Systematic analysis of survival-associated alternative splicing signatures in clear cell renal cell carcinoma

A rare KLHDC4 variant Glu510Lys is associated with genetic susceptibility and promotes tumor metastasis in nasopharyngeal carcinoma

Various genetic association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with nasopharyngeal carcinoma (NPC) risk. However, these studies have predominantly focused on common variants, leaving the contribution of rare variants to the "missing heritability" largely unexplored. Here, we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP, rs141121474, resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk. Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis. Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells, enhancing their migratory and metastatic capabilities, with these effects being further augmented by the Glu510Lys mutation. Mechanistically, the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4 (KLHDC4-WT), leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process, thereby promoting tumor metastasis. Moreover, Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant. Collectively, our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.

Systematic exploration of prognostic alternative splicing events related to tumor immune microenvironment of Clear Cell Renal Cell Carcinoma

BackgroundPathologically, clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma, with high heterogeneity and poor prognosis. There is increasing evidence that alternative splicing (AS) is involved in tumor evolution and tumor immune microenvironment (TIME). However, studies on the exploration of AS events and TIME in ccRCC are still few but needed.MethodsThe transcriptional data and clinicopathological information of patients with ccRCC in The Cancer Genome Atlas (TCGA) database were extracted completely. Patients were grouped according to the ESTIMATE algorithm and differentially expressed AS events (DEASs) were identified. The relationship between AS events and features of TIME were investigated by functional enrichment analysis and unsupervised consensus analysis. Finally, hub splicing factors (SFs) was identified by the regulatory network of survival-related AS events and intersection SFs, and its biological function was further verified in vitro.ResultsIn total, the data of 515 patients with ccRCC were extracted and analyzed. Patients with low immune-score presented longer overall survival (OS) than high immune-score. 861 AS events were identified as DEASs, and they were enriched in immune-related pathways. 3 AS-based clusters were identified and found to have different prognoses and unique immune features. Finally, MBNL1 was identified as a hub SF, and it was shown to inhibit proliferation and metastasis, promote apoptosis, and block cells in G2/M phase in 786O and A498 cells. Mechanistically, MBNL1 regulates QKI expression through AS.ConclusionThe prognostic risk model constructed base on immune-related AS events has good predictive ability for ccRCC. The hub SF MBNL1 identied in the present study could inhibit the progression of ccRCC. This effect is likely due to the regulation of QKI expression through AS.

RNA-binding protein DAZAP1 accelerates the advancement of pancreatic cancer by inhibiting ferroptosis

BACKGROUND

Pancreatic cancer (PC) is a highly aggressive malignancy with a poor prognosis due to its late-stage diagnosis and limited treatment options.

OBJECTIVES

This study aimed to elucidate the molecular mechanisms underlying PC progression and identify potential molecular targets for its diagnosis and treatment.

METHODS

DAZAP1 expression in PC tissues, normal tissues and cell lines was assessed using immunohistochemistry (IHC), reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. DAZAP1 knockdown was achieved through plasmid transfection, and its effects on ferroptosis and PC progression were evaluated using RT-qPCR, western blotting, CCK-8 assays, EdU staining, Fe2+ content measurement, reactive oxygen species (ROS) detection, wound healing and Transwell migration assays.

RESULTS

DAZAP1 expression was significantly upregulated in PC tissues and cell lines compared to normal counterparts. DAZAP1 knockdown suppressed PC cell proliferation and induced ferroptosis, while ferroptosis inhibition reversed these effects, enhancing PC cell proliferation and metastasis.

CONCLUSIONS

DAZAP1 suppression promotes ferroptosis, thereby inhibiting PC cell proliferation and metastasis. These findings suggest that DAZAP1 is a potential therapeutic target for PC.

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data

The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene data (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.9 %.

