A two-microRNA signature as a diagnostic and prognostic marker of pancreatic adenocarcinoma

Comprehensive analysis of ICD-related lncRNAs in predicting risk stratification, clinical prognosis and immune response for breast cancer

BACKGROUND

Breast cancer (BRCA) represents a significant threat with high mortality rates due to relapse, metastasis, and chemotherapy resistance. As a regulated cell death process characterized by the induction of immunogenic signals, immunogenic cell death (ICD) has been identified as an effective anti-tumorigenesis approach. However, the comprehensive study and its clinical value of ICD-related lncRNAs in BRCA is still missing.

METHODS

The transcriptome matrix and corresponding clinical information of BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was performed to identify ICD-related lncRNAs (ICDRLs). To determine the prognostic value of the identified ICDRLs, univariate Cox regression analysis, LASSO algorithm, and multivariate Cox regression analysis were employed to construct a risk model. The prognostic risk model was subsequently evaluated using univariate and multivariate Cox regression analysis, as well as Nomogram analysis. In vitro experiments were also conducted to validate the bioinformatics findings using quantitative real-time PCR (qRT-PCR).

RESULTS

We established a prognostic risk signature consisting of five ICDRLs. The prognostic value of this model was subsequently confirmed in guiding BRCA prognostic stratification. Furthermore, we explored the correlation of the risk score with various clinical characteristics and chemotherapy response. qRT-PCR result confirmed the abnormal expression of ICDRLs, which was consistent with the bioinformatics data.

CONCLUSIONS

Our findings provide evidence of the critical role of ICDRLs in BRCA and offer a novel perspective for exploring precise treatment options for BRCA patients.

DSCR9/miR-21-5p axis inhibits pancreatic cancer proliferation and resistance to gemcitabine via BTG2 signaling

The outcome of pancreatic adenocarcinoma (PAAD) patients is poor, given resistance to gemcitabine. Long noncoding RNA (lncRNA) has been implicated in the carcinogenesis of pancreatic cancer; however, its function and mechanism in PAAD resistance to gemcitabine (GEM) are yet unknown. Herein, we demonstrate that lncRNA DSCR9 is significantly reduced in PAAD in vitro and in vivo. CCK-8, BrdU and flow cytometry assays show that overexpression of DSCR9 markedly suppresses pancreatic cancer cell proliferation and invasion, and promotes apoptosis under gemcitabine treatment. BTG2 acts as a tumor suppressor by reducing the proliferation and invasion of pancreatic cancer cells and increasing gemcitabine-induced apoptosis. Immunofluorescence (IF) staining combined with fluorescence in situ hybridization (FISH) of pancreatic cancer tissues shows that DSCR9 and BTG2 are both increased in pancreatic cancer tissues. Luciferase assay shows that miR-21-5p simultaneously binds to DSCR9 and 3'UTR of BTG2; DSCR9 relieves miR-21-5p-induced inhibition of BTG2 by competing with BTG2 for miR-21-5p binding. Overexpression of miR-21-5p enhances the invasiveness of pancreatic cancer cells by promoting cancer cell proliferation and invasion and attenuating gemcitabine-induced apoptosis. Overexpression of miR-21-5p attenuates the effect of DSCR9 overexpression on BTG2 expression and invasiveness of pancreatic cancer cells. Finally, miR-21-5p expression is increased, while BTG2 expression is decreased in pancreatic cancer tissues. miR-21-5p is negatively correlated with DSCR9 and BTG2. In conclusion, the DSCR9/miR-21-5p/BTG2 axis modulates pancreatic cancer proliferation, invasion, and gemcitabine resistance.

Identification and validation of an immune-related lncRNAs signature to predict the overall survival of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological cancer in women. Studies had reported that immune-related lncRNAs signatures were valuable in predicting the survival and prognosis of patients with various cancers. In our study, the prognostic value of immune-related lncRNAs was investigated in OC patients from TCGA-RNA-seq cohort (n=378) and HG-U133_Plus_2 cohort (n=590), respectively. Pearson correlation analysis was implemented to screen the immune-related lncRNA and then univariate Cox regression analysis was performed to explore their prognostic value in OC patients. Five prognostic immune-related lncRNAs were identified as prognostic lncRNAs. Besides, they were inputted into a LASSO Cox regression to establish and validate an immune-related lncRNA prognostic signature in TCGA-RNA-Seq cohort and HG-U133_Plus_2 cohort, respectively. Based on the best cut-off value of risk score, patients were divided into high- and low-risk groups. Survival analysis suggested that patients in the high-risk group had a worse overall survival (OS) than those in the low-risk group in both cohorts. The association between clinicopathological feathers and risk score was then evaluated by using stratification analysis. Moreover, we constructed a nomogram based on risk score, age and stage, which had a strong ability to forecast the OS of the OC patients. The influence of risk score on immune infiltration and immunotherapy response were assessed and the results suggested that patients with high-risk score might recruit multiple immune cells and stromal cells, leading to facilitating immune surveillance evasive. Ultimately, we demonstrated that the risk model was associated with chemotherapy response of multiple antitumor drugs, especially for paclitaxel, metformin and veliparib, which are commonly used in treating OC patients. In conclusion, we constructed a novel immune-related lncRNA signature, which had a potential prognostic value for OC patients and might facilitate personalized counselling for immunotherapy and chemotherapy.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

