Identification of prognosis-related alternative splicing events in kidney renal clear cell carcinoma

Alternative splicing events in tumor immune infiltration in renal clear cell carcinomas

Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity and dysregulation of AS plays a significant role in tumorigenesis. This study aimed to develop a prognostic signature based on AS and elucidate the role in tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC). The prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan-Meier survival analysis, and proportional hazards model. The context of TIME in ccRCC was also analyzed. Gene and protein expression data of C4orf19 were obtained from ONCOMINE website and Human Protein Altas. Splicing factors (SFs) regulatory networks were visualized. 4431 survival-related AS events in ccRCC were screened. Based on splicing subtypes, eight AS prognostic signatures were constructed. A nomogram with good prognostic prediction was generated. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. C4orf19 was the only gene whose expression levels were downregulated among the prognostic AS-related genes, which is considered as a promising prognostic factor in ccRCC. Potential functions of SFs were determined by splicing regulatory networks. In our study, AS patterns of novel indicators for prognostic prediction of ccRCC were explored. The AS-SF networks provide information of regulatory mechanisms. Players of AS events related to TIME were investigated, which contribute to prognosis monitoring of ccRCC.

The Role of Alternative Splicing in Cancer: Regulatory Mechanism, Therapeutic Strategy, and Bioinformatics Application

[Formula: see text] Alternative splicing (AS) can generate distinct transcripts and subsequent isoforms that play differential functions from the same pre-mRNA. Recently, increasing numbers of studies have emerged, unmasking the association between AS and cancer. In this review, we arranged AS events that are closely related to cancer progression and presented promising treatments based on AS for cancer therapy. Obtaining proliferative capacity, acquiring invasive properties, gaining angiogenic features, shifting metabolic ability, and getting immune escape inclination are all splicing events involved in biological processes. Spliceosome-targeted and antisense oligonucleotide technologies are two novel strategies that are hopeful in tumor therapy. In addition, bioinformatics applications based on AS were summarized for better prediction and elucidation of regulatory routines mingled in. Together, we aimed to provide a better understanding of complicated AS events associated with cancer biology and reveal AS a promising target of cancer treatment in the future.

A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

Background

Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.

Methods

We analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan–Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.

Results

A total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.

Conclusion

Taken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease

Background

Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.

Methods

The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction.

Results

A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively.

Conclusions

This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Clinical predictive model for the 1-year remission probability of IgA vasculitis nephritis

PURPOSE

Early remission of Immunoglobulin A vasculitis nephritis (IgAVN) substantially affects its prognosis. In this work, a multivariate model to predict the 1-year remission probability of patients with IgAVN was developed on the basis of clinical laboratory data.

METHODS

Data of 187 patients with IgAVN confirmed by renal biopsy were retrospectively assessed. Least absolute shrinkage and selection operator regression analysis were conducted to establish a multivariate logistic regression model. A nomogram based on the multivariate logistic regression model was constructed for easy application in clinical practice. Concordance index, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram.

RESULTS

The predictive factors contained in the multivariate model included duration, gender, respiratory infection, arthritis, edema, estimated glomerular filtration rate, 24 h urine protein, uric acid, and renal ultrasound intensity. The area under the curves (AUC) of the nomogram in the training set and testing set were 0.814 and 0.822, respectively, indicating its good predictive ability. Moreover, the DCA curve and CIC revealed its clinical utility.

CONCLUSION

The developed multivariate predictive model combines the clinical and laboratory factors of patients with IgAVN and is useful in the individualized prediction of the 1-year remission probability aid for clinical decision-making during treatment and management of IgAVN.

Systematic analysis of the relationship between ovarian cancer prognosis and alternative splicing

Background

Ovarian cancer(OC) is the gynecological tumor with the highest mortality rate, effective biomarkers are of great significance in improving its prognosis. In recent years, there have been many studies on alternative splicing (AS) events, and the role of AS events in tumor has become a focus of attention.

