A novel alternative splicing-based prediction model for uteri corpus endometrial carcinoma

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.

[WITHDRAWN] miR-144-3p Functions as a Tumor Suppressor in Endometrial Cancer by Targeting PRR11

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Role of alternative splicing events in endometrial cancer prognosis

OBJECTIVES

Alternative splicing (AS), as a potent and pervasive mechanism of transcriptional regulation, can expand the genome's coding capacity. Growing evidence suggests that the AS events may be associated with various types of cancer. This study aims to explore the prognostic value of AS in endometrial cancer (EC).

METHODS

Differently expressed AS (DEAS) events were screened by pairing the percent spliced in (PSI) value of tumor and paracancerous tissues in The Cancer Genome Atlas (TCGA) database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on their parental gene analysis of organisms. Subsequently, univariate Cox analysis was used to identify the prognostic AS events and a stepwise multi-factor Cox regression analysis was performed to further construct prognostic models. Furthermore, the diagnostic value of the prognostic model was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. Finally, the regulatory network of AS events and splicing factory in the model was also constructed.

RESULTS

A total of 28 281 AS events were detected in EC. Of them, 42 DEAS were identified, and their parental genes were involved in tumor-related processes such as meiotic nuclear division, alpha-amino acid biosynthetic process, nuclear division, and so on. Univariate Cox analysis identified 2 289 prognostic-related AS events and constructed Cox prognostic models based on 7 different types and all types of AS events, in which the area under the curve of ROC of all types was as high as 0.882 and was better than that of 7 different splicing types. Finally, 12 splicing factors and AS events showed an obvious regulatory relationship.

CONCLUSIONS

We use the whole genome analysis of AS events to establish a scientific prognostic prediction model for EC patients, which provides a reliable theoretical basis for the evaluation of EC clinical prognosis.

Upregulation of microRNA miR-141-3p and its prospective targets in endometrial carcinoma: a comprehensive study

The clinicopathological value of microRNA-141-3p (miR-141-3p) and its prospective target genes in endometrial carcinoma (EC) remains unclear. The present study determined the expression level of miR-141-3p in EC via quantitative real-time PCR (RT-qPCR). RT-qPCR showed a markedly higher expression level of miR-141-3p in EC tissues than in non-EC endometrium tissues (P < 0.0001). The microarray and miRNA-seq data revealed upregulation of miR-141-3p. Integrated analysis based on 675 cases of EC and 63 controls gave a standardized mean difference of 1.737, confirmed the upregulation of miR-141-3p. The Kaplan-Meier survival curve showed that a higher expression of miR-141-3p positively corelated with a poorer prognosis. Combining the predicted targets and downregulated genes in EC, we obtained 271 target genes for miR-141-3p in EC. Two potential targets, PPP1R12A and PPP1R12B, were downregulated at both the mRNA and protein levels. This study indicates that the overexpression of miR-141-3p may play an important part in the carcinogenesis of EC. The overexpression of miR-141-3p may be a risk factor for the prognosis of patients with EC.

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

Effects of Alternative Splicing Events on Acute Myeloid Leukemia

As suggested by an increasing amount of evidence, there is alternative splicing (AS) modification within malignancy, which is related to cancer occurrence and development. AS within acute myeloid leukemia (AML) has not yet been systematically analyzed yet. This study analyzed the transcriptomic profiling and corresponding clinical data from AML cases based on The Cancer Genome Atlas (TCGA). In addition, the prediction model, along with the splicing network, was used to analyze the prognosis for AML patients according to the seven different AS event types. Among the 34,984 AS events across the 8830 genes, 2896 AS events were detected among 1905 genes, showing marked correlation with the overall survival of patients. The risk scoring model based on all AS event types was the most efficient in identifying the prognosis for AML patients. Meanwhile, the area under the curve at 1-, 3-, 5-year were 0.852, 0.935, 0.955, respectively. At the same time, the splicing regulating network, which was constituted by 21 splicing factor genes as well as 32 AS events related to survival, was characterized. In conclusion, our predictive model constructed based on the AS events accurately predicts the survival for AML patients. In addition, the network between AS events and splicing factor is established, which may serve as a potential mechanism.

Using mRNAsi to identify prognostic-related genes in endometrial carcinoma based on WGCNA

AIMS

Cancer Stem Cells (CSCs) refers to heterogeneous tumor cells retaining the abilities of self-renewal and differentiation. This study used mRNAsi, which is an index to describe the similarity between tumor cells and CSCs, to define genes involved in endometrial carcinoma.

MATERIALS AND METHODS

The mRNA expression profiles of 552 tumor samples and 23 non-tumor samples were calculated for differentially expressed genes. WGCNA was utilized to construct gene co-expression networks and classify screened genes into different modules. Univariate and multivariate Cox regression models were performed to identify and construct the prognostic model. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, and nomogram were used to assess the prognostic capacity of the six-gene signature. The screened genes were further validated by GEO (GSE17025) and qRT-PCR in EC tissues.