Establishment and Analysis of an Individualized Immune-Related Gene Signature for the Prognosis of Gastric Cancer

A growing number of studies have shown that immunity plays an important clinical role in the process of gastric cancer (GC). The purpose of this study was to explore the function of differentially expressed immune-related genes (DEIRGs) of GC, and construct a gene signature to predict the overall survival (OS) of patients. Gene expression profiles and clinical data of GC patients were downloaded from TCGA and GEO databases. Combined with immune-related genes (IRGs) downloaded from the ImmPort database, 357 DEIRGs in GC tissues and adjacent tissues were identified. Based on the analysis of Lasso and Cox in the training set, a prognostic risk scoring model consisting of 9 (RBP7, DES, CCR1, PNOC, SPP1, VIP, TNFRSF12A, TUBB3, PRKCG) DEIRGs was obtained. Functional analysis revealed that model genes may participate in the formation and development of tumor cells by affecting the function of cell gap junction intercellular communication (GJJC). According to the model score, the samples were divided into high-risk and low-risk groups. In multivariate Cox regression analysis, the risk score was an independent prognostic factor (HR = 1.674, 95% CI = 1.470–1.907, P < 0.001). Survival analysis showed that the OS of high-risk GC patients was significantly lower than that of low-risk GC patients (P < 0.001). The area under the receiver operating characteristic curve (ROC) of the model was greater than other clinical indicators when verified in various data sets, confirming that the prediction model has a reliable accuracy. In conclusion, this study has explored the biological functions of DEIRGs in GC and discovered novel gene targets for the treatment of GC. The constructed prognostic gene signature is helpful for clinicians to determine the prognosis of GC patients and formulate personalized treatment plans.

GNG7 and ADCY1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma through bioinformatic-based analyses

Pancreatic adenocarcinoma (PAAD) is one of the most lethal malignant tumors in the world. The GSE55643 and GSE15471 microarray datasets were downloaded to screen the diagnostic and prognostic biomarkers for PAAD. 143 downregulated genes and 118 upregulated genes were obtained. Next, we performed gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these genes and constructed a protein-protein interaction (PPI) network. We screened out two important clusters of genes, including 13 upregulated and 5 downregulated genes. After the survival analysis, 3 downregulated genes and 10 upregulated genes were identified as the selected key genes. The KEGG analysis on 13 selected genes showed that GNG7 and ADCY1 enriched in the Pathway in Cancer. Next, the diagnostic and prognostic value of GNG7 and ADCY1 was investigated using independent cohort of the Cancer Genome Atlas (TCGA), GSE84129 and GSE62452. We observed that the expression of the GNG7 and ADCY1 was decreased in PAAD. The diagnostic receiver operating characteristic (ROC) analysis indicated that the GNG7 and ADCY1 could serve as sensitive diagnostic markers in PAAD. Survival analysis suggested that expression of GNG7, ADCY1 were significantly associated with PAAD overall survival (OS). The multivariate cox regression analysis showed that the expression of GNG7, ADCY1 were independent risk factors for PAAD OS. Our study indicated GNG7 and ADCY1 may be potential diagnostic and prognostic biomarkers in patients with PAAD.

Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.

METHODS

Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.

RESULTS

We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages.

CONCLUSIONS

An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA.

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Background

Colorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.

Methods

The clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.

Results

A total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.