Alternative splicing regulation and its therapeutic potential in bladder cancer

Bladder cancer is one of the leading causes of mortality globally. The development of bladder cancer is closely associated with alternative splicing, which regulates human gene expression and enhances the diversity of functional proteins. Alternative splicing is a distinctive feature of bladder cancer, and as such, it may hold promise as a therapeutic target. This review aims to comprehensively discuss the current knowledge of alternative splicing in the context of bladder cancer. We review the process of alternative splicing and its regulation in bladder cancer. Moreover, we emphasize the significance of abnormal alternative splicing and splicing factor irregularities during bladder cancer progression. Finally, we explore the impact of alternative splicing on bladder cancer drug resistance and the potential of alternative splicing as a therapeutic target.

Biological functions and clinic significance of SAF‑A (Review)

As one member of the heterogeneous ribonucleoprotein (hnRNP) family, scaffold attachment factor A (SAF-A) or hnRNP U, is an abundant nuclear protein. With RNA and DNA binding activities, SAF-A has multiple functions. The present review focused on the biological structure and different roles of SAF-A and SAF-A-related diseases. It was found that SAF-A maintains the higher-order chromatin organization via RNA and DNA, and regulates transcription at the initiation and elongation stages. In addition to regulating pre-mRNA splicing, mRNA transportation and stabilization, SAF-A participates in double-strand breaks and mitosis repair. Therefore, the aberrant expression and mutation of SAF-A results in tumors and impaired neurodevelopment. Moreover, SAF-A may play a role in the anti-virus system. In conclusion, due to its essential biological functions, SAF-A may be a valuable clinical prediction factor or therapeutic target. Since the role of SAF-A in tumors and viral infections may be controversial, more animal experiments and clinical assays are needed.

Alternative Splicing Factor Heterogeneous Nuclear Ribonucleoprotein U as a Promising Biomarker for Gastric Cancer Risk and Prognosis with Tumor-Promoting Properties

Gastric cancer (GC) is a major global health concern with poor outcomes. Heterogeneous nuclear ribonucleoprotein U (HNRNPU) is a multifunctional protein that participates in pre-mRNA packaging, alternative splicing regulation, and chromatin remodeling. Its potential role in GC remains unclear. In this study, the expression characteristics of HNRNPU were analyzed by The Cancer Genome Atlas data, Gene Expression Omnibus data, and then further identified by real-time quantitative PCR and immunohistochemistry using tissue specimens. From superficial gastritis, atrophic gastritis, and hyperplasia to GC, the in situ expression of HNRNPU protein gradually increased, and the areas under the curve for diagnosis of GC and its precancerous lesions were 0.911 and 0.847, respectively. A nomogram integrating HNRNPU expression, lymph node metastasis, and other prognostic indicators exhibited an area under the curve of 0.785 for predicting survival risk. Knockdown of HNRNPU significantly inhibited GC cell proliferation, migration, and invasion and promoted apoptosis in vitro. In addition, RNA-sequencing analysis showed that HNRNPU could affect alternative splicing events in GC cells, with functional enrichment analysis revealing that HNRNPU may exert malignant biological function in GC progression through alternative splicing regulation. In summary, the increased expression of HNRNPU was significantly associated with the development of GC, with a good performance in diagnosing and predicting the prognostic risk of GC. Functionally, HNRNPU may play an oncogenic role in GC by regulating alternative splicing.

Potentials of lncRNA-miRNA-mRNA networks as biomarkers for laryngeal squamous cell carcinoma

