Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas

HNRNPL facilitates ferroptosis in hepatocellular carcinoma cells by promoting S100A9 expression

OBJECTIVE

This study probed into the effect of HNRNPL on ferroptosis in hepatocellular carcinoma (HCC) cells and related molecular mechanisms.

METHODS

Expression patterns of HNRNPL, Recombinant S100 Calcium Binding Protein A9 (S100A9) were analyzed in HCC tissues or cells. Following transfection, HCC cell activity was analyzed, followed by detection of levels of ROS, iron content, LPO, MDA, and GSH as well as the expression of ferroptosis-related proteins. For molecular mechanism, RIP, RNA pull-down assay and actinomycin D assay were implemented to verify the binding relationship between HNRNPL and S100A9. Finally, in vivo nude mouse xenograft tumor experiments were performed for further validate the crucial role of HNENPL expression in HCC.

RESULTS

HNRNPL and S100A9 were significantly overexpressed in HCC. sh-HNRNPL treatment led to a significant decrease in cellular activity, GSH content, and expression of GPX4 and SLC7A11, and a significant increase in iron content, LPO level, MDA, ROS content, and expression of ACSL4 and TFR1. In addition, after sh-HNRNPL was combined with oe-S100A9 or Fer-1, a ferroptosis inhibitor, both oe-S100A9 and Fer-1 reversed the promotional effect of sh-HNRNPL on ferroptosis of HCC cells when sh-HNRNPL acted alone. Mechanically, HNRNPL promoted S100A9 mRNA stability and expression through RBP. Furthermore, low expression of HNRNPL in vivo delayed the growth of xenograft tumors and the expression of ferroptosis-related proteins.

CONCLUSION

HNRNPL promotes S100A9 mRNA stability and expression through RBP action, thereby promoting ferroptosis in HCC cells.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

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.

Identification of prognostic alternative splicing events related to the immune microenvironment of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world, and its 5-year survival rate is less than 20%, despite various treatments being available. Increasing evidence indicates that alternative splicing (AS) plays a nonnegligible role in the formation and development of the tumor microenvironment (TME). However, the comprehensive analysis of the impact on prognostic AS events on immune-related perspectives in HCC is lacking but urgently needed.

Methods

The transcriptional data and clinical information of HCC patients were downloaded from TCGA (The Cancer Genome Atlas) database for calculating immune and stromal scores by ESTIMATE algorithm. We then divided patients into high/low score groups and explored their prognostic significance using Kaplan–Meier curves. Based on stromal and immune scores, differentially expressed AS events (DEASs) were screened and evaluated with functional enrichment analysis. Additionally, a risk score model was established by applying univariate and multivariate Cox regression analyses. Finally, gene set variation analysis (GSVA) was adopted to explore differences in biological behaviors between the high- and low-risk subgroups.

Results

A total of 370 HCC patients with complete and qualified corresponding data were included in the subsequent analysis. According to the results of ESTIMATE analysis, we observed that the high immune/stromal score group had a longer survival probability, which was significantly correlated with prognosis in HCC patients. In addition, 467 stromal/immune score-related DEASs were identified, and enrichment analysis revealed that DEASs were significantly enriched in pathways related to HCC tumorigenesis and the immune microenvironment. More importantly, the final prognostic signature containing 16 DEASs showed powerful predictive ability. Finally, GSVA demonstrated that activation of carcinogenic pathways and immune-related pathways in the high-risk group may lead to poor prognosis.

Conclusions

Collectively, these outcomes revealed prognostic AS events related to carcinogenesis and the immune microenvironment, which may yield new directions for HCC immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00294-3.

Alternative RNA Splicing in Fatty Liver Disease

Alternative RNA splicing is a process by which introns are removed and exons are assembled to construct different RNA transcript isoforms from a single pre-mRNA. Previous studies have demonstrated an association between dysregulation of RNA splicing and a number of clinical syndromes, but the generality to common disease has not been established. Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease affecting one-third of adults worldwide, increasing the risk of cirrhosis and hepatocellular carcinoma (HCC). In this review we focus on the change in alternative RNA splicing in fatty liver disease and the role for splicing regulation in disease progression.