Conclusion

Eight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

Interpreting Sequence Variation in PDAC-Predisposing Genes Using a Multi-Tier Annotation Approach Performed at the Gene, Patient, and Cohort Level

We investigated germline variation in pancreatic ductal adenocarcinoma (PDAC) predisposition genes in 535 patients, using a custom-built panel and a new complementary bioinformatic approach. Our panel assessed genes belonging to DNA repair, cell cycle checkpoints, migration, and preneoplastic pancreatic conditions. Our bioinformatics approach integrated annotations of variants by using data derived from both germline and somatic references. This integrated approach with expanded evidence enabled us to consider patterns even among private mutations, supporting a functional role for certain alleles, which we believe enhances individualized medicine beyond classic gene-centric approaches. Concurrent evaluation of three levels of evidence, at the gene, sample, and cohort level, has not been previously done. Overall, we identified in PDAC patient germline samples, 12% with mutations previously observed in pancreatic cancers, 23% with mutations previously discovered by sequencing other human tumors, and 46% with mutations with germline associations to cancer. Non-polymorphic protein-coding pathogenic variants were found in 18.4% of patient samples. Moreover, among patients with metastatic PDAC, 16% carried at least one pathogenic variant, and this subgroup was found to have an improved overall survival (22.0 months versus 9.8; p=0.008) despite a higher pre-treatment CA19-9 level (p=0.02). Genetic alterations in DNA damage repair genes were associated with longer overall survival among patients who underwent resection surgery (92 months vs. 46; p=0.06). ATM alterations were associated with more frequent metastatic stage (p = 0.04) while patients with BRCA1 or BRCA2 alterations had improved overall survival (79 months vs. 39; p=0.05). We found that mutations in genes associated with chronic pancreatitis were more common in non-white patients (p<0.001) and associated with longer overall survival (52 months vs. 26; p=0.004), indicating the need for greater study of the relationship among these factors. More than 90% of patients were found to have variants of uncertain significance, which is higher than previously reported. Furthermore, we generated 3D models for selected mutant proteins, which suggested distinct mechanisms underlying their dysfunction, likely caused by genetic alterations. Notably, this type of information is not predictable from sequence alone, underscoring the value of structural bioinformatics to improve genomic interpretation. In conclusion, the variation in PDAC predisposition genes appears to be more extensive than anticipated. This information adds to the growing body of literature on the genomic landscape of PDAC and brings us closer to a more widespread use of precision medicine for this challenging disease.

Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method

Cancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis. Traditional methods of identifying cancer prognostic markers may not be accurate. Tensor decomposition (TD) is a useful method uncovering the underlying low-dimensional structures in the tensor. The TD-based unsupervised feature extraction method was applied to analyse mRNA and miRNA expression profiles. Biological annotations of the prognostic miRNAs and mRNAs were examined utilizing the pathway and oncogenic signature databases DIANA-miRPath and MSigDB. TD identified the miRNA signatures and the associated genes. These genes were found to be involved in cancer-related pathways, and 23 genes were significantly correlated with the survival of KIRC patients. We demonstrated that the results are robust and not highly dependent upon the databases we selected. Compared with traditional supervised methods tested, TD achieves much better performance in selecting prognostic miRNAs and mRNAs. These results suggest that integrated analysis using the TD-based unsupervised feature extraction technique is an effective strategy for identifying prognostic signatures in cancer studies.

Prognostic value of microRNAs in pancreatic cancer: a meta-analysis

BACKGROUND

The prognostic impact of microRNA (miRNA) expression levels in pancreatic cancer (PC) has been estimated for years, but the outcomes are controversial and heterogeneous. Therefore, we comprehensively reviewed the evidence collected on miRNA expression in PC to determine this effect.

RESULTS

PC patients with high miR-21 (HR=2.61, 95%CI=1.68-4.04), miR-451a (HR=2.23, 95%CI=1.23-4.04) or miR-1290 (HR=1.43, 95%CI=1.04-1.95) levels in blood had significantly poorer OS (P<0.05). Furthermore, PC patients with high miR-10b (HR=1.73, 95%CI=1.09-2.76), miR-17-5p (HR=1.91, 95%CI=1.30-2.80), miR-21 (HR=1.90, 95%CI=1.61-2.25), miR-23a (HR=2.18, 95%CI=1.52-3.13), miR-155 (HR=2.22, 95%CI=1.27-3.88), miR-203 (HR=1.65, 95%CI=1.14-2.40), miR-221 (HR=1.72, 95%CI=1.08-2.74), miR-222 levels (HR=1.72, 95%CI=1.02-2.91) or low miR-29c (HR=1.39, 95%CI=1.08-1.79), miR-126 (HR=1.55, 95%CI=1.23-1.95), miR-218 (HR=2.62, 95%CI=1.41-4.88) levels in tissues had significantly shorter OS (P<0.05).

CONCLUSIONS

In summary, blood miR-21, miR-451a, miR-1290 and tissue miR-10b, miR-17-5p, miR-21, miR-23a, miR-29c, miR-126, miR-155, miR-203, miR-218, miR-221, miR-222 had significant prognostic value.

