutic drugs, particularly its biological interpretation, requires a further investigation. Video Abstract.

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.

Upregulation of ASPM, BUB1B and SPDL1 in tumor tissues predicts poor survival in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a major cause of cancer-associated mortality, with poor patient outcome. The present study aimed to identify key candidate genes and investigate the potential molecular mechanisms associated with the progression of PDAC. The GSE46234 dataset was downloaded from the Gene Expression Omnibus database, in order to identify the upregulated differentially expressed genes (DEGs) in PDAC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the biological functions and pathways of the upregulated DEGs, and a protein-protein interaction (PPI) network was subsequently constructed to screen the hub genes. Subsequently, survival analyses of the hub genes were undertaken in patients with PDAC, using The Cancer Genome Atlas dataset. Reverse transcription-quantitative (RT-q)PCR analysis was performed to assess the mRNA expression levels of the hub genes associated with the prognosis of patients with PDAC. In the present study, 65 upregulated DEGs were identified. GO analysis suggested that the DEGs were enriched in response to hypoxia, calcium ion and negative regulation of catecholamine. KEGG analysis demonstrated that the DEGs were enriched in gastric acid secretion, the ECM-receptor interaction and the cGMP-PKG signaling pathway. Among the 18 hub genes determined by module screening of the PPI network, upregulation of three key genes, abnormal spindle-like microcephaly-associated protein (ASPM), mitotic checkpoint serine/threonine-protein kinase BUB1 β (BUB1B) and protein spindly (SPDL1), was significantly associated with worse overall survival and disease-free survival time in patients with PDAC. Furthermore, ASPM, BUB1B and SPDL1 were demonstrated to be associated with advanced tumor stage, and their upregulation in PDAC tumor tissues was validated using RT-qPCR analysis. Taken together, the results of the present study demonstrate that ASPM, BUB1B and SPDL1 may have the potential to function as prognostic markers and therapeutic targets for PDAC.

HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.