Integration of single-nucleus and exosome RNA sequencing dissected inter-cellular communication and biomarkers in pancreatic ductal adenocarcinoma

Background

Mounting evidence underscores the importance of cell communication within the tumor microenvironment, which is pivotal in tumor proliferation, invasion, and metastasis. Exosomes play a crucial role in cell-to-cell communication. Although single-cell RNA sequencing (scRNA-seq) provides insights into individual cell transcriptional characteristics, it falls short of comprehensively capturing exosome-mediated intercellular communication.

Method

We analyzed Pancreatic Ductal Adenocarcinoma (PDAC) tissues, separating supernatant and precipitate for exosome purification and single-cell nucleus suspension. We then constructed Single-nucleus RNA sequencing (snRNA-seq) and small RNA-seq libraries from these components. Our bioinformatic analysis integrated these sequences with ligand-receptor analysis and public miRNA data to map the cell communication network.

Results

We established intercellular communication networks using bioinformatic analysis to track exosome miRNA effects and ligand-receptor pairs. Significantly, hsa-miR-1293 emerged as a prognostic biomarker for pancreatic cancer, linked to immune evasion, increased myeloid-derived suppressor cells, and poorer prognosis. Targeting this miRNA may enhance anti-tumor immunity and improve outcomes.

Conclusion

Our study offers a novel approach to constructing intercellular communication networks using snRNA-seq and exosome-small RNA sequencing. By integrating miRNA tracing with ligand-receptor analysis, we illuminate the complex interactions in the pancreatic cancer microenvironment, highlighting the pivotal role of miRNAs and identifying potential biomarkers and therapeutic targets.

Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study

Background

Chronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population.

Patients and methods

The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors.

Results

Cox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155-1.348]); albumin (HR 0.952 [95% CI 0.931-0.974); age (HR 1.033 [95% CI 1.022-1.044]); globulin (HR 1.057 [95% CI 1.038-1.077]); smoking years (HR 1.011 [95% CI 1.006-1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007-1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010-1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017-1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the "Survival Curve Prediction System for COPD patients", was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time.

Conclusion

The current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).

Application of miRNA Biomarkers in Predicting Overall Survival Outcomes for Lung Adenocarcinoma

Background

With the development of research, the importance of microRNAs (miRNAs) in the occurrence, metastasis, and prognosis of lung adenocarcinoma (LUAD) has attracted extensive attention. This study is aimed at predicting overall survival (OS) results through bioinformatics to identify novel miRNA biomarkers and hub genes.

Materials and Methods

The data of LUAD-related miRNA and mRNA samples was downloaded from The Cancer Genome Atlas (TCGA) database. Upon screening and pretreatment of initial data, TCGA data were analyzed using R platform and a series of analytical tools to identify biomarkers with high specificity and sensitivity.

Results

7 miRNAs and 13 hub genes that had strong relation to the overall surviving status were identified in patients with LUAD. The expression of seven miRNAs (hsa-miR-19a-3p, hsa-miR-126-5p, hsa-miR-556-3p, hsa-miR-671-5p, hsa-miR-937-3p, hsa-miR-4664-3p, and hsa-miR-4746-5p) could apparently improve the OS rate of patient with LUAD. The 13 hub genes, namely, CCT6A, CDK5R1, CEP55, DNAJB4, EGLN3, HDGF, HOXC8, LIMD1, MKI67, PCP4L1, PPIL1, SCAI, and STK32A, showed a correlation with the OS status.

Conclusion

7 miRNAs were identified as novel biomarkers for the prognosis of patients with LUAD. This study offered a deeper comprehension of LUAD treatment and prognosis from the molecular level and helped enhance the understanding of the pathogenesis and potential molecular events of LUAD.

Six MicroRNA Prognostic Models for Overall Survival of Lung Adenocarcinoma

Objective

The purpose of this study is to screen for microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) and to explore its prognosis and effects on the tumor microenvironment in patients with LUAD.

Methods

Gene expression data, miRNA expression data, and clinical data for two different databases, TCGA-LUAD and CPTAC-3 LUAD, were downloaded from the GDC database. The miRNA prognosis of LUAD was filtered by the Cox proportional hazard model and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The performance of the model was validated by time-dependent receiver operating characteristics (ROC) curves. Possible biological processes associated with the miRNAs target gene were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the prognostic model was scored by risk, divided into high- and low-risk groups by median, and the differences in the immersion level of 21 immune cells in the high- and low-risk groups were assessed. To gain a deeper understanding of the underlying mechanism behind the model, the two most important miRNAs in the model, miR-195-3p and miR-5571-5p, were selected for HPA database validation and ceRNA network construction.

