Pancreatic Cancer Dataset Research Articles

Deciphering the potential ability of DExD/H-box helicase 60 (DDX60) on the proliferation, diagnostic and prognostic biomarker in pancreatic cancer: a research based on silico, RNA-seq and molecular biology experiment

BackgroundPancreatic cancer is one of the most malignant abdominal tumors. DDX60 has been shown to be associated with a variety of tumor biological processes. However, DDX60 in pancreatic cancer has not been reported. Our study confirmed that DDX60 can serve as a novel biomarker for diagnosis and treatment of pancreatic cancer.Materials and methodsWe downloaded pancreatic cancer datasets from GEO and TCGA databases, respectively. To investigate the relationship between DDX60 expression and prognosis in pancreatic cancer. GSEA analysis was performed on DDX60. We performed RNA-seq to further explore the downstream biological targets of DDX60 and the signaling pathways that may be involved in pancreatic cancer. Finally, we tested it through molecular biology experiments. First, we constructed the plasmid and tested the plasmid effect by WB. Then MTT assay was performed to explore the effect of DDX60 knockout on the proliferation of pancreatic cancer cells. LDH assay was performed to explore the effect of DDX60 on the release of lactate dehydrogenase from tumor cells. The effect of DDX60 on cell proliferation was further explored by clonal formation experiment. Continue to explore clinical therapeutic drugs sensitive to DDX60 targets.ResultsBy analyzing the GSE71729, GSE183795, GSE16515, GSE28735 and GSE62452 data sets, we found that DDX60 was highly expressed in pancreatic cancer. And is associated with poorer outcomes for pancreatic patients. The mRNA expression level of DDX60 was correlated with lymph node metastasis and grade in clinical pancreatic patients. Through the results of RNA-seq analysis, GO and KEGG analysis showed that DDX60 may be associated with cell cycle in pancreatic cancer. Through molecular biology experiments (MTT, LDH, and clonal formation experiment), we found that When DDX60 is knocked down in pancreatic cancer cells, the proliferation ability of tumor cells is significantly decreased. Several drugs targeting about DDX60 have been found, such as JW-7–52-1, this could provide direction for drug therapy against the DDX60 target.ConclusionIn summary, DDX60 can be used as a novel biomarker related to the diagnosis and treatment of pancreatic cancer, participate in tumor proliferation, and is associated with poor prognosis in patients.

Abstract C054: Clinicogenomic determinants of response to FOLFIRINOX versus gemcitabine plus nab-paclitaxel as first-line systemic therapy for pancreatic cancer

Abstract Background: FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GA) are two recommended first-line systemic options for pancreatic cancer; however, there is limited evidence to support a particular regimen or provide guidance for treatment selection. We sought to compare outcomes for FFX versus GA across disease stages, and test whether there was an association between clinicogenomic features, treatment response, and survival. Methods: For this retrospective analysis, we utilized AACR’s GENIE PANC v1.2 database, the first large-scale pancreatic cancer dataset that combines genomic data with clinical annotations. The clinicogenomic profiles of patients were stratified and compared by stage: resectable/borderline resectable (R/BR), locally advanced (LA), or metastatic (MET). Patients who received FFX or GA as first-line therapy were compared across stages, with a focus on MET patients. We evaluated OS and PFS as survival outcomes and performed an adjusted analysis using Cox proportional hazards modeling. Treatment responses were further evaluated by change in serum CA 19-9. To test for features associated with treatment response, the genomic profiles of best and worst responders were compared. Results: Of 987 patients with complete genomic data, there were 452 R/BR, 82 LA, and 453 MET. While there was no difference in KRAS status, patients with later-stage disease accumulated greater common co-alterations: CDKN2A (19%, 23%, 35%), SMAD4 (26%, 29%, 30%), TP53 (72%, 73%, 77%), and ARID1A (6%, 6%, 11). Within MET patients, there was no significant difference in PFS between those treated with FFX (n=239) and GA (n=82), but patients treated with FFX had significantly longer OS, even after multivariable adjustment (median 1.14 vs. 0.854 years, p < 0.01; HR: 0.66, 95% CI: 0.49- 0.89, p < 0.01). Despite improved OS in FFX patients, GA patients had a greater reduction in CA 19-9 (median 50.0 vs. 69.0 U/mL). In addition, when comparing the genomic profiles of best/worst responders for MET patients treated with FFX and GA, there was no clear signal that one regimen should be favored over another in a specific context. Exploratory analyses in the R/BR and LA groups indicated no significant survival differences between first-line FFX vs. GA patients, nor in first-line treatment among patients who developed metastases post- diagnosis. Conclusion: Common co-alterations appear to accumulate in later-stage disease, and outcomes in patients undergoing systemic therapy is worse generally in patients with more complex genomic profiles. However, we did not observe any specific genomic features suggestive of a particular response to FFX vs. GA. Of note, we found that Met GA patients had a larger magnitude in CA 19-9 response, even though Met FFX was associated with improved OS. This provokes important questions regarding how biochemical response should be gauged in patients treated with these regimens. Citation Format: Kunling Tong, Elishama Kanu, Ethan Agritelley, Jiayin Bao, Peter J Allen, Daniel P Nussbaum. Clinicogenomic determinants of response to FOLFIRINOX versus gemcitabine plus nab-paclitaxel as first-line systemic therapy for pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C054.

