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

Differences in Tumor Gene Expression Profiles Between De Novo Metastatic Castration-sensitive Prostate Cancer and Metastatic Relapse After Prior Localized Therapy

BACKGROUND AND OBJECTIVE

It has been reported that patients with de novo metastatic castration-sensitive prostate cancer (dn-mCSPC) have worse prognosis and outcomes than those whose cancer relapses after prior local therapy (PLT-mCSPC). Our aim was to interrogate and validate underlying differences in tumor gene expression profiles between dn-mCSPC and PLT-mCSPC.

METHODS

The inclusion criteria were histologically confirmed prostate adenocarcinoma and the availability of RNA sequencing data for treatment-naïve primary prostate tissue. RNA sequencing was performed by Tempus or Caris Life Sciences, both of which have Clinical Laboratory Improvement Amendments certification. The Tempus cohort was used for interrogation, while the Caris cohort was used for validation. Differential gene expression analysis between the cohorts was conducted using the DEseq2 pipeline. The resulting gene expression profiles were further analyzed using Gene Set Enrichment software to identify pathways with enrichment in each cohort.

KEY FINDINGS AND LIMITATIONS

Overall, 128 patients were eligible, of whom 78 were in the Tempus cohort (dn-mCSPC 37, PLT-mCSPC 41) and 50 were in the Caris cohort (dn-mCSPC 30, PLT-mCSPC 20). Tumor tissues from patients with dn-mCSPC had higher expression of genes associated with inflammation pathways, while tissues from patients with PLT-mCSPC had higher expression of genes involved in oxidative phosphorylation, fatty acid metabolism, and androgen response pathways.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Our study revealed upregulation of distinct genomic pathways in dn-mCSPC in comparison to PLT-mCSPC. These hypothesis-generating data could guide personalized therapy for men with prostate cancer and explain different survival outcomes for dn-mCSPC and PLT-mCSPC.

PATIENT SUMMARY

We measured gene expression levels in tumors from patients with metastatic castration-sensitive prostate cancer. In patients with metastatic disease at first diagnosis, inflammatory pathways were upregulated. In patients whose metastasis occurred on relapse after treatment, androgen response pathways were upregulated. These findings could help in personalizing therapy for prostate cancer and explaining differences in survival.

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.

Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling

Rationale: Accumulating evidence illustrated that the reprogramming of the super-enhancers (SEs) landscape could promote the acquisition of metastatic features in pancreatic cancer (PC). Given the anatomy-based TNM staging is limited by the heterogeneous clinical outcomes in treatment, it is of great clinical significance to tailor individual stratification and to develop alternative therapeutic strategies for metastatic PC patients based on SEs. Methods: In our study, ChIP-Seq analysis for H3K27ac was performed in primary pancreatic tumors (PTs) and hepatic metastases (HMs). Bootstrapping and univariate Cox analysis were implemented to screen prognostic HM-acquired, SE-associated genes (HM-SE genes). Then, based on 1705 PC patients from 14 multicenter cohorts, 188 machine-learning (ML) algorithm integrations were utilized to develop a comprehensive super-enhancer-related metastatic (SEMet) classifier. Results: We established a novel SEMet classifier based on 38 prognostic HM-SE genes. Compared to other clinical traits and 33 published signatures, the SEMet classifier possessed robust and powerful performance in predicting prognosis. In addition, patients in the SEMetlow subgroup owned dismal survival rates, more frequent genomic alterations, and more activated cancer immunity cycle as well as better benefits in immunotherapy. Remarkably, there existed a tight correlation between the SEMetlow subgroup and metastatic phenotypes of PC. Among 18 SEMet genes, we demonstrated that E2F7 may promote PC metastasis through the upregulation of TGM2 and DKK1. Finally, after in silico screening of potential compounds targeted SEMet classifier, results revealed that flumethasone could enhance the sensitivity of metastatic PC to routine gemcitabine chemotherapy. Conclusion: Overall, our study provided new insights into personalized treatment approaches in the clinical management of metastatic PC patients.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

miR-451a suppresses papillary thyroid cancer cell proliferation and invasion and facilitates apoptosis through targeting DCBLD2 and AKT1