Chemoresistance, radioresistance, and facile spreading of laryngeal squamous cell carcinoma (LSCC) make the practically clinical treatment invalid. Such dismal outcome mainly originates from the lack of effective biomarkers which are highly desirable to understand the pathogenesis of LSCC, and strives to find promising novel biomarkers to improve early screening, effective treatment, and prognosis evaluation in LSCC. Recently, long non-coding RNAs (lncRNAs), a kind of non-coding RNAs longer than 200 nucleotides, can participate in the process of tumorigenesis and progression through many regulatory modalities, such as epigenetic transcriptional regulation and post-transcriptional regulation. Meanwhile, microRNAs (miRNAs, miRs), essentially involved in the post-transcriptional regulation of gene expression, are aberrantly expressed in cancer-related genomic regions or susceptible sites. An increasing number of studies have shown that lncRNAs are important regulators of miRNAs expression in LSCC, and that miRNAs can also target to regulate the expression of lncRNAs, and they can target to regulate downstream messenger RNAs (mRNAs) transcriptionally or post-transcriptionally, thereby affecting various physiopathological processes of LSCC. Complex cross-regulatory networks existing among lncRNAs, miRNAs, and mRNAs can regulate the tumorigenesis and development of LSCC. Such networks may become promising biomarkers and potential therapeutic targets in the research field of LSCC. In this review, we mainly summarize the latest research progress on the regulatory relationships among lncRNAs, miRNAs, and downstream mRNAs, and highlight the potential applications of lncRNA-miRNA-mRNA regulatory networks as biomarkers for the early diagnosis, epithelial-mesenchymal transition (EMT) process, chemoresistance, radioresistance, and prognosis of LSCC, aiming to provide important clues for understanding the pathogenesis of LSCC and developing new diagnostic and therapeutic strategies.

Seven Fatty Acid Metabolism-Related Genes as Potential Biomarkers for Predicting the Prognosis and Immunotherapy Responses in Patients with Esophageal Cancer

Background: Esophageal cancer (ESCA) is a major cause of cancer-related mortality worldwide. Altered fatty acid metabolism is a hallmark of cancer. However, studies on the roles of fatty acid metabolism-related genes (FRGs) in ESCA remain limited. Method: We identified differentially expressed FRGs (DE-FRGs). Then, the DE-FRGs prognostic model was constructed and validated using a comprehensive analysis. Moreover, the correlation between the risk model and clinical characteristics was investigated. A nomogram for predicting survival was established and evaluated. Subsequently, the difference in tumor microenvironment (TME) was compared between two risk groups. The sensitivity of key DE-FRGs to chemotherapeutic interventions and their correlation with immune cells were investigated. Finally, DEGs between two risk groups were measured and the prognostic value of key DE-FRGs in ESCA was confirmed in other databases. Results: A prognostic model was constructed based on seven selected DEG-FRGs. TNM staging and CD8+ T cells were significantly correlated with high-risk groups. Low-risk groups exhibited more infiltrated M0 macrophages, an activation of type II interferon (IFN-γ) responses, and were found to be more suitable for immunotherapy. Seven key DE-FRGs with prognostic value were found to be considerably influenced by different chemotherapy drugs. Conclusion: A prognostic model based on seven DE-FRGs may efficiently predict patient prognosis and immunotherapy response, helping to develop individualized treatment strategies in ESCA.

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.

DAZAP1 overexpression promotes growth of HCC cell lines: a primary study using CEUS

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common types of hepatic carcinoma. The overall prognosis is poor. DAZAP1, a regulator of alternative splicing (AS) events, may participate in tumor growth.

METHODS

We collected 105 HCC patients and tissue samples from the Department of Hepatological Surgery in the Second Affiliated Hospital of Qiqihar Medical University. TCGA datasets were downloaded and operated using the R project. DAZAP1 expressions were examined by quantitative RT-PCR and western blotting. CCK8 assay was used to investigate the cell proliferation, and transwell assay was employed to examine the ability of migration and invasion in vitro. Contrast-enhanced ultrasound (CEUS) was used to evaluate images and parameters of the tumor.

RESULTS

DAZAP1 is highly expressed in the tissue samples of HCC. The peak intensity (PI) and area under the curve (AUC) of the tumor is higher than that of liver parenchyma, and correlated with high DAZAP1 expression. Parameters of CEUS in the tumor are correlated with TNM stage, tumor size, and vascularity. High DAZAP1 expression correlates with a shorter survival time and advanced histologic grade (G3-G4). Bioinformatical analysis revealed that downregulation of DAZAP1 identified differentiated expressed genes (DEGs) involved in the tumor growth process.