A comprehensive learning based swarm optimization approach for feature selection in gene expression data

Gene expression data analysis is challenging due to the high dimensionality and complexity of the data. Feature selection, which identifies relevant genes, is a common preprocessing step. We propose a Comprehensive Learning-Based Swarm Optimization (CLBSO) approach for feature selection in gene expression data. CLBSO leverages the strengths of ants and grasshoppers to efficiently explore the high-dimensional search space. Ants perform local search and leave pheromone trails to guide the swarm, while grasshoppers use their ability to jump long distances to explore new regions and avoid local optima. The proposed approach was evaluated on several publicly available gene expression datasets and compared with state-of-the-art feature selection methods. CLBSO achieved an average accuracy improvement of 15% over the original high-dimensional data and outperformed other feature selection methods by up to 10%. For instance, in the Pancreatic cancer dataset, CLBSO achieved 97.2% accuracy, significantly higher than XGBoost-MOGA's 84.0%. Convergence analysis showed CLBSO required fewer iterations to reach optimal solutions. Statistical analysis confirmed significant performance improvements, and stability analysis demonstrated consistent gene subset selection across different runs. These findings highlight the robustness and efficacy of CLBSO in handling complex gene expression datasets, making it a valuable tool for enhancing classification tasks in bioinformatics.

Abstract 6475: Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health

Abstract Pancreatic cancer is the third leading cause of cancer-related death in the United States. Current therapeutic options offer a dismal overall survival with the 5-year survival at just ~12%. Analysis of the clinical and molecular underpinnings of pancreatic cancer is critical to developing both early detection methodologies as well as novel therapeutic options. The aggressiveness and deadly nature of this disease warranted the development of a data repository and analysis system dedicated to pancreatic cancer data. The Pancreatic Cancer Action Network’s (PanCAN) SPARK platform is a cloud-based data and analytics platform, powered by Velsera, that integrates real-world patient health data from PanCAN research initiatives and accelerates research by making pancreatic cancer data easier to access and use. Encompassing clinical, imaging, and genomics data from over 600 patients with pancreatic cancer within PanCAN’s Know Your Tumor ® (KYT) precision medicine service, the SPARK platform connects with petabytes of publicly available cancer data via the Cancer Genomics Cloud (CGC), also powered by Velsera. The CGC is part of NCI’s Cancer Research Data Commons (CRDC), a cloud-based data science infrastructure that connects data with analytics tools to allow researchers to share, integrate, analyze, visualize, and drive scientific discovery. Here, we demonstrate the application of these datasets by providing a case study demonstrating how to combine and enrich data to accelerate pancreatic cancer research. Currently, the genomic and proteomic data available on CRDC amounts to 402 and 304 cases of pancreatic tumor samples, respectively. We will use the capabilities of the SPARK and CGC platforms, which provide ready-to-use tools for multi-omics analysis that require no coding knowledge. Using the KYT and CRDC open-access pancreatic cancer data, we aim to demonstrate how to perform integrated analysis of data from diverse scientific domains, and share with collaborators all in one space, streamlining and increasing the potential for new scientific discoveries. Further expansion of the PanCAN and CGC datasets will undoubtedly provide a more comprehensive understanding of pancreatic cancer tumor biology. SPARK and CGC’s cloud based computation infrastructure, along with numerous available cancer datasets and easy-to-use multi-omics data processing workflows and data analytic tools will be instrumental in this process. Citation Format: Kawther Abdilleh, Zelia Worman, Rowan Beck, Cera Fisher, Divya Sain, Jack DiGiovanna, Lynn Matrisian, Sudheer Doss, Brandi Davis-Dusenbery. Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6475.