Results

Of the 209 variance expressions identified in the screening analysis, 145 were upregulated and 64 were downregulated by miRNAs. The prognostic models of six miRNA genes were obtained: miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293. These six genes were significantly associated with survival rates in LUAD patients. In particular, miR-1293, miR-195-3p, and miR-5571-5p are highly correlated with OS. The higher expression of miR-195-3p and miR-5571-5p, the better survival of LUAD OS is, and these two miRNA expressions contribute the most to the model. Finally, after sorting the risk scores calculated from low to high using the prognostic model, the patients with higher scores had shorter survival time and higher frequency of death, and there were significant differences in the immersion levels of 21 immune cells in the high- and low-risk groups. ceRNA network analysis found that TM9SF3 was regulated by miR-195-3p and was highly expressed in the tissues of LUAD patients, and the prognosis of the patients was poor.

Conclusions

miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293 may be used as new biomarkers for prognosis prediction of LUAD. Our results also identified a lncRNA MEG3/miR-195-3p/RAB1A/TM9SF3 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.

Comprehensive Analyses of Mutation-Derived Long-Chain Noncoding RNA Signatures of Genome Instability in Kidney Renal Papillary Cell Carcinoma

Background: The role of long-chain noncoding RNA (lncRNA) in genomic instability has been demonstrated to be increasingly importance. Therefore, in this study, lncRNAs associated with genomic instability were identified and kidney renal papillary cell carcinoma (KIRP)-associated predictive features were analysed to classify high-risk patients and improve individualised treatment.

Methods: The training (n = 142) and test (n = 144) sets were created using raw RNA-seq and patient’s clinical data of KIRP obtained from The Cancer Genome Atlas (TCGA).There are 27 long-chain noncoding RNAs (lncRNAs) that are connected with genomic instability, these lncRNAs were identified using the ‘limma’ R package based on the numbers of somatic mutations and lncRNA expression profiles acquired from KIRP TCGA cohort. Furthermore, Cox regression analysis was carried out to develop a genome instability-derived lncRNA-based gene signature (GILncSig), whose prognostic value was confirmed in the test cohort as well as across the entire KIRP TCGA dataset.

Results: A GILncSig derived from three lncRNAs (BOLA3-AS1, AC004870, and LINC00839), which were related with poor KIRP survival, was identified, which was split up into high- and low-risk groups. Additionally, the GILncSig was found to be an independent prognostic predictive index in KIRP using univariate and multivariate Cox analysis. Furthermore, the prognostic significance and characteristics of GilncSig were confirmed in the training test and TCGA sets. GilncSig also showed better predictive performance than other prognostic lncRNA features.

Conclusion: The function of lncRNAs in genomic instability and the genetic diversity of KIRP were elucidated in this work. Moreover, three lncRNAs were screened for prediction of the outcome of KIRP survival and novel insights into identifying cancer biomarkers related to genomic instability were discussed.

A 16-miRNA Prognostic Model to Predict Overall Survival in Neuroblastoma

Neuroblastoma is the most malignant childhood tumor. The outcome of neuroblastoma is hard to predict due to the limitation of prognostic markers. In our study, we constructed a 16-miRNA prognostic model to predict the overall survival of neuroblastoma patients for early diagnosis. A total of 205 DE miRNAs were screened using RNA sequencing data from GSE121513. Lasso Cox regression analysis generated a 16-miRNA signature consisting of hsa-let-7c, hsa-miR-135a, hsa-miR-137, hsa-miR-146a, hsa-miR-149, hsa-miR-15a, hsa-miR-195, hsa-miR-197, hsa-miR-200c, hsa-miR-204, hsa-miR-302a, hsa-miR-331, hsa-miR-345, hsa-miR-383, hsa-miR-93, and hsa-miR-9star. The concordance index of multivariate Cox regression analysis was 0.9, and the area under the curve (AUC) values of 3-year and 5-year survival were 0.92 and 0.943, respectively. The mechanism was further investigated using the TCGA and GSE90689 datasets. Two miRNA-gene interaction networks were constructed among DEGs from two datasets. Functional analysis revealed that immune-related processes were involved in the initiation and metastasis of neuroblastoma. CIBERSORT and survival analysis suggested that lower CD8 T-cell proportion and higher SPTA1 expressions were related to a better prognosis. Our study demonstrated that the miRNA signature may be useful in prognosis prediction and management improvement.