CONCLUSIONS

DAZAP1 is highly expressed in hepatic carcinoma and related to the blood flow, and high DAZAP1 expression predicts poor prognosis. DAZAP1 may promote liver carcinoma cell proliferation, migration, and invasion of HEPG2 cells. CEUS parameters are related to the high DAZAP1 expression, and will help to differentiate the HCC tumor.

Targeting HNRNPU to overcome cisplatin resistance in bladder cancer

Purpose

The overall response of cisplatin-based chemotherapy in bladder urothelial carcinoma (BUC) remains unsatisfactory due to the complex pathological subtypes, genomic difference, and drug resistance. The genes that associated with cisplatin resistance remain unclear. Herein, we aimed to identify the cisplatin resistance associated genes in BUC.

Experimental design

The cytotoxicity of cisplatin was evaluated in six bladder cancer cell lines to compare their responses to cisplatin. The T24 cancer cells exhibited the lowest sensitivity to cisplatin and was therefore selected to explore the mechanisms of drug resistance. We performed genome-wide CRISPR screening in T24 cancer cells in vitro, and identified that the gene heterogeneous nuclear ribonucleoprotein U (HNRNPU) was the top candidate gene related to cisplatin resistance. Epigenetic and transcriptional profiles of HNRNPU-depleted cells after cisplatin treatment were analyzed to investigate the relationship between HNRNPU and cisplatin resistance. In vivo experiments were also performed to demonstrate the function of HNRNPU depletion in cisplatin sensitivity.

Results

Significant correlation was found between HNRNPU expression level and sensitivity to cisplatin in bladder cancer cell lines. In the high HNRNPU expressing T24 cancer cells, knockout of HNRNPU inhibited cell proliferation, invasion, and migration. In addition, loss of HNRNPU promoted apoptosis and S-phase arrest in the T24 cells treated with cisplatin. Data from The Cancer Genome Atlas (TCGA) demonstrated that HNRNPU expression was significantly higher in tumor tissues than in normal tissues. High HNRNPU level was negatively correlated with patient survival. Transcriptomic profiling analysis showed that knockout of HNRNPU enhanced cisplatin sensitivity by regulating DNA damage repair genes. Furthermore, it was found that HNRNPU regulates chemosensitivity by affecting the expression of neurofibromin 1 (NF1).

Conclusions

Our study demonstrated that HNRNPU expression is associated with cisplatin sensitivity in bladder urothelial carcinoma cells. Inhibition of HNRNPU could be a potential therapy for cisplatin-resistant bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01517-9.

Pan-Cancer Analysis Revealed SRSF9 as a New Biomarker for Prognosis and Immunotherapy

Background. Serine/arginine-rich splicing factor 9 (SRSF9) is one of the members of SRSF gene family and related to the tumorigenesis and the progression of tumor. However, whether SRSF9 has a crucial role across pan-cancer is still unknown. Methods. In this study, we used public databases, such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx), to analyze SRSF9 expression level among tumor and normal cells. Survival analysis, K-M plotter, and PrognoScan were used to analyze the prognosis value of SRSF9, regarding to overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Moreover, we performed the correlation between SRSF9 and clinical characteristics (including the outcome of prognosis), as well as molecular events of tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint gene, tumor microenvironment (TME), immune infiltrating cells, mismatch repair (MMR) genes, m6A genes, DNA methyltransferases, and neoantigen with bioinformatics methods and TISIDB, TIMER, and Sangerbox websites. Results. In general, SRSF9 expression was upregulated in most cancers, such as BLCA, CHOL, and UCEC, which SRSF9 was associated with short survival and severe progression. In COAD, STAD, and UCEC, SRSF9 expression was positively related to both TMB and MSI. In BRCA, BLCA, ESCA, GBM, HNSC, LUSC, LUAD, OV, PRAD, TGCT, THCA, and UCEC, both immune score and stomal score showed a negative relationship with SRSF9 expression. Immune score showed a positive relationship with SRSF9 expression in LGG. SRSF9 expression had a significant and positive correlation with six types of immune infiltration cells in LGG, KIRC, LIHC, PCPG, PRAD, SKCM, THCA, and THYM, except in LUSC. In LIHC, SRSF9 was highly significant correlated with most immune checkpoint genes. For neoantigens, correlation between SRSF9 and the quantity of neoantigens was significantly positive in some cancer types. SRSF9 was also correlated with MMR genes, m6A genes, and DNA methyltransferases. In the 33 cancer types, gene set enrichment analysis (GSEA) demonstrated that SRSF9 was correlated with multiple functions and signaling pathways. Conclusion. These findings demonstrated that SRSF9 may be a new biomarker for the prognosis and immunotherapy in various cancers. As a result, it will be beneficial to provide new therapies for cancer patients, thereby improving the treatment and prognosis of cancer patients.