Construction of an RNA-Binding Protein-Related Prognostic Model for Pancreatic Adenocarcinoma Based on TCGA and GTEx Databases

Background: Recently, RNA-binding proteins (RBPs) were reported to interact with target mRNA to regulate gene posttranscriptional expression, and RBP-mediated RNA modification can regulate the expression and function of proto-oncogenes and tumor suppressor genes. We systematically analyzed the expression of RBPs in pancreatic adenocarcinoma (PAAD) and constructed an RBP-associated prognostic risk model. Methods: Gene expression data of normal pancreatic samples as well as PAAD samples were downloaded from TCGA-PAAD and GTEx databases. Wilcoxon test and univariate Cox analysis were, respectively, applied to screen differential expression RBPs (DE-RBPs) and prognostic-associated RBPs (pRBPs). Functional enrichment was analyzed by GO, KEGG, and GSEA. Protein-protein interaction (PPI) network was constructed by STRING online database. Modeling RBPs were selected by multivariate Cox analysis. Kaplan-Meier survival and Cox analysis were applied to evaluate the effects of risk score on the overall survival of PAAD patients. ROC curves and validation cohort were applied to verify the accuracy of the model. Nomogram was applied for predicting 1-, 3-, and 5-year overall survival (OS) of PAAD patients. At last, modeling RBPs were further analyzed to explore their differential expression, prognostic value, as well as enrichment pathways in PAAD. Results: RBPs (453) were differentially expressed in normal and tumor samples, besides, 28 of which were prognostic associated. DE-RBPs (453) are functionally associated with ribosome, ribonuclease, spliceosome, etc. Eight RBPs (PABPC1, PRPF6, OAS1, RBM5, LSM12, IPO7, FXR1, and RBM6) were identified to construct a prognostic risk model. Higher risk score not only predicted poor prognosis but also was an independent poor prognostic indicator, which was verified by ROC curves and validation cohort. Eight modeling RBPs were confirmed to be significantly differentially expressed between normal and tumor samples from RNA and protein level. Besides, all of eight RBPs were related with overall survival of PAAD patients. Conclusions: We successfully constructed an RBP-associated prognostic risk model in PAAD, which has a potential clinical application prospect.

Genome-Wide Analysis of Prognostic Alternative Splicing Signature and Splicing Factors in Lung Adenocarcinoma

Analysis of The Cancer Genome Atlas data revealed that alternative splicing (AS) events could serve as prognostic biomarkers in various cancer types. This study examined lung adenocarcinoma (LUAD) tissues for AS and assessed AS events as potential indicators of prognosis in our cohort. RNA sequencing and bioinformatics analysis were performed. We used SUPPA2 to analyze the AS profiles. Using univariate Cox regression analysis, overall survival (OS)-related AS events were identified. Genes relating to the OS-related AS events were imported into Cytoscape, and the CytoHubba application was run. OS-related splicing factors (SFs) were explored using the log-rank test. The relationship between the percent spliced-in value of the OS-related AS events and SF expression was identified by Spearman correlation analysis. We found 1957 OS-related AS events in 1151 genes, and most were protective factors. Alternative first exon splicing was the most frequent type of splicing event. The hub genes in the gene network of the OS-related AS events were FBXW11, FBXL5, KCTD7, UBB and CDC27. The area under the curve of the MIX prediction model was 0.847 for 5-year survival based on seven OS-related AS events. Overexpression of SFs CELF2 and SRSF5 was associated with better OS. We constructed a correlation network between SFs and OS-related AS events. In conclusion, we identified prognostic predictors using AS events that stratified LUAD patients into high- and low-risk groups. The discovery of the splicing networks in this study provides an insight into the underlying mechanisms.