METHODS

We searched PubMed, EMBASE, Web of Science and Cochrane Database of Systematic Reviews to recognize eligible studies, and 57 studies comprising 5445 PC patients and 15 miRNAs were included to evaluate the associations between miRNA expression levels and overall survival (OS) up to June 1, 2019. Summary hazard ratios (HR) with 95% confidence intervals (CI) were calculated to assess the effect.

Prognostic and predictive factors in pancreatic cancer

Pancreatic cancer is one of the leading causes of cancer death worldwide. Its high mortality rate has remained unchanged for years. Radiotherapy and surgery are considered standard treatments in early and locally advanced stages. Chemotherapy is the only option for metastatic patients. Two treatment regimens, i. e. the association of 5-fluorouracil- irinotecan-oxaliplatin (FOLFIRINOX) and the association of nab-paclitaxel with gemcitabine, have been shown to improve outcomes for metastatic pancreatic adenocarcinoma patients. However, there are not standardized predictive biomarkers able to identify patients who benefit most from treatments. CA19-9 is the most studied prognostic biomarker, its predictive role remains unclear. Other clinical, histological and molecular biomarkers are emerging in prognostic and predictive settings. The aim of this review is to provide an overview of prognostic and predictive markers used in clinical practice and to explore the most promising fields of research in terms of treatment selection and tailored therapy in pancreatic cancer.

Identification of prognostic splicing factors and exploration of their potential regulatory mechanisms in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD), the most common subtype of pancreatic cancer, is a highly lethal disease. In this study, we integrated the expression profiles of splicing factors (SFs) of PAAD from RNA-sequencing data to provide a comprehensive view of the clinical significance of SFs. A prognostic index (PI) based on SFs was developed using the least absolute shrinkage and selection operator (LASSO) COX analysis. The PI exhibited excellent performance in predicting the status of overall survival of PAAD patients. We also used the percent spliced in (PSI) value obtained from SpliceSeq software to quantify different types of alternative splicing (AS). The prognostic value of AS events was explored using univariate COX and LASSO COX analyses; AS-based PIs were also proposed. The integration of prognosis-associated SFs and AS events suggested the potential regulatory mechanisms of splicing processes in PAAD. This study defined the markedly clinical significance of SFs and provided novel insight into their potential regulatory mechanisms.

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma

Purpose

Hepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC.

Methods

We selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model.

Results

GSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely, ADA, FEN1, POLR2G, SAC3D1, SEC61A1, SF3A3, and UPF3B, were significantly a ssociated with overall survival (OS) and used to form a gene signature. The signature risk score was calculated and used to divide patients into high‐ and low‐risk groups. The high-risk group showed worse prognosis (log-rank test p<0.0001). Univariate and multivariate Cox regression analysis showed that the prognostic performance of this risk score signature was robust in different subgroups based on clinicopathological features, with p-values <0.05 (HR=2.38, 95% CI (confidence interval) =1.355–4.184), indicating that it can serve as an independent prognostic indicator.

Conclusion

We developed and identified a seven‐gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.

A seven-miRNA expression-based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer

The present study aimed to establish a microRNA (miRNA/miR) signature to predict the prognosis of patients with pancreatic cancer (PC) at the early stage and to investigate the involvement of competing endogenous RNAs (ceRNAs) in PC. Using mature miRNA expression profiles from The Cancer Genome Atlas, differentially expressed miRNAs in tissues derived from patients exhibiting early PC and tissues from healthy individuals were compared. The least absolute shrinkage and selection operator regression method was used to construct a miRNA-based signature for predicting prognosis. The miRNet tool, gene set enrichment analysis (GSEA) and the LncRNADisease database were utilized to explore the mechanistic involvement of ceRNAs. A total of seven downregulated miRNAs in PC (miR-424-5p, miR-139-5p, miR-5586-5p, miR-126-3p, miR-3613-5p, miR-454-3p and miR-1271-5p) were selected to generate a signature. Based on this seven-miRNA signature, it was possible to stratify patients with PC into low- and high-risk groups. The overall survival of the low-risk group was significantly longer than that of the high-risk group (P<0.001). The seven-miRNA signature was able to predict the 2-year-survival rate of patients with early PC with an area under the curve of 0.750. Furthermore, as opposed to routine clinicopathological features, this seven-miRNA signature was an independent prognostic factor according to multivariate Cox regression analysis. GSEA indicated that the extracellular matrix receptor interaction pathway and the transforming growth factor-β signaling pathway were enriched in the high-risk group. A ceRNA network of the seven-miR signature was constructed. In conclusion, the present study provided a seven-miRNA signature, according to which patients with early PC may be divided into high- and low-risk groups. The ceRNA network of the prognostic signature was preliminarily explored.