Systematic Genome-Wide Profiles Reveal Alternative Splicing Landscape and Implications of Splicing Regulator DExD-Box Helicase 21 in Aggressive Progression of Adrenocortical Carcinoma

Alternative splicing (AS) in the tumor biological process has provided a novel perspective on carcinogenesis. However, the clinical significance of individual AS patterns of adrenocortical carcinoma (ACC) has been underestimated, and in-depth investigations are lacking. We selected 76 ACC samples from the Cancer Genome Atlas (TCGA) SpliceSeq and SpliceAid2 databases, and 39 ACC samples from Fudan University Shanghai Cancer Center (FUSCC). Prognosis-related AS events (PASEs) and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm. In total, 23,984 AS events and 3,614 PASEs were detected in the patients with ACC. The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients (p < 0.001). Prognostic models produced AUC values of 0.907 in all PASEs' groups. Eight splicing factors (SFs), including BAG2, CXorf56, DExD-Box Helicase 21 (DDX21), HSPB1, MBNL3, MSI1, RBMXL2, and SEC31B, were identified in regulatory networks of ACC. DDX21 was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts. In conclusion, the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts. Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS, providing valuable insights into the potential usages of DDX21 for predicting prognosis for patients with ACC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-021-00026-x.

RNA binding protein DAZAP1 promotes HCC progression and regulates ferroptosis by interacting with SLC7A11 mRNA

RNA-binding proteins (RBPs) closely regulate the whole lifecycle of most RNA molecules, from the very early stage of transcription to RNA decay. Dysregulation of RBPs significantly affects the fate of cancer-related transcripts. Therefore, it is imperative to fully understand the complicated RBP-RNA regulatory networks in malignant diseases and to explore novel therapeutic targets. The RBP DAZAP1 (deleted in azoospermia-associated protein 1), originally identified as an important protein in spermatogenesis, had rarely been studied in the context of carcinogenesis. The role of DAZAP1 in hepatocellular carcinoma (HCC) was unveiled in this study. The relative expression of DAZAP1 was significantly upregulated in HCC and was positively associated with several key malignant characteristics and poor postoperative survival in patients. DAZAP1 knockdown by small interfering RNA markedly inhibited HCC cell proliferation, migration and invasion. Furthermore, DAZAP1 significantly reduced cellular sensitivity to sorafenib (SF), which had been proven to be an inducer of ferroptosis by targeting the system Xc^- (composed of a light chain, xCT/SLC7A11, and a heavy chain, 4F2 heavy chain). At the mechanistic level, DAZAP1 was identified as a potent inhibitor of ferroptosis and an efficient binding partner of SLC7A11 mRNA. Further study revealed that DAZAP1 interacted with the 3'UTR (untranslated region) of SLC7A11 mRNA and positively regulated its stability. In our work, we clarified novel functions of DAZAP1 and preliminarily revealed its underlying mechanism in ferroptosis, which may be conducive to the exploration of biomarkers and therapeutic targets in HCC patients.