A construction and comprehensive analysis of ceRNA networks and infiltrating immune cells in papillary renal cell carcinoma

Background

As the second most common malignancy in adults, papillary renal cell carcinoma (PRCC) has shown an increasing trend in both incidence and mortality. Effective treatment for advanced metastatic PRCC is still lacking. In this study, we aimed to establish competitive endogenous RNA (ceRNA) networks related to PRCC tumorigenesis, and analyze the specific role of differentially expressed ceRNA components and infiltrating immune cells in tumorigenesis.

Methods

CeRNA networks were established to identify the key ceRNAs related to PRCC tumorigenesis based on the 318 samples from The Cancer Genome Atlas database (TCGA), including 285 PRCC and 33 normal control samples. The R package, “CIBERSORT,” was used to evaluate the infiltration of 22 types of immune cells. Then we identified the significant ceRNAs and immune cells, based on which two nomograms were obtained for predicting the prognosis in PRCC patients. Finally, we investigated the co‐expression of PRCC‐specific immune cells and core ceRNAs via Pearson correlation test.

Results

COL1A1, H19, ITPKB, LDLR, TCF4, and WNK3 were identified as hub genes in ceRNA networks. Four prognostic‐related tumor‐infiltrating immune cells, including T cells CD4 memory resting, Macrophages M1, and Macrophages M2 were revealed. Pearson correlation test indicated that Macrophage M1 was negatively related with COL1A1 (p < 0.01) and LDLR (p < 0.01), while Macrophage M2 was positively related with COL1A1 (p < 0.01), TCF4 (p < 0.01), and H19 (p = 0.032). Two nomograms were conducted with favorable accuracies (area under curve of 1‐year survival: 0.935 and 0.877; 3‐year survival: 0.849 and 0.841; and 5‐year survival: 0.818 and 0.775, respectively).

Conclusion

The study constructed two nomograms suited for PRCC prognosis predicting. Moreover, we concluded that H19‐miR‐29c‐3p‐COL1A1 axis might promote the polarization of M2 macrophages and inhibit M1 macrophage activation through Wnt signaling pathway, collaborating to promote PRCC tumorigenesis and lead to poor overall survival of PRCC patients.

A Review of Recent Research on the Role of MicroRNAs in Renal Cancer

Renal cell carcinoma (RCC) is a most common type of urologic neoplasms; it accounts for 3% of malignant tumors, with high rates of relapse and mortality. The most common types of renal cancer are clear cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal carcinoma (chRCC), which account for 90%, 6–15%, and 2–5%, respectively, of all renal malignancies. Although surgical resection, chemotherapy, and radiotherapy are the most common treatment method for those diseases, their effects remain dissatisfactory. Furthermore, recent research shows that the treatment efficacy of checkpoint inhibitors in advanced RCC patients is widely variable. Hence, patients urgently need a new molecular biomarker for early diagnosis and evaluating the prognosis of RCC. MicroRNAs (miRNAs) belong to a family of short, non-coding RNAs that are highly conserved, have long half-life evolution, and post-transcriptionally regulate gene expression; they have been predicted to play crucial roles in tumor metastasis, invasion, angiogenesis, proliferation, apoptosis, epithelial-mesenchymal transition, differentiation, metabolism, cancer occurrence, and treatment resistance. Although some previous papers demonstrated that miRNAs play vital roles in renal cancer, such as pathogenesis, diagnosis, and prognosis, the roles of miRNAs in kidney cancer are still unclear. Therefore, we reviewed studies indexed in PubMed from 2017 to 2020, and found several studies suggesting that there are more than 82 miRNAs involved in renal cancers. The present review describes the current status of miRNAs in RCC and their roles in progression, diagnosis, therapy targeting, and prognosis of RCC.

pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers

MOTIVATION

Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.

RESULTS

We propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.