Abstract 2545: KRAS co-mutational landscape dictates treatment outcomes in pancreatic cancer (PC) patients

Abstract Background: KRAS is commonly mutated in PC patients, with a prevalence of ~80%. A holistic understanding of the landscape of KRAS mutations, co-occurring mutations (co-mutations) in other driver genes, and their impact on patient outcomes treated with current standard-of-care (SoC) therapies may inform better treatment decisions. Methods: In this study, we use a real-world clinico-genomics PC database (ConcertAI’s Genome360TM PC dataset; N=1302) to:1.Examine the distribution of KRAS mutations.2.Identify the genes most frequently co-mutated with KRAS, both positively and negatively, based on their odds ratios.3.Evaluate patient outcomes (overall survival (OS) and progression-free survival (PFS)) for the two most common first-line SoC therapies (FOLFIRINOX: folinic acid, fluorouracil, irinotecan hydrochloride, oxaliplatin, and GEMPAC: gemcitabine, paclitaxel) stratified by the mutational status of KRAS and TP53. Results: The top 5 most observed KRAS mutations and the top 3 positively (OR>1) and negatively (OR<1) KRAS co-mutated genes are shown in Table 1. 80%, 71%, and 65% of PC patients had pathogenic mutations in KRAS, TP53, and both genes, respectively. In the entire cohort, FOLFIRINOX (n=327, OS/PFS=1.26/0.58 years) was more effective (p-value<0.05) than GEMPAC (n=305, OS/PFS=0.79/0.44 years). However, the underlying genetic makeup (mutational status of KRAS and TP53) significantly affects these outcomes (Table 1). The KRAS+/TP53− (pathogenic mutations in KRAS, but not in TP53) cohort has the worst prognosis, and the advantage of FOLFIRINOX as the treatment of choice is not observed in this cohort. Table 1. Evaluation of KRAS mutational landscape in PC and its impact on treatment outcomes. Top 5 KRAS mutations KRAS mutations G12D G12V G12R Q61H Q61R % PC patients tested positive 42.51 32.52 14.33 4.62 1.79 Top 3 positively and negatively co-mutated genes with KRAS KRAS co-mutated genes Positive association Negative association TP53 CDKN2A SMAD4 BRAF TERT PTEN Odds Ratio 8.56 6.93 6.66 0.05 0.27 0.32 Treatment outcomes for various genetic states of KRAS and TP53 Mutational status OS (years) PFS (years) FOLFIRINOX GEMPAC FOLFIRINOX GEMPAC KRAS+ TP53+ (850) 1.13 (196) 0.68 (188) 0.58 (196) 0.39 (188) KRAS− TP53− (155) 2.08 (40) 1.11 (27) 0.66 (40) 0.51 (27) KRAS+ TP53− (190) 0.77 (33) 0.93 (37) 0.51 (33) 0.33 (37) KRAS− TP53+ (81) 2.14 (15) 0.84 (24) 0.54 (15) 0.4 (24) Conclusions: This study provides insights into the genetic makeup of patients with KRAS mutations and how co-mutation with TP53 affects outcomes of SoC treatments. It highlights the need for comprehensive genetic profiling to make better therapy decisions. Citation Format: Pritha Ghosh, Rohini George, Neeraj Singh, Smita Agrawal. KRAS co-mutational landscape dictates treatment outcomes in pancreatic cancer (PC) patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2545.

Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics

Previous work has shown that inhibition of abundant myeloid azurophil granule-associated serine proteases (ELANE [neutrophil elastase], PRTN3 [protease 3], and CTSG [Cathepsin G]) is required to stabilize some proteins in myeloid cells. We therefore hypothesized that effective inhibition of these proteases may be necessary for quantitative proteomic analysis of samples containing myeloid cells. To test this hypothesis, we thawed viably preserved acute myeloid leukemia cells from cryovials in the presence or the absence of diisopropyl fluorophosphate (DFP), a cell-permeable and irreversible serine protease inhibitor. Global proteomic analysis was performed, using label-free and isobaric peptide-labeling quantitation. The presence of DFP resulted in an increase of tryptic peptides (14–57%) and proteins (9–31%). In the absence of DFP, 11 to 31% of peptide intensity came from nontryptic peptides; 52 to 75% had cleavage specificity consistent with activities of ELANE–PRTN3. Treatment with DFP reduced the intensity of nontryptic peptides to 4-8% of the total. ELANE inhibition was 95%, based on diisopropyl phosphate modification of active site serine residue. Overall, the relative abundance of 20% of proteins was significantly altered by DFP treatment. These results suggest that active myeloid serine proteases, released during sample processing, can skew quantitative proteomic measurements. Finally, significant ELANE activity was also detected in Clinical Proteomics Tumor Analysis Consortium datasets of solid tumors (many of which have known myeloid infiltration). In the pancreatic cancer dataset, the median percentage of nontryptic intensity detected across patient samples was 34%, with many patient samples having more than half of their detected peptide intensity from nontryptic cleavage events consistent with ELANE-PRTN3 cleavage specificity. Our study suggests that in vitro cleavage of proteins by myeloid serine proteases may be relevant for proteomic studies of any tumor that contains infiltrating myeloid cells.

Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

Improving Representation Learning for Histopathologic Images with Cluster Constraints.

Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides-a process that is both labor-intensive and timeconsuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset. The code and additional details are accessible at https://github.com/wwyi1828/CluSiam.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

Tracking and handling behavioral biases in active learning frameworks

The computational capabilities of AI engines integrated with human knowledge and experience can help create intelligent human-in-the-loop (HITL) decision systems. Such synergistic frameworks improve the decision-making process and make AI-enabled systems more robust, accurate and smarter. In safety-critical applications that require a certain level of human supervision, human and AI engine errors can be costly. However, modeling human behavior in collaborative human-AI decision setup is not straightforward. Humans use cognitive mechanisms and decision heuristics to process information and make decisions under uncertainty. This paper, for the first time, presents a systematic framework for modeling, tracking and adaptation of behavioral biases in a collaborative decision environment within an “Active Learning” context. The proposed framework is validated using experiments conducted on a real-world pancreatic cancer dataset. We consider five different learning scenarios based on different grades of human experts and compare the performance of bias-aware decision models with naive models. It is observed that bias-aware models improve the classification accuracy of decision models by upto 16%.

Presence of PD-1 similarity genes in monocytes may promote the development of type 1 diabetes mellitus and poor prognosis of pancreatic cancer

IntroductionTo identify proteins and corresponding genes that share sequential and structural similarity with programmed cell death protein-1 (PD-1) in patients with type 1 diabetes mellitus (T1DM) via bioinformatics analysis.Research design...

Impact of patient characteristics and treatment patterns on outcomes in potentially resectable pancreatic cancer (PC).

681 Background: Treatment of PC remains a challenge, with surgery being the mainstay of potentially curative therapy. Even after surgical resection, however, many patients experience recurrence. There has been a shift toward treating PC as a systemic disease from diagnosis with neoadjuvant chemotherapy (nCT) or chemoradiation (nCRT). This study examines characteristics of real-world patients who were deemed to have resectable (R) or borderline resectable (BR) disease, and how they relate to overall survival (OS). Methods: This is a retrospective analysis of patients in an academic health system who presented for initiation of treatment for PC staged as R or BR. Descriptive data on patient characteristics, performance status (PS), laboratory values, neoadjuvant therapies, recurrence, and mortality were obtained. OS was evaluated using Kaplan-Meier analysis and log-rank tests. Two-sided p-values were calculated. Results: From electronic records at the University of Cincinnati, 129 patients were identified. Median age was 66 with baseline ECOG PS of mostly 0 (86, 67%) or 1 (40, 31%). Primary tumor site was most commonly head of pancreas (104, 81%). Of 129, 55 (43%) patients received surgery upfront; the rest received neoadjuvant therapy (39 nCRT, 35 nCT) with gemcitabine (68%) or 5-fluorouracil (32%) containing regimens. Of those receiving nCRT/nCT, 36 (49%) went on to resection; the rest experienced progression (27, 36%) or toxicities (11, 15%) including death. Of 129, 92 (71%) have died, with median OS of 20.1 months (95% CI 16.4 - 25.0) for the full cohort. On adjusted multivariable analysis, the following were associated with worse OS: failure to undergo resection (HR 5.65, 95% CI 3.17 - 10.09, p<0.0001); receiving <3 cycles nCT (HR 3.00, 95% CI 1.38 - 6.53, p=0.006); baseline CA 19-9 >100 (HR 2.73, 95% CI 1.52 - 4.90, p=0.001); BR disease (HR 2.20, 95% CI 1.27 – 3.81, p=0.005); not receiving RT (HR 1.91, 95% CI 1.04 - 3.53, p=0.038). Regimen choice was not associated with OS. Conclusions: In this real-world PC dataset, surgical resection remains the mainstay of curative therapy. Inadequate nCRT/nCT lead to suboptimal outcomes, likely reflecting patient physiology and disease biology effects. Future trials should focus on maximizing neoadjuvant therapy with the goal of resection.[Table: see text]