Identification of driver genes with aberrantly alternative splicing events in pediatric patients with retinoblastoma

Retinoblastoma (RB) is one of the most common cancer in children. However, the specific mechanism about RB tumorigenesis has not been fully understood. In this study, to comprehensively characterize the splicing alterations in the tumorigenesis of RB, we analyzed the differential alternative splicing events in RB. Specifically, the isoforms of RB1 were downregulated in the RB samples, and a large proportion of differentially expressed genes had multiple differentially expressed transcripts (64%). We identified 1453 genes with differential alternative splicing, among which, SE accounted for the majority, followed by MXE, RI, A3SS, and A5SS. Furthermore, the biological function related to the normal function of eyes, and E2F family TFs were significantly enriched by the genes with differential alternative splicing. Among the genes associated with visual sense, ABCA4 was found to have two mutually exclusive exons, resulting in two isoforms with different functionalities. Notably, DAZAP1 was identified as one of the critical splicing factors in RB, which was potentially involved in E2F and RB pathways. Functionally, differential binding sites in DAZAP1 protein were significantly observed between RB and normal samples. Based on the comprehensive analysis of the differential alternative splicing events and splicing factors, we identified some driver genes with differential alternative splicing and critical splicing factors involved in RB, which would greatly improve our understanding of the alternative splicing process in the tumorigenesis of RB.

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC

Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC.

Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5′-C-phosphate-G- 3′ (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes.

Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients.

Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data

BACKGROUND

With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC).

METHODS

Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis.

RESULTS

A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (P < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC.

CONCLUSION

The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.

Identification of Survival-Associated Alternative Splicing Signatures in Lung Squamous Cell Carcinoma

Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients.

Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape.

Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events.

Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Objective

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

Methods

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

Results

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

Conclusions

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

LINC00649 underexpression is an adverse prognostic marker in acute myeloid leukemia

BACKGROUND

Long noncoding RNAs (lncRNA) play a role in leukemogenesis, maintenance, development, and therapeutic resistance of AML. While few studies have focused on the prognostic significance of LINC00649 in AML, which we aim to investigate in this present study.

METHODS

We compared the expression level of LINC00649 between AML patients and healthy controls. The Kaplan-Meier curves of AML patients expressing high versus low level of LINC00649 was performed. The LINC00649 correlated genes/miRNAs/lncRNAs and methylation CpG sites were screened by Pearson correlation analysis with R (version 3.6.0), using TCGA-LAML database. The LINC00649 associated ceRNA network was established using lncBase 2.0 and miRWalk 2.0 online tools, combining results from correlation analysis. Finally, a prediction model was constructed using LASSO-Cox regression.

RESULTS

LINC00649 was underexpressed in bone marrow of AML group than that in healthy control group. The patients of LINC00649-low group have significantly inferior PFS and OS. A total of 154 mRNAs, 31 miRNAs, 28 lncRNAs and 1590 methylated CpG sites were identified to be significantly correlated with LINC00649. Furthermore, the network of ceRNA was established with 6 miRNAs and 122 mRNAs. The Lasso-Cox model fitted OS/PFS to novel prediction models, which integrated clinical factors, ELN risk stratification, mRNA/miRNA expression and methylation profiles. The analysis of time-dependent ROC for our model showed a superior AUC (AUC = 0.916 at 1 year, AUC = 0.916 at 3 years, and AUC = 0.891 at 5 years).

CONCLUSIONS

Low expression of LINC00649 is a potential unfavorable prognostic marker for AML patients, which requires the further validation. The analysis by LASSO-COX regression identified a novel comprehensive model with a superior diagnostic utility, which integrated clinical and genetic variables.