Papillary thyroid cancer (PTC) is a common malignancy. MicroRNAs (miRNAs) may act as oncogenes or tumor suppressor genes. However, the role of miR-451a in PTC is not fully understood. Hence, the objective of the study was to research the effect and mechanism of miR-451a in PTC. Differentially expressed miRNAs between GSE113629 and GSE103996 databases were assessed by Venn diagram. miR-451a and its downstream target genes were assessed by RT-PCR and Western blot. The proliferation, invasion, and apoptosis were determined by CCK-8, EdU, transwell, and flow cytometry assays. Dual-luciferase reporter assay were used to evaluated the target of miR-451a. Xenografted tumors was used to explore the function of miR-451a in vivo. Pathological changes and related protein expression were measured by HE staining and immunohistochemistry. MiR-451a was downregulated in PTC tissues and blood, and low expression of miR-451a was related to short overall survival, serious lymph node metastasis and high TNM grade in PTC patients. Moreover, increase of miR-451a restrained the proliferation and invasion and accelerated the apoptosis. Furthermore, miR-451a repressed VEGF signaling pathway. Importantly, miR-451a was demonstrated to target DCBLD2 and AKT1. Overexpression of DCBLD2 and AKT1 could restore the effect of miR-451a on PTC cells. In addition, miR-451a reduced the growth of xenografted tumors in vivo. The data suggested that miR-451a attenuated the proliferation, invasion and promoted apoptosis in PTC cells via inhibiting DCBLD2 and AKT1.

A cancer-associated fibroblast gene signature predicts prognosis and therapy response in patients with pancreatic cancer

Pancreatic cancer is a lethal malignancy with a 5-year survival rate of about 10% in the United States, and it is becoming an increasingly prominent cause of cancer death. Among pancreatic cancer patients, pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all cases and has a very poor prognosis with an average survival of only 1 year in about 18% of all tumor stages. In the past years, there has been an increasing interest in cancer-associated fibroblasts (CAFs) and their roles in PDAC. Recent data reveals that CAFs in PDAC are heterogeneous and various CAF subtypes have been demonstrated to promote tumor development while others hinder cancer proliferation. Furthermore, CAFs and other stromal populations can be potentially used as novel prognostic markers in cancer. In the present study, in order to evaluate the prognostic value of CAFs in PDAC, CAF infiltration rate was evaluated in 4 PDAC datasets of TCGA, GEO, and ArrayExpress databases and differentially expressed genes (DEGs) between CAF-high and CAF-low patients were identified. Subsequently, a CAF-based gene expression signature was developed and studied for its association with overall survival (OS). Additionally, functional enrichment analysis, somatic alteration analysis, and prognostic risk model construction was conducted on the identified DEGs. Finally, oncoPredict algorithm was implemented to assess drug sensitivity prediction between high- and low-risk cohorts. Our results revealed that CAF risk-high patients have a worse survival rate and increased CAF infiltration is a poor prognostic indicator in pancreatic cancer. Functional enrichment analysis also revealed that "extracellular matrix organization" and "vasculature development" were the top enriched pathways among the identified DEGs. We also developed a panel of 12 genes, which in additional to its prognostic value, could predict higher chemotherapy resistance rate. This CAF-based panel can be potentially utilized alone or in conjunction with other clinical parameters to make early predictions and prognosticate responsiveness to treatment in PDAC patients. Indeed, it is necessary to conduct extensive prospective investigations to confirm the clinical utility of these findings.

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.

An Inflammatory Response Related Gene Signature Associated with Survival Outcome and Gemcitabine Response in Patients with Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumors with an extremely low 5-year survival rate. Accumulating evidence has unveiled that inflammatory response promotes tumor progression, enhances angiogenesis, and causes local immunosuppression. Herein, we aim to develop an inflammatory related prognostic signature, and found it could be used to predict gemcitabine response in PDAC.

Methods: PDAC cohorts with mRNA expression profiles and clinical information were systematically collected from the four public databases. An inflammatory response related genes (IRRGs) prognostic signature was constructed by LASSO regression analysis. Kaplan–Meier survival analysis, receiver operating characteristic analysis, principal component analysis, and univariate and multivariate Cox analyses were carried out to evaluate effectiveness, and reliability of the signature. The correlation between gemcitabine response and risk score was evaluated in the TCGA-PAAD cohort. The GDSC database, pRRophetic algorithm, and connectivity map analysis were used to predict gemcitabine sensitivity and identify potential drugs for the treatment of PDAC. Finally, we analyzed differences in frequencies of gene mutations, infiltration of immune cells, as well as biological functions between different subgroups divided by the prognostic signature.

