Bioinformatics analysis of SRSF1-controlled gene networks in colorectal cancer

Network pharmacology-based strategy to investigate the effect and mechanism of α-solanine against glioma

BACKGROUND

An anti-tumour activity has been demonstrated for α-solanine, a bioactive compound extracted from the traditional Chinese herb Solanum nigrum L. However, its efficacy in the treatment of gliomas and the underlying mechanisms remain unclear. The aim of this study was to investigate the inhibitory effects of α-solanine on glioma and elucidate its mechanisms and targets using network pharmacology, molecular docking, and molecular biology experiments.

METHODS

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was utilized to predict the potential targets of α-solanine. GeneCards was used to gather glioma-related targets, and the STRING online database was used to analyze protein-protein interaction (PPI) networks for the shared targets. Hub genes were identified from the resulting PPI network and further investigated using Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, prognostic and gene set enrichment analyses (GSEA) were carried out to identify potential therapeutic targets and their underlying mechanisms of action in relation to the prognosis of gliomas. In vitro experiments were conducted to verify the findings from the network pharmacology analysis.

RESULTS

A total of 289 α-solanine targets and 1149 glioma-related targets were screened, of which 78 were common targets. 11 hub genes were obtained, including SRC, HRAS, HSP90AA1, IGF1, MAPK1, MAPK14, KDR, STAT1, JAK2, MAP2K1, and IGF1R. The GO and KEGG pathway analyses unveiled that α-solanine was strongly associated with several signaling pathways, including positive regulation of MAP kinase activity and PI3K-Akt. Moreover, α-solanine (10 µM and 15 µM) inhibited the proliferation and migration but promoted the apoptosis of glioma cells. Finally, STAT1 was identified as a potential mediator of the effect of α-solanine on glioma prognosis.

CONCLUSION

α-Solanine can inhibit the proliferation and migration of gliomas by regulating multiple targets and signalling pathways. These findings lay the foundation for the creation of innovative clinical anti-glioma agents.

A patient-derived organoid-based study identified an ASO targeting SNORD14E for endometrial cancer through reducing aberrant FOXM1 Expression and β-catenin nuclear accumulation

BACKGROUND

Most of the endometrial cancer (EC) patients are diagnosis in early stage with a good prognosis while the patients with locally advanced recurrent or metastatic result in a poor prognosis. Adjuvant therapy could benefit the prognosis of patients with high-risk factors. Unfortunately, the molecular classification of great prognostic value has not yet reached an agreement and need to be further refined. The present study aims to identify new targets that have prognostic value in EC based on the method of EC patient-derived organ-like organs (PDOs), and further investigate their efficacy and mechanism.

METHODS

The Cancer Genome Atlas (TCGA) database was used to determine SNORD14E expression. The effects of SNORD14E were investigated using CCK8, Transwell, wound-healing assays, and a xenograft model experiment; apoptosis was measured by flow cytometry. Antisense oligonucleotide (ASO) targeting SNORD14E was designed and patient-derived organoids (PDO) models in EC patients was established. A xenograft mouse and PDO model were employed to evaluate the effects of ASO targeting SNORD14E. RNA-seq, Nm-seq, and RNA immunoprecipitation (RIP) experiments were employed to confirm the alternative splicing (AS) and modification induced by SNORD14E. A minigene reporter gene assay was conducted to confirm AS and splicing factors on a variable exon. Actinomycin-d (Act-D) and Reverse Transcription at Low deoxy-ribonucleoside triphosphate concentrations followed by PCR (RTL-P) were utilized to confirm the effects of 2'-O methylation modification on FOXM1.

RESULTS

We found that SNORD14E was overexpressed in EC tissues and patients with high expressed SNORD14E were distributed in the TCGA biomolecular classification subgroups without difference. Further, SNORD14E could reduce disease-free survival (DFS) and recurrence free survival (RFS) of EC patients. SNORD14E promoted proliferation, migration, and invasion and inhibited the apoptosis of EC cells in vitro. ASOs targeting SNORD14E inhibited cell proliferation, migration, invasion while promoted cell apoptosis. ASOs targeting SNORD14E inhibited tumor growth in the xenograft mouse model. TCGA-UCEC database showed that the proportion of patients with high expression of SNORD14E in middle-high risk and high-risk patients recommended by EMSO-ESGO-ESTRO guidelines for adjuvant therapy is more than 50%. Next, we enrolled 8 cases of high-risk and high-risk EC patients according to EMSO-ESGO-ESTRO guidelines and successfully constructed EC-PDOs. ASOs targeting SNORD14E inhibited the EC-PDO growth. Mechanistically, SNORD14E could recognize the mRNA of FOXM1 and recruit SRSF1 to promote the shearing of the variable exon VIIa of FOXM1, resulting in the overexpression of the FOXM1 malignant subtypes FOXM1b and FOXM1c. In addition, SNORD14E modified FOXM1 mRNA with 2`-O-methylation, which prolonged the half-life of FOXM1 mRNA. The nucleus accumulation of β-catenin caused by aberrant FOXM1 expression led to EC progression.