AVAILABILITY AND IMPLEMENTATION

pDriver is available at https://github.com/pvvhoang/pDriver.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated Analysis Revealed the MicroRNA-Based Prognostic Predicting Signature for Papillary Renal Cell Carcinoma

Renal cell carcinoma (RCC) is one of the most frequently occurring tumors worldwide. Herein, we established a microRNA (miRNA) predicting signature to assess the prognosis of papillary-type RCC (PRCC) patients. miR-1293, miR-34a, miR-551b, miR-937, miR-299, and miR-3199-2 were used in building the overall survival (OS)-related signature, whereas miR-7156, miR-211, and miR-301b were used to construct the formula of recurrence-free survival (RFS) with the help of LASSO Cox regression analysis. The Kaplan-Meier and receiver operating characteristic curves indicated good discrimination and efficiency of the two signatures. Functional annotation for the downstream genes of the OS/RFS-related miRNAs exposed the potential mechanisms of PRCC. Notably, the multivariate analyses suggested that the two signatures were independent risk factors for PRCC patients and had better prognostic capacity than any other classifier. In addition, the nomogram indicated synthesis effects and showed better predictive performance than clinicopathologic features and our signatures. We validated the OS and RFS prediction formulas in clinical samples and met our expectations. Finally, we established two novel miRNA-based OS and RFS predicting signatures for PRCC, which are reliable tools for assessing the prognosis of PRCC patients.

cg04448376, cg24387542, cg08548498, and cg14621323 as a Novel Signature to Predict Prognosis in Kidney Renal Papillary Cell Carcinoma

INTRODUCTION

DNA methylation plays a vital role in prognosis prediction of cancers. In this study, we aimed to identify novel DNA methylation site biomarkers and create an efficient methylated site model for predicting survival in kidney renal papillary cell carcinoma (KIRP).

METHODS

DNA methylation and gene expression profile data were downloaded from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Differential methylated genes (DMGs) and differential expression genes (DEGs) were identified and then searched for the hub genes. Cox proportional hazards regression was applied to identify DNA methylated site biomarkers from the hub genes. Kaplan-Meier survival and ROC analyses were used to validate the effective prognostic ability of the methylation gene site biomarker. The biomarker sites were validated in the GEO cohorts. The GO and KEGG annotation was done to explore the biological function of DNA methylated site signature.

RESULTS

Nine DMGs with opposite expression patterns containing 47 methylated sites were identified. Finally, four methylated sites were identified using the hazard regression model (cg04448376, cg24387542, cg08548498, and cg14621323) located in UTY, LGALS9B, SLPI, and PFN3, respectively. These sites classified patients into high- and low-risk groups in the training cohort. The 5-year survival rates for patients with low-risk and high-risk scores were 97.5% and 75.9% (P < 0.001). The prognostic accuracy and signature methylation sites were validated in the test (TCGA, n = 87) and GEO cohorts (n = 14). Multivariate regression analysis showed that the signature was an independent prediction prognostic factor for KIRP. Based on this analysis, we developed methylated site signature nomogram that predicts an individual's risk of survival. Functional analysis suggested that these signature genes are involved in the biological processes of protein binding.

CONCLUSIONS

Our study demonstrated that the methylated gene site signature might be a powerful prognostic tool for evaluating survival rate and guiding tailored therapy for KIRP patients.

circ_001504 promotes the development of renal cell carcinoma by sponging microRNA-149 to increase NUCB2

Renal cell carcinoma (RCC) accounts for over 90% of primary renal tumors in adults. Although treatment approaches have steadily improved over the years, the prognosis outcome remains poor. With the aim of developing novel targets for RCC treatment, we explored the role of the circular RNA (circRNA) circ_001504 in the progression of RCC. We initially detected the expression of circ_001504 and microRNA (miRNA)-149 in RCC tissues and cells. RT-qPCR results showed that circ_001504 was highly expressed in RCC tissues, whereas miR-149 was poorly expressed. Interestingly, downregulation of circ_001504 suppressed malignant phenotypes in RCC cells, and upregulation of miR-149 exerted a similar effect. Bioinformatics analysis suggested potential binding sites between circ_001504 and miR-149, verified by a dual-luciferase reporter gene assay. Next, we identified nucleobindin 2 (NUCB2), a calcium-binding protein, as a target gene of miR-149. Furthermore, our data suggested that circ_001504 might serve as a competing endogenous RNA of miR-149, serving to elevate the expression of NUCB2. The silencing of circ_001504 resulted in decreased NUCB2 expression, which could be reversed by miR-149 inhibition. In addition, in vivo experiments demonstrated that circ_001504 depletion could suppress tumor growth in an established mouse RCC model. Collectively, reduced expression of circ_001504 lowered NUCB2 expression by sponging miR-149, thereby attenuating RCC progression, providing insight into circ_001504/miR-149/NUCB2 feedback loop into RCC treatment.