Results: We established a seven IRRGs (ADM, DCBLD2, EREG, ITGA5, MIF, TREM1, and BTG2) signature which divided the PDAC patients into low- and high-risk groups. Prognostic value of the signature was validated in 11 PDAC cohorts consisting of 1337 PDAC patients from 6 countries. A nomogram that integrated the IRRGs signature and clinicopathologic factors of PDAC patients was constructed. The risk score showed positive correlation with gemcitabine resistance. Two drugs (BMS-536924 and dasatinib) might have potential therapeutic implications in high-risk PDAC patients. We found that the high-risk group had higher frequencies of KRAS, TP53, and CDKN2A mutations, increased infiltration of macrophages M0, neutrophils, and macrophages M2 cells, as well as upregulated hypoxia and glycolysis pathways, while the low-risk group had increased infiltration of CD8⁺ T, naïve B, and plasma and macrophages M1 cells.

Conclusion: We constructed and validated an IRRGs signature that could be used to predict the prognosis and gemcitabine response of patients with PDAC, as well as two drugs (BMS-536924 and dasatinib) may contribute to PDAC treatment.

Prognostic Stratification Based on HIF-1 Signaling for Evaluating Hypoxic Status and Immune Infiltration in Pancreatic Ductal Adenocarcinomas

Pancreatic ductal adenocarcinoma (PDAC) has a hypoxic and desmoplastic tumor microenvironment (TME), leading to treatment failure. We aimed to develop a prognostic classifier to evaluate hypoxia status and hypoxia-related molecular characteristics of PDAC. In this study, we classified PDAC into three clusters based on 16 known hypoxia-inducible factor 1 (HIF-1)-related genes. Nine differentially expressed genes were identified to construct an HIF-1 score system, whose predictive efficacy was evaluated. Furthermore, we investigated oncogenic pathways and immune-cell infiltration status of PDAC with different scores. The C-index of the HIF-1score system for OS prediction in the meta-PDAC cohort and the other two validation cohorts were 0.67, 0.63, and 0.65, respectively, indicating that it had a good predictive value for patient survival. Furthermore, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the HIF-1α score system for predicting 1-, 3-, and 4-year OS indicated the HIF-1α score system had an optimal discrimination of prognostic prediction for PDAC. Importantly, our model showed superior predictive ability compared to previous hypoxia signatures. We also classified PDAC into HIF-1 scores of low, medium, and high groups. Then, we found high enrichment of glycolysis, mTORC1 signaling, and MYC signaling in the HIF-1 score high group, whereas the cGMP metabolic process was activated in the low score group. Of note, analysis of public datasets and our own dataset showed a high HIF-1 score was associated with high immunosuppressive TME, evidenced by fewer infiltrated CD8⁺ T cells, B cells, and type 1 T-helper cells and reduced cytolytic activity of CD8⁺ T cells. In summary, we established a specific HIF-1 score system to discriminate PDAC with various hypoxia statuses and immune microenvironments. For highly hypoxic and immunosuppressive tumors, a combination treatment strategy should be considered in the future.

Cell-to-cell interaction analysis of prognostic ligand-receptor pairs in human pancreatic ductal adenocarcinoma

Cell-to-cell interactions (CCIs) through ligand-receptor (LR) pairs in the tumor microenvironment underlie the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). However, there is scant knowledge of the association of CCIs with PDAC prognosis, which is critical to the identification of potential therapeutic candidates. Here, we sought to identify the LR pairs associated with PDAC patient prognosis by integrating survival analysis and single-cell CCI prediction. Via survival analysis using gene expression from cancer cohorts, we found 199 prognostic LR pairs. CCI prediction based on single-cell RNA-seq data revealed the enriched LR pairs associated with poor prognosis. Notably, the CCIs involved epithelial tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages through integrin-related and ANXA1-FPR pairs. Finally, we determined that CCIs involving 33 poor-prognostic LR pairs were associated with tumor grade. Although the clinical implication of the set of LR pairs must be determined, our results may provide potential therapeutic targets in PDAC.

Extracellular vesicles in pancreatic cancer progression and therapies

Pancreatic cancer (PC) is one of the leading causes of cancer-related death worldwide due to delayed diagnosis and limited treatments. More than 90% of all pancreatic cancers are pancreatic ductal adenocarcinoma (PDAC). Extensive communication between tumour cells and other cell types in the tumour microenvironment have been identified which regulate cancer hallmarks during pancreatic tumorigenesis via secretory factors and extracellular vesicles (EVs). The EV-capsuled factors not only facilitate tumour growth locally, but also enter circulation and reach distant organs to construct a pre-metastatic niche. In this review, we delineate the key factors in pancreatic ductal adenocarcinoma derived EVs that mediate different tumour processes. Also, we highlight the factors that are related to the crosstalk with cancer stem cells/cancer-initiating cells (CSC/CIC), the subpopulation of cancer cells that can efficiently metastasize and resist currently used chemotherapies. Lastly, we discuss the potential of EV-capsuled factors in early diagnosis and antitumour therapeutic strategies.