An Efficient Novel Approach with Multi Class Label Classification through Machine Learning Models for Pancreatic Cancer

Pancreatic cancer is right now the fourth largest cause of cancer-related deaths. Early diagnosis is one good solution for pancreatic cancer patients and reduces the mortality rate. Accurate and earlier diagnosis of the pancreatic tumor is a demanding task due to several factors such as delayed diagnosis and absence of early warning symptoms. The conventional distributed machine learning techniques such as SVM and logistic regression were not efficient to minimize the error rate and improve the classification of pancreatic cancer with higher accuracy. Therefore, a novel technique called Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System (DHEGQDRLCS) is developed in this paper. First, the number of data samples is collected from the repository dataset. This repository contains all the necessary files for the identification of prognostic biomarkers for pancreatic cancer. After the data collection, the separation of training and testing samples is performed for the accurate classification of pancreatic cancer samples. Then the training samples are considered and applied to Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System. The proposed hybrid classifier system uses the Kernel Quadratic Discriminant Function to analyze the training samples. After that, the Elitism gradient gene optimization is applied for classifying the samples into multiple classes such as non-cancerous pancreas, benign hepatobiliary disease i.e., pancreatic cancer, and Pancreatic ductal adenocarcinoma. Then the Reinforced Learning technique is applied to minimize the loss function based on target classification results and predicted classification results. Finally, the hybridized approach improves pancreatic cancer diagnosing accuracy. Experimental evaluation is carried out with pancreatic cancer dataset with Hadoop distributed system and different quantitative metrics such as Accuracy, balanced accuracy, F1-score, precision, recall, specificity, TN, TP, FN, FP, ROC_AUC, PRC_AUC, and PRC_APS. The performance analysis results indicate that the DHEGQDRLCS provides better diagnosing accuracy when compared to existing methods.

Dependent censoring based on parametric copulas

Summary Consider a survival time $T$ that is subject to random right censoring, and suppose that $T$ is stochastically dependent on the censoring time $C$. We are interested in the marginal distribution of $T$. This situation is often encountered in practice. Consider, for example, the case where $T$ is a patient’s time to death from a certain disease. Then the censoring time $C$ could be the time until the patient leaves the study or the time until death from another cause. If the reason for leaving the study is related to the health condition of the patient, or if the patient dies from a disease that has similar risk factors to the disease of interest, then $T$ and $C$ are likely to be dependent. In this paper we propose a new model that takes such dependence into account. The model is based on a parametric copula for the relationship between $T$ and $C$, and on parametric marginal distributions for $T$ and $C$. Unlike most other authors, we do not assume that the parameter defining the copula is known. We give sufficient conditions on these parametric copulas and marginals under which the bivariate distribution of $(T,C)$ is identified. These sufficient conditions are then checked for a wide range of common copulas and marginals. We also study the estimation of the model, and carry out extensive simulations and analysis on a pancreatic cancer dataset to illustrate the proposed model and estimation procedure.

Identification of the Genetic Association Between Type-2-Diabetes and Pancreatic Cancer.