KEY FINDINGS

2573 upregulated and 1890 downregulated genes were identified. A total of 35 genes in the turquoise module were identified as key genes. With multivariate analysis, six genes (DEPDC1, FAM83D, NCAPH, SPC25, TPX2, and TTK) up-regulated in endometrial carcinoma were identified, and their higher expression was associated with a higher stage/age/grade. Moreover, ROC and Kaplan-Meier plots indicated these genes had a high prognostic value for EC. A nomogram was constructed for clinical use. In addition, we explored the pathogenesis involving six genes. The results showed that these genes may become pathogenic as their copy numbers changes and methylation level reduces. Finally, GSEA revealed these genes had a close association with cell cycle, etc. SIGNIFICANCE: These findings may provide new insights into the treatment of diseases.

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.

A Novel Prognostic Index Based on Alternative Splicing in Papillary Renal Cell Carcinoma

Background

Papillary renal cell carcinoma (pRCC) is a heterogeneous multifocal or isolated tumor with an invasive phenotype. Previous studies presented that alternative splicing, as a crucial posttranscriptional regulator in gene expression, is associated with tumorigenesis. However, the association between alternative splicing and pRCC has not been clarified

Methods

The RNA sequencing data and clinical information were downloaded from The Cancer Genome Atlas database and mRNA splicing profiles from TCGASpliceSeq. The percent spliced in data of alternative splicing merged with survival information was firstly calculated by univariate Cox regression analysis to screen for survival‐associated alternative splicing events, and survival‐associated alternative splicing events were then analyzed by Gene Ontology categories using Kyoto Encyclopedia of Genes and Genomes. Meanwhile, the least absolute shrinkage and selection operator Cox analysis and multivariate Cox analysis were performed to calculate the prognostic index for each alternative splicing type. In addition, clinical factors were introduced to assess the performance of prognostic index.

Results

A total of 4,084 candidate survival-associated alternative splicing events in 2,558 genes were screened out. Patients were divided into the low-risk group and the high-risk group based on the median prognostic index value. The Kaplan-Meier survival analysis (p < 0.05) and receiver operating characteristics curves (AUC>0.9) indicated that prognostic index was effective and stable for predicting the prognosis of pRCC patients. Furthermore, a regulatory network was constructed incorporating alternative splicing events and survival-associated splicing factors.

Conclusion

Our study provides new insights into the mechanism of alternative splicing events in tumorigenesis and their clinical potential for pRCC.

Role of alternative splicing signatures in the prognosis of glioblastoma

BACKGROUND

Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking.

METHODS

The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan-Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM.

RESULTS

A total of 2355 survival-associated AS events were identified. The splicing prognostic model revealed that patients in the high-risk group have worse survival rates than those in the low-risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = -.506).

CONCLUSIONS

Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.

Prognostic Potential of Alternative Splicing Markers in Endometrial Cancer

Alternative splicing (AS), an important post-transcriptional regulatory mechanism that regulates the translation of mRNA isoforms and generates protein diversity, has been widely demonstrated to be associated with oncogenic processes. In this study, we systematically analyzed genome-wide AS patterns to explore the prognostic implications of AS in endometrial cancer (EC). A total of 2,324 AS events were identified as being associated with the overall survival of EC patients, and eleven of these events were further selected using a random forest algorithm. With the implementation of a generalized, boosted regression model, a prognostic AS model that aggregated these eleven markers was ultimately established with high performance for risk stratification in EC patients. Functional analysis of these eleven AS markers revealed various potential signaling pathways implicated in the progression of EC. Splicing network analysis demonstrated the notable correlation between the expression of splicing factors and AS markers in EC and further determined eight candidate splicing factors that could be therapeutic targets for EC. Taken together, the results of this study present the utility of AS profiling in identifying biomarkers for the prognosis of EC and provide comprehensive insight into the molecular mechanisms involved in EC processes.

Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events

Cholangiocarcinoma (CCA) is a type of malignant tumor that originates in the mucosal epithelial cells of the biliary system. It is a highly aggressive cancer that progresses rapidly, has low surgical resection rates and a high recurrence. At present, no prognostic molecular biomarker for CCA has been identified. However, CCA progression is affected by mRNA precursors that modify gene expression levels and protein structures through alternative splicing (AS) events, which create molecular indicators that may potentially be used to predict CCA outcomes. The present study aimed to construct a model to predict CCA prognosis based on AS events. Using prognostic data available from The Cancer Genome Atlas, including the percent spliced index of AS events obtained from TCGASpliceSeq in 32 CCA cases, univariate and multivariate Cox regression analyses were performed to assess the associations between AS events and the overall survival (OS) rates of patients with CCA. Additional multivariate Cox regression analyses were used to identify AS events that were significantly associated with prognosis, which were used to construct a prediction model with a prognostic index (PI). A receiver operating characteristic (ROC) curve was used to determine the predictive value of the PI, and Pearson's correlation analysis was used to determine the association between OS-related AS events and splicing factors. A total of 38,804 AS events were identified in 9,673 CCA genes, among which univariate Cox regression analysis identified 1,639 AS events associated with OS (P<0.05); multivariate Cox regression analysis narrowed this list to 23 CCA AS events (P<0.001). The final PI model was constructed to predict the survival of patients with CCA; the ROC curve demonstrated that it had a high predictive power for CCA prognosis, with a highest area under the curve of 0.986. Correlations between 23 OS-related AS events and splicing factors were also noted, and may thus, these AS events may be used to improve predictions of OS. In conclusion, AS events exhibited potential for predicting the prognosis of patients with CCA, and thus, the effects of AS events in CCA required further examination.