Systematic Analysis of Alternative Splicing Landscape in Pancreatic Adenocarcinoma Reveals Regulatory Network Associated with Tumorigenesis and Immune Response

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive gastrointestinal tumors and has an extremely high mortality rate. Recent studies indicate that alternative splicing (AS), a common post-transcriptional process, has important roles in tumor biological behaviors and may provide novel immunotherapeutic targets. This study systematically analyzes AS profiles in PDAC and reveals their potential regulatory effects on cancer immune response. MATERIAL AND METHODS AS event, RNA sequencing, and splicing factor (SF) data were extracted from SpliceSeq, The Cancer Genome Atlas, and SpliceAid2, respectively. Overall survival (OS)-associated AS events and SFs were identified with univariate analysis. The LASSO method and multivariate Cox regression analysis were used to construct predictive signatures for the prediction of patient prognosis. The proportions of immune cells within PDAC samples were evaluated using the CIBERSORT algorithm. The correlations among AS events, SFs, and immune cell proportions were calculated using Spearman correlation analysis. Consensus clustering and immune classification were performed on the PDAC cohort. RESULTS A total of 4812 OS-related AS events from 3341 parent genes were identified, and 8 AS-based predictive models were constructed for PDAC. An OS-related SF-AS regulatory network was constructed. The AS events regulated by ELAVL4 exhibited strong correlations with CD8 T cells and regulatory T cells. In addition, AS-based clusters demonstrated distinct OS outcomes and immune features. CONCLUSIONS AS-based predictive models with high accuracy were constructed to facilitate prognosis prediction and treatment of PDAC. An SF-AS regulatory network was constructed, revealing the potential relationships among SF, AS, and immune response.

Prognostic alternative mRNA splicing signatures and associated splicing factors in acute myeloid leukemia

The dysregulation of alternative splicing (AS) has emerged as a mechanism of acute myeloid leukemia (AML). However, the prognostic impact of AS events remains under-explored in AML. Here we report the prognostic value of AS events and associated splicing factors based on three datasets of AML patients. We defined the landscape of AS events in AML and identified 7033 AS events associated with the survival of AML patients. Based on these events, we further developed a composite 15 AS event-based prognostic signature, which was independent of the cytogenetic risk stratification and patient age, and showed a better performance than known gene expression signatures. More importantly, our new signature markedly improved the European LeukemiaNet (ELN) risk classification, indicating a broad applicability in the clinical management of AML. Furthermore, the splicing-regulatory network established the correlations between prognostic AS events and associated splicing factors. The finding was validated by CRISPR-based data, which indicated that the increased expression of RBM39 contributed to the higher exon inclusion of SETD5 and conferred a poor outcome. Together, AS events may serve as a novel assortment of prognosticators for AML and could refine the ELN risk stratification. The splicing regulatory network provides clues regarding the splicing factor-mediated mechanisms of AML.

Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer

BACKGROUND

Aberrant DNA methylation is a crucial epigenetic regulator that is closely related to the occurrence and development of various cancers, including breast cancer (BC). The present study aimed to identify a novel methylation-based prognosis biomarker panel by integrally analyzing gene expression and methylation patterns in BC patients.

METHODS

DNA methylation and gene expression data of breast cancer (BRCA) were downloaded from The Cancer Genome Atlas (TCGA). R packages, including ChAMP, SVA, and MethylMix, were applied to identify the unique methylation-driven genes. Subsequently, these genes were subjected to Metascape for GO analysis. Univariant Cox regression was used to identify survival-related genes among the methylation-driven genes. Robust likelihood-based survival modeling was applied to define the prognosis markers. An independent data set (GSE72308) was used for further validation of our risk score system.

RESULTS

A total of 879 DNA methylation-driven genes were identified from 765 BC patients. In the discovery cohort, we identified 50 survival-related methylation-driven genes. Finally, we built an eight-methylation-driven gene panel that serves as prognostic predictors.

CONCLUSIONS

Our analysis of transcriptome and methylome variations associated with the survival status of BC patients provides a further understanding of basic biological processes and a basis for the genetic etiology in BC.