miR-1293 Suppresses Tumor Malignancy by Targeting Hydrocyanic Oxidase 2: Therapeutic Potential of a miR-1293/Hydrocyanic Oxidase 2 Axis in Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a common cancer, and extensive research suggests that microRNA may play an important role in the progression of RCC. The emphasis of this article was to reveal the function and mechanism of microRNA-1293(miR-1293) in the development of RCC tumors. First, the authors carried out bioinformatics analysis. The differential expression of miR-1293 in RCC tumor and normal cells was analyzed using the data from The Cancer Genome Atlas database, and Kaplan-Meier survival analysis was carried out to test the survival rate. Subsequently, the miR-1293 expression in RCC cell lines was examined by quantitative real-time PCR. Then Cell counting kit-8 and Transwell assays were executed to detect the function of miR-1293 in RCC. Bioinformatics prediction, western blotting, and dual-luciferase reporter assay were set to check the target gene of miR-1293. Finally, they conducted rescue experiments to verify whether the regulation of miR-1293 on the biological function of RCC cells was achieved by regulating hydrocyanic oxidase 2 (HAO2). Bioinformatics results showed that miR-1293 was highly expressed in RCC, and the miR-1293 high-expression group showed a lower survival rate than the miR-1293 low-expression group, which suggested that the high expression of miR-1293 was related to unfavorable prognosis in RCC. Subsequent assays evidenced that upregulation of miR-1293 expression significantly increased the cell viability and promoted cell migration and invasion in RCC. Silencing miR-1293 expression showed opposite results. Furthermore, HAO2 was confirmed to be a direct target gene of miR-1293 by dual-luciferase reporter assay, and miR-1293 negatively regulated the expression of HAO2. Moreover, rescue experiments evidenced that miR-1293 reduced the cell viability, invasion, and migration of RCC by regulating HAO2. In sum, miR-1293 can regulate the viability, invasion, and migration of RCC tumor cells by targeting HAO2, suggesting that miR-1293 can be used as a new biomarker for clinical treatment of RCC.

Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

Background

Increasing studies have suggested that aberrant expression of microRNAs might play essential roles in the progression of cancers. In this study, we sought to construct a high-specific and superior microRNAs signature to improve the survival prediction of colon adenocarcinoma (COAD) patients.

Methods

The genome-wide miRNAs, mRNA and lncRNA expression profiles and corresponding clinical information of COAD were collected from the TCGA database. Differential expression analysis, Kaplan–Meier curve and time-dependent ROC curve were calculated and performed using R software and GraphPad Prism7. Univariate and multivariate Cox analysis was performed to evaluate the prognostic ability of signature. Functional enrichment analysis was analyzed using STRING database.

Results

We identified ten prognosis-related microRNAs, including seven risky factors (hsa-miR-197, hsa-miR-32, hsa-miR-887, hsa-miR-3199-2, hsa-miR-4999, hsa-miR-561, hsa-miR-210) and three protective factors (hsa-miR-3917, hsa-miR-3189, hsa-miR-6854). The Kaplan–Meier survival analysis showed that the patients with high risk score had shorter overall survival (OS) in test series. And the similar results were observed in both validation and entire series. The time-dependent ROC curve suggested this signature have high accuracy of OS for COAD. The Multivariate Cox regression analysis and stratification analysis suggested that the ten-microRNA signature was an independent factor after being adjusted with other clinical characteristics. In addition, we also found microRNA signature have higher AUC than other signature. Furthermore, we identified some miRNA-target genes that affect lymphatic metastasis and invasion of COAD patients.

Conclusion

In this study, we established a ten-microRNA signature as a potentially reliable and independent biomarker for survival prediction of COAD patients.

Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a relatively rare malignancy of the urinary tract system. RCC is a heterogenous disease in terms of underlying histology and its associated underlying pathobiology, prognosis and treatment schedule. The most prevalent histological RCC subtype is clear-cell renal cell carcinoma (ccRCC), accounting for about 70-80% of all RCCs. Though the pathobiology and treatment schedule for ccRCC are well-established, non-ccRCC subtypes account for 20%-30% of RCC altogether, and their underlying molecular biology and treatment options are poorly defined. The class of non-coding RNAs-molecules that are generally not translated into proteins-are new cancer drivers and suppressors in all types of cancer. Of these, small non-coding microRNAs (miRNAs) contribute to carcinogenesis by regulating posttranscriptional gene silencing. Additionally, a growing body of evidence supports the role of long non-coding RNAs (lncRNAs) in cancer development and progression. Most studies on non-coding RNAs in RCC focus on clear-cell histology, and there is a relatively limited number of studies on non-ccRCC subtypes. The aim of this review is to give an overview of the current knowledge regarding the role of non-coding RNAs (including short and long non-coding RNAs) in non-ccRCC and to highlight possible implications as diagnostic, prognostic and predictive biomarkers.