Methods

Data were downloaded from the TCGA database and Univariate Cox regression analysis was performed to determine AS events associated with OC prognosis.Eight prognostic models of OC were constructed in R package, and the accuracy of the models were evaluated by the time-dependent receiver operating characteristic (ROC) curves.Eight types of survival curves were drawn to evaluate the differences between the high and low risk groups.Independent prognostic factors of OC were analyzed by single factor independent analysis and multi-factor independent prognostic analysis.Again, Univariate Cox regression analysis was used to analyze the relationship between splicing factors(SF) and AS events, and Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed on OS-related SFs to understand the pathways.

Results

Univariate Cox regression analysis showed that among the 15,278 genes, there were 31,286 overall survival (OS) related AS events, among which 1524 AS events were significantly correlated with OS. The area under the time-dependent receiver operating characteristic curve (AUC) of AT and ME were the largest and the RI was the smallest,which were 0.757 and 0.68 respectively. The constructed models have good value for the prognosis assessment of OC patients. Among the eight survival curves, AP was the most significant difference between the high and low risk groups, with a P value of 1.61e − 1.The results of single factor independent analysis and multi-factor independent prognostic analysis showed that risk score calculated by the model and age could be used as independent risk factors.According to univariate COX regression analysis,109 SFs were correlated with AS events and adjusted in two ways: positive and negative.

Conclusions

SFs and AS events can directly or indirectly affect the prognosis of OC patients. It is very important to find effective prognostic markers to improve the survival rate of OC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00866-1.

TF-RBP-AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers

Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF–RBP–AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA–KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF–RBP–AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer.

Nucleolar protein NOP2 could serve as a potential prognostic predictor for clear cell renal cell carcinoma

As an indispensable part for cancer precision medicine, biomarkers and signatures for predicting cancer prognosis and therapeutic benefits were urgently required. The purpose of this study was to investigate the prognostic roles of NOP2 in renal clear cell carcinoma (ccRCC) for overall survival (OS) and its relationships with immunity. NOP2-related gene expression matrix associated with clinical information was obtained from the Cancer Genome Atlas (TCGA) ccRCC dataset and NOP2-related pathways were identified by gene set enrichment analysis (GSEA). Associations among the NOP2 expression and MSI, TMB, TNB, and immunity were also explored. Both the NOP2 mRNA and protein/phosphoprotein had a higher expression in ccRCC tumor tissues than in normal kidney tissues (both P < 0.001) and elevated NOP2 expression was associated with poor OS (P < 0.001). Logistic regression analysis revealed the NOP2 expression was significantly linked to stage, age, grade, N stage, T stage, and M stage (all P < 0.05). Univariate/multivariate Cox hazard regression analysis results indicated that NOP2 was an independent prognostic factor for OS in ccRCC and GSEA revealed five NOP2-related signaling pathways. Nomogram based on NOP2 and eight clinical characteristic parameters (grade, age, stage, gender, T stage, race, M stage, N stage) was constructed and carefully evaluated. Furthermore, NOP2 gene expression was also found to be significantly related to MSI, TMB, and immunity. Our findings revealed that NOP2 might be a potential prognostic factor for OS in ccRCC and it was significantly associated with immunity, MSI, and TMB.

Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma

Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.

Transcriptome-wide analysis and modelling of prognostic alternative splicing signatures in invasive breast cancer: a prospective clinical study

Aberrant alternative splicing (AS) has been highly involved in the tumorigenesis and progression of most cancers. The potential role of AS in invasive breast cancer (IBC) remains largely unknown. In this study, RNA sequencing of IBC samples from The Cancer Genome Atlas was acquired. AS events were screened by conducting univariate and multivariate Cox analysis and least absolute shrinkage and selection operator regression. In total, 2146 survival-related AS events were identified from 1551 parental genes, of which 93 were related to prognosis, and a prognostic marker model containing 14 AS events was constructed. We also constructed the regulatory network of splicing factors (SFs) and AS events, and identified DDX39B as the node SF gene, and verified the accuracy of the network through experiments. Next, we performed quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) in triple negative breast cancer patients with different responses to neoadjuvant chemotherapy, and found that the exon-specific expression of EPHX2, C6orf141, and HERC4 was associated with the different status of patients that received neoadjuvant chemotherapy. In conclusion, this study found that DDX39B, EPHX2 (exo7), and HERC4 (exo23) can be used as potential targets for the treatment of breast cancer, which provides a new idea for the treatment of breast cancer.

