Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma

SRSF3 suppresses RCC tumorigenesis and progression via regulating SP4 alternative splicing

Abnormal alternative splicing (AS) caused by dysregulated expression of splicing factors plays a crucial role in tumorigenesis and progression. The serine/arginine-rich (SR) RNA-binding protein family is a major class of splicing factors regulating AS. However, their roles and mechanisms in renal cell carcinoma (RCC) development and progression are not fully understood. Here, we found that SR splicing factor 3 (SRSF3) was an important splicing factor affecting RCC progression. SRSF3 was downregulated in RCC tissues and its low level was associated with decreased overall survival time of RCC patients. SRSF3 overexpression suppressed RCC cell malignancy. Mechanistically, the binding of SRSF3 to SP4 exon 3 led to the inclusion of SP4 exon 3 and the increase of long SP4 isoform (L-SP4) level in RCC cells. L-SP4, but not S-SP4 overexpression suppressed RCC cell malignancy. Meanwhile, L-SP4 participated in SRSF3-mediated anti-proliferation by transcriptionally promoting SMAD4 expression. Taken together, our findings provide new insights into the anticancer mechanism of SRSF3, suggesting that SRSF3 may serve as a novel potential therapeutic target for RCC.

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Clear cell renal cell carcinoma: immunological significance of alternative splicing signatures

Background

Renal cell carcinoma (RCC) accounts for 90% of renal cancers, of which clear cell carcinoma (ccRCC) is the most usual histological type. The process of alternative splicing (AS) contributes to protein diversity, and the dysregulation of protein diversity may have a great influence on tumorigenesis. We developed a prognostic signature and comprehensively analyzed the role of tumor immune microenvironment (TIME) and immune checkpoint blocking (ICB) treatment in ccRCC.

Methods

To identify prognosis-related AS events, univariate Cox regression was used and functional annotation was performed using gene set enrichment analysis (GSEA). In this study, prognostic signatures were developed based on multivariate Cox, univariate Cox, and LASSO regression models. Moreover, to assess the prognostic value, the proportional hazards model, Kruskal-Wallis analysis, and ROC curves were used. To obtain a better understanding of TIME in ccRCC, the ESTIMATE R package, single sample gene set enrichment analysis (ssGSEA) algorithm, CIBERSORT method, and the tumor immune estimation resource (TIMER) were applied. The database was searched to verify the expression of C4OF19 in tumor and normal samples. Regulatory networks for AS-splicing factors (SFs) were visualized using Cytoscape 3.9.1.

Results

There were 9,347 AS cases associated with the survival of ccRCC patients screened. A total of eight AS prognostic signatures were developed with stable prognostic predictive accuracy based on splicing subtypes. In addition, a qualitative prognostic nomogram was developed, and the prognostic prediction showed high effectiveness. In addition, we found that the combined signature was significantly associated with the diversity of TIME and ICB treatment-related genes. C4ORF19 might become an important prognostic factor for ccRCC. Finally, the AS-SF regulatory network was established to clearly reveal the potential function of SFs.

Conclusion

We found novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.) for the prognostic prediction of ccRCC. A new and reliable prognostic nomogram was established to quantitatively predict the clinical outcome. The AS-SF networks could provide a new way for the study of potential regulatory mechanisms, and the important roles of AS events in the context of TIME and immunotherapy efficiency were exhibited. C4ORF19 was found to be a vital gene in TIME and ICB treatment.

Transcriptional signals of transformation in human cancer

BACKGROUND

As normal cells transform into cancers, their cell state changes, which may drive cancer cells into a stem-like or more primordial, foetal, or embryonic cell state. The transcriptomic profile of this final state may encode information about cancer's origin and how cancers relate to their normal cell counterparts.

METHODS

Here, we used single-cell atlases to study cancer transformation in transcriptional terms. We utilised bulk transcriptomes across a wide spectrum of adult and childhood cancers, using a previously established method to interrogate their relationship to normal cell states. We extend and validate these findings using single-cell cancer transcriptomes and organ-specific atlases of colorectal and liver cancer.

RESULTS

Our bulk transcriptomic data reveals that adult cancers rarely return to an embryonic state, but that a foetal state is a near-universal feature of childhood cancers. This finding was confirmed with single-cell cancer transcriptomes.

CONCLUSIONS

Our findings provide a nuanced picture of transformation in human cancer, indicating cancer-specific rather than universal patterns of transformation pervade adult epithelial cancers.

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.

Transcriptome analysis reveals high tumor heterogeneity with respect to re-activation of stemness and proliferation programs

Significant alterations in signaling pathways and transcriptional regulatory programs together represent major hallmarks of many cancers. These, among all, include the reactivation of stemness, which is registered by the expression of pathways that are active in the embryonic stem cells (ESCs). Here, we assembled gene sets that reflect the stemness and proliferation signatures and used them to analyze a large panel of RNA-seq data from The Cancer Genome Atlas (TCGA) Consortium in order to specifically assess the expression of stemness-related and proliferation-related genes across a collection of different tumor types. We introduced a metric that captures the collective similarity of the expression profile of a tumor to that of ESCs, which showed that stemness and proliferation signatures vary greatly between different tumor types. We also observed a high degree of intertumoral heterogeneity in the expression of stemness- and proliferation-related genes, which was associated with increased hazard ratios in a fraction of tumors and mirrored by high intratumoral heterogeneity and a remarkable stemness capacity in metastatic lesions across cancer cells in single cell RNA-seq datasets. Taken together, these results indicate that the expression of stemness signatures is highly heterogeneous and cannot be used as a universal determinant of cancer. This calls into question the universal validity of diagnostic tests that are based on stem cell markers.

Identification of a Twelve Epithelial-Mesenchymal Transition-Related lncRNA Prognostic Signature in Kidney Clear Cell Carcinoma

Background

Epithelial-mesenchymal transition (EMT) plays a vital role in tumor metastasis and drug resistance. It has been reported that EMT is regulated by several long noncoding RNAs (lncRNAs). We aimed to identify EMT-related lncRNAs and develop an EMT-related lncRNA prognostic signature in kidney renal clear cell carcinoma (KIRC).

Materials and Methods

In total, 530 ccRCC patients with 611 transcriptome profiles were included in this study. We first identified differentially expressed EMT-related lncRNAs. Then, all the samples with transcriptional data and clinical survival information were randomly split into training/test sets at a ratio of 1 : 1. Accordingly, we further developed a twelve differentially expressed EMT-related lncRNA prognostic signature in the training set. Following this, risk analysis, survival analysis, subgroup analysis, and the construction of the ROC curves were applied to verify the efficacy of the signature in the training set, test set, and all patients. Besides, we further investigated the differential immune infiltration, immune checkpoint expression, and immune-related functions between high-risk patients. Finally, we explored the different drug responses to targeted therapy (sunitinib and sorafenib) and immunotherapy (anti-PD1 and anti-CTLA4).

Results

A twelve differentially expressed EMT-related lncRNA prognostic signature performed superior in predicting the overall survival of KIRC patients. High-risk patients were observed with a significantly higher immune checkpoint expression and showed better responses to the targeted therapy and immunotherapy.

Conclusions

Our study demonstrates that the twelve differentially expressed EMT-related lncRNA prognostic signature could act as an efficient prognostic indicator for KIRC, which also contributes to the decision-making of the further treatment.

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.

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.