Clusterization in acute myeloid leukemia based on prognostic alternative splicing signature to reveal the clinical characteristics in the bone marrow microenvironment

Background

Alternative splicing (AS), a crucial post-transcriptional regulatory mechanism in expanding the coding capacities of genomes and increasing the diversity of proteins, still faces various challenges in the splicing regulation mechanism of acute myeloid leukemia (AML) and microenvironmental changes.

Results

A total of 27,833 AS events were detected in 8337 genes in 178 AML patients, with exon skip being the predominant type. Approximately 11% of the AS events were significantly related to prognosis, and the prediction models based on various events demonstrated high classification efficiencies. Splicing factors correlation networks further altered the diversity of AS events through epigenetic regulation and clarified the potential mechanism of the splicing pathway. Unsupervised cluster analysis revealed significant correlations between AS and immune features, molecular mutations, immune checkpoints and clinical outcome. The results suggested that AS clusters could be used to identify patient subgroups with different survival outcomes in AML, among which C1 was both associated with good outcome in overall survival. Interestingly, C1 was associated with lower immune scores compared with C2 and C3, and favorable-risk cytogenetics was rarely distributed in C2, but much more common in C1.

Conclusions

This study revealed a comprehensive landscape of AS events, and provides new insight into molecular targeted therapy and immunotherapy strategy for AML.

Systematic profiling of alternative splicing in Helicobacter pylori-negative gastric cancer and their clinical significance

Background

Alternative splicing (AS) may cause structural and functional variations in the protein to promote the proliferation of tumor cells. However, there is no comprehensive analysis of the clinical significance of AS in Helicobacter pylori-negative gastric cancer (HP⁻ GC).

Methods

The clinical, gene expression profile data and AS events of 138 HP⁻ GC patients were obtained from the database named the cancer genome atlas. Differently expressed AS (DEAS) events were determined by a comparison of the PSI values between HP⁻ GC samples and adjacent normal samples. Unsupervised clustering analysis, proportional regression and Kaplan–Meier analysis were used to explore the association between clinical data and immune features and to establish two nomograms about the prognosis of HP⁻ GC. Finally, splicing networks were constructed using Cytoscape.

Results

A total of 48141 AS events and 1041 DEAS events were found in HP⁻ GC. Various functions and pathways of DEAS events parent genes were enriched, such as cell-substrate junction, cell leading edge, focal adhension, and AMPK signaling. Seven overall survival (OS)-related and seven disease-free survival (DFS)-related AS events were used to construct the prognostic signatures. Based on the independent prognostic factors, two nomograms were established and showed excellent performance. Then, splicing regulatory networks among the correlations suggested that splicing factors were significantly associated with prognostic DEASs. Finally, the unsupervised clustering analysis revealed that DEAS-based clusters were associated with clinical characteristics, tumor microenvironment, tumor mutation burden, and immune features.

Conclusion

Seven OS-related and seven DFS-related AS events have been found to be correlated with the prognosis of HP⁻ GC and can be used as prognostic factors to establish an effective nomogram.

Identification of prognostic alternative splicing signatures in hepatitis B or/and C viruses related hepatocellular carcinoma

BACKGROUND

Alternative splicing (AS) takes a crucial part in tumor process. We aim to analyze AS in Hepatitis B virus (HBV) or/and hepatitis C virus (HCV) related hepatocellular carcinoma (HCC).

METHODS

Cox regression analysis was conducted to screen survival-associated AS events. The receiver operating characteristic curve used to evaluate the predictive accuracy. Splicing network was built to investigate the relationship between splicing factors and AS events.

RESULTS

Ninety-six survival-associated AS events were obtained by univariate Cox regression. Final prognostic model could significantly distinguish the prognosis. We identified RBFOX2 as the hub gene in splicing network based on differentially expressed splicing factors, and obtained MAP3K13_AT as the key AS event in survival-related splicing network.

CONCLUSION

Our results highlight the AS signatures in HCC patients with HBV or/and HCV infection. Meanwhile, AS events and splicing factors in different virus-infected HCC subgroups can provide novel perspectives as biomarkers and individualized therapeutic targets.