Systematic Profiling of Alternative Splicing for Sarcoma Patients Reveals Novel Prognostic Biomarkers Associated with Tumor Microenvironment and Immune Cells

Background

Alternative splicing (AS) events is a novel biomarker of tumor prognosis, but the role of AS events in sarcoma patients remains unclear.

Material/Methods

RNA-seq and clinicopathologic data of the sarcoma cohort were extracted from the TCGA database and data on AS events were downloaded from the TCGASpliceSeq database. Univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were performed to determine the overall survival (OS)- and disease-free survival (DFS)-related AS events. Two nomograms were developed based on the independent variables, and subgroup analysis was performed. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. Then, we used the CIBERSORT and ESTIMATE package to determine the immune cell proportion and tumor microenvironment (TME) score, respectively. The associations between AS events-based clusters and TME and immune cells were studied.

Results

We identified 1945 and 1831 AS events as OS- and DFS-related AS events, respectively. Two nomograms based on the AS events and clinical data were established and the AUCs of nomograms ranged from 0.807 to 0.894. The calibration curve and DCA showed excellent performance of nomograms. In addition, the results indicated the distinct relationships between AS events-based clusters and OS, DFS, immune score, stromal score, and 10 immune cells.

Conclusions

Our study indicated that AS events are novel prognostic biomarkers for sarcoma patients that may be associated with the TME and immune cells.

JAK3 is a potential biomarker and associated with immune infiltration in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers globally, with an overall poor prognosis. The Janus kinase (JAK) family plays an essential role in cellular mechanisms such as proliferation, metastasis, invasion, and immunity. In our study, various web-portals were used to explore the expression and clinical significance of JAK3 in KIRC. JAK3 expression was significantly up-regulated in KIRC tissues. Patients with KIRC having high JAK3 levels displayed a substantially decreased disease-free survival rate and overall survival rate. Significant correlations were obtained between JAK3 expression and the abundance of immune cells and immune biomarker sets. Enrichment function analysis revealed that gene function significantly correlated with JAK3, which was primarily associated with the immune response, JAK-STAT signaling pathway, Ras signaling pathway via several cancer-related kinases, miRNAs, and transcription factors. Moreover, we also identified several kinase, miRNA or transcription factor targets of JAK3 in KIRC. The hub genes (JAK3, FCHO1, INSl3, DEF6, and GPR132) were associated with the activation or inhibition of several famous cancer related pathways. Our results demonstrated that JAK3 is a potential biomarker and associated with immune infiltration in KIRC.

Identification of prognosis-related alternative splicing events in kidney renal clear cell carcinoma

Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, genome‐wide AS profiles were generated in 537 patients with KIRC in the cancer genome atlas. With a total of 42 522 mRNA AS events in 10 600 genes acquired, 8164 AS events were significantly associated with the survival of patients with KIRC. Logistic regression analysis of the least absolute shrinkage and selection operator was conducted to identify an optimized multivariate prognostic predicting mode containing four predictors. In this model, the receptor‐operator characteristic curves of the training set were built, and the areas under the curves (AUCs) at different times were >0.88, thus indicating a stable and powerful ability in distinguishing patients' outcome. Similarly, the AUCs of the test set at different times were >0.73, verifying the results of the training set. Correlation and gene ontology analyses revealed some potential functions of prognostic AS events. This study provided an optimized survival‐predicting model and promising data resources for future in‐depth studies on AS mechanisms in KIRC.