Type-2-diabetes (T2D) and pancreatic cancer (PC) are both common diseases globally. Although T2D is reported as the adverse factor for predicting PC prognosis, its pathophysiology and relation with PC remain unknown. This study focused on exploring differentially expressed genes (DEGs) as well as their functional roles in T2D and PC, aiming to reveal the underlying association between the T2D and PC. To identify DEGs in T2D and PC, this study analyzed four microarray datasets obtained from Gene Expression Omnibus (GEO) database. Then, this work carried out enrichment as well as protein-protein interaction (PPI) network analysis for exploring DEGs-enriched functions and pathway. Besides, expression of hub genes was explored. TISIDB database was adopted to analyze the correlations among key gene and immune characteristics. Finally, the key gene expression was confirmed in vitro. DEGs were first screened from gene expression profiles of T2D and PC datasets, respectively. Then 135 common genes were identified in these four datasets. Based on functional analysis, common DEGs were mostly related to hormone secretion and metabolism pathways. Four hub genes were up-regulated, among which, MAFB was the most significant potential biomarker for PC. MAFB expression was strongly correlated with chemokines, chemokine receptors and immunomodulators. Finally, RT-qPCR was conducted to demonstrate the MAFB expression in T2D and PC. This study identified 15 hub genes with significant effects on the association of T2D with PC, and MAFB gene might be a biomarker for PC and had potential treatment value for PC.

SGLT-2 as a potential target in pancreatic cancer: the preliminary clue from The Cancer Genome Atlas data.

Sodium-glucose co-transporters-2 (SGLT-2) has been reported as overexpressed in tumors including pancreatic cancer (PC). The aim of this study was to investigate the clinicopathological and prognostic significance, as well as the potential role of SGLT-2 in PC development and progression. The expression of SGLT-2 was assessed using The Cancer Genome Atlas (TCGA) PC dataset (179 cases). The overall survival (OS) and disease-free survival (DFS) of PC patients with high and low SLC5A2 expression were compared using the online database Gene Expression Profiling Interactive Analysis (GEPIA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool. The genetic correlations of SLC5A2 genes in different subtypes of PC were analyzed by using cBioPortal and LinkedOmics online databases. No relationship between SGLT-2 expression and PC risk factors, tumor location, histology grade, or tumor-node-metastasis (TNM) stage was identified. Further, SGLT-2 could not be used as prognosis predictor. The KEGG analyses demonstrated that high SGLT-2 expression is correlated with activation of pathways related with chemical carcinogenesis, energy metabolism and drug metabolism, and the suppression of nucleotide excision repair, messenger RNA (mRNA) surveillance, and cell cycle regulation. Specifically, high SGLT-2 level also coexisted with upregulation of gene symbols for pancreatic progenitor subtype for PC. There is potential for SGLT-2 as a potential target for PC treatment, and SGLT-2 inhibitors should be further evaluated as a novel therapy in PC.

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.

Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

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.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual's Disease-Related Signature.

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

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

Background Pancreatic cancer (PC) is a malignant tumor of the digestive tract. It presents with atypical clinical symptoms and lacks specific diagnostic indicators. This study is aimed at exploring the potential biomarkers of PC. Methods TCGA database pancreatic cancer dataset was normalized and used to identify differentially expressed genes (DEGs). Survival, independent prognostic, and clinical correlation analyses were performed on DEGs to screen for key genes. DNA methylation, mutation, and copy number variation (CNV) analyses were used to analyze genetic variants in key genes. GSEA was performed to explore the functional enrichment of the key genes. Based on the expression of key genes, construction of a competing endogenous RNA (ceRNA) network, analysis of the tumor microenvironment (TME), and prediction of chemotherapeutic drug sensitivity were performed. Furthermore, the GEO database was used to validate the reliability of key genes. Results Two key genes (ECT2 and COL17A1) were identified, which were highly expressed in PC. The mRNA expression of ECT2 and COL17A1 was associated with DNA methylation and CNV. The cell cycle, proteasome, and pathways in cancer were enriched in the high-COL17A1 and ECT2 groups. The TME results showed that immune scores were decreased in the high-ECT2 group. CeRNA network results showed that there were eleven miRNAs were involved in the regulation of ECT2 and COL17A1. Moreover, pRRophetic analysis showed that 20 chemotherapeutic drugs were associated with ECT2 and COL17A1 expression. Conclusions Collectively, ECT2 and COL17A1 may be potential biomarkers for PC, providing a new direction for clinical diagnosis and treatment.