Identification of novel prognostic alternative splicing signature in papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is a heterogeneous disease containing multifocal or solitary tumors with an aggressive phenotype. Increasing evidence has indicated the involvement of aberrant splicing variants in renal cell cancer, while systematic profiling of aberrant alternative splicing (AS) in pRCC was lacking and largely unknown. In the current study, comprehensive profiling of AS events were performed based on the integration of pRCC cohort from the Cancer Genome Atlas database and SpliceSeq software. With rigorous screening and univariate Cox analysis, a total of 2077 prognoses AS events from 1642 parent genes were identified. Then, stepwise least absolute shrinkage and selection operator method-penalized Cox regression analyses with 10-fold cross-validation followed by multivariate Cox regression were used to construct the prognostic AS signatures within each AS type. And a final 21 AS event-based signature was proposed which showed potent prognostic capability in stratifying patients into low- and high-risk subgroups (P < .0001). Furthermore, time-dependent receiver operating characteristics curves confirmed that the final AS signature was effective and robust in predicting overall survival for pRCC patients with the area under the curve above 0.9 from 1 to 5 years. In addition, splicing correlation network was built to uncover the potential regulatory pattern among prognostic splicing factors and candidate AS events. Besides, gene set enrichment analysis revealed the involvement of these candidates AS events in tumor-related pathways including extracellular matrix organization, oxidative phosphorylation, and P53 signaling pathways. Taken together, our results could contribute to elucidating the underlying mechanism of AS in the oncogenesis process and broaden the novel field of prognostic and clinical application of molecule-targeted approaches in pRCC.

Identification of miRNA profiling in prediction of tumor recurrence and progress and bioinformatics analysis for patients with primary esophageal cancer: Study based on TCGA database

OBJECT

This study focused on the identification of prognostic miRNAs for the prediction of tumor recurrence and progress in esophageal cancer.

METHODS

MiRNA profiling and clinical characteristics of esophageal cancer patients was downloaded from the TCGA database. Univariate analysis was performed to select potential prognostic miRNAs and covariates. LASSO based logistic regression was conducted to identify the prognostic miRNAs given covariates. Bioinformatics analysis including gene ontology, disease ontology and pathway enrichment analysis were performed. A nomogram was generated based on multivariate logistic regression to illustrate the association between the identified miRNAs and the risk of tumor recurrence and progress.

RESULTS

A total of 1881 miRNAs and 10 clinical characteristics were obtained from TCGA database. 18 miRNAs were finally identified in which 6 miRNAs were identified for the first time to be associated with the tumor recurrence and progress of esophageal cancer given covariates. Bioinformatics analysis suggested that the identified miRNAs were associated with the tumor recurrence and progress of esophageal cancer. The association between identified miRNAs and risk of tumor recurrence and progress were presented in a nomogram.

CONCLUSION

The 6 newly identified miRNAs may be potential biomarkers for the prediction of tumor recurrence and progress of esophageal cancer.

A Five-microRNA Signature for Survival Prognosis in Pancreatic Adenocarcinoma based on TCGA Data

Novel biomarkers for pancreatic adenocarcinoma are urgently needed because of its poor prognosis. Here, by using The Cancer Genome Atlas (TCGA) RNA-seq data, we evaluated the prognostic values of the differentially expressed miRNAs and constructed a five-miRNA signature that could effectively predict patient overall survival (OS). The Kaplan-Meier overall survival curves of two groups based on the five miRNAs were notably different, showing overall survival in 10.2% and 47.8% at five years for patients in high-risk and low-risk groups, respectively. The ROC curve analysis achieved AUC of 0.775, showing good sensitivity and specificity of the five-miRNA signature model in predicting pancreatic adenocarcinoma patient survival risk. The functional enrichment analysis suggested that the target genes of the miRNA signature may be involved in various pathways related to cancer, including PI3K-Akt, TGF-β, and pluripotent stem cell signaling pathways. Finally, we analyzed expression of the five specific miRNAs in the miRNA signature, and validated the reliability of the results in 20 newly diagnosed pancreatic adenocarcinoma patients using qRT-PCR. The expression results of qRT-PCR were consistent with the TCGA results. Taken together, these findings suggested that the five-miRNA signature (hsa-miR-203, hsa-miR-424, hsa-miR-1266 hsa-miR-1293, and hsa-miR-4772) could be used as a prognostic marker for pancreatic adenocarcinoma.