CONCLUSIONS

ASO targeting SNORD14E can be an effective treatment for EC.

New Clues to Prognostic Biomarkers of Four Hematological Malignancies

Globally, one out of every two reported cases of hematologic malignancies (HMs) results in death. Each year approximately 1.24 million cases of HMs are recorded, of which 58% become fatal. Early detection remains critical in the management and treatment of HMs. However, this is thwarted by the inadequate number of reliable biomarkers. In this study, we mined public databases for RNA-seq data on four common HMs intending to identify novel biomarkers that could serve as HM management and treatment targets. A standard RNA-seq analysis pipeline was strictly adhered to in identifying differentially expressed genes (DEGs) with DESeq2, limma+voom and edgeR. We further performed gene enrichment analysis, protein-protein interaction (PPI) network analysis, survival analysis and tumor immune infiltration level detection on the genes using G:Profiler, Cytoscape and STRING, GEPIA tool and TIMER, respectively. A total of 2,136 highly-ranked DEGs were identified in HM vs. non-HM samples. Gene ontology and pathway enrichment analyses revealed the DEGs to be mainly enriched in steroid biosynthesis (5.075×10-4), cholesterol biosynthesis (2.525×10-8), protein binding (3.308×10-18), catalytic activity (2.158×10-10) and biogenesis (5.929×10-8). The PPI network resulted in 60 hub genes which were verified with data from TCGA, MET500, CPTAC and GTEx projects. Survival analyses with clinical data from TCGA showed that high expression of SRSF1, SRSF6, UBE2Z and PCF11, and low expression of HECW2 were correlated with poor prognosis in HMs. In summary, our study unraveled essential genes that could serve as potential biomarkers for prognosis and may serve as drug targets for HM management.

The Clinical Role of SRSF1 Expression in Cancer: A Review of the Current Literature

Background: SFRS1 is a member of the splicing factor protein family. Through a specific sequence of alteration, SRSF1 can move from the cytoplasm to the nucleus where it can work autonomously as a splicing activator, or as a silencer when interacting with other regulators. Alternative splicing (AS) is a fundamental biological process that ensures protein diversity. In fact, different proteins, produced by alternative splicing, can gain different and even antagonistic biological functions. Methods: Our review is based on English articles published in the MEDLINE/PubMed medical library between 2000 and 2021. We retrieved articles that were specifically related to SRSF1 and cancers, and we excluded other reviews and meta-analyses. We included in vitro studies, animal studies and clinical studies, evaluated using the Medical Education Research Study Quality Instrument (MERSQI) and the Newcastle–Ottawa Scale-Education (NOSE). Result: SRSF1 is related to various genes and plays a role in cell cycle, ubiquitin-mediated proteolysis, nucleotide excision repair, p53 pathway, apoptosis, DNA replication and RNA degradation. In most cases, SRSF1 carries out its cancer-related function via abnormal alternative splicing (AS). However, according to the most recent literature, SRSF1 may also be involved in mRNA translation and cancer chemoresistance or radio-sensitivity. Conclusion: Our results showed that SRSF1 plays a key clinical role in tumorigenesis and tumor progression in several types of cancer (such as Prostate, Lung, Breast, Colon, Glioblastoma), through various mechanisms of action and different cellular pathways. This review could be a starting point for several studies regarding the biology of and therapies for cancer.

Splicing factor SRSF1 promotes breast cancer progression via oncogenic splice switching of PTPMT1

Background

Intensive evidence has highlighted the effect of aberrant alternative splicing (AS) events on cancer progression when triggered by dysregulation of the SR protein family. Nonetheless, the underlying mechanism in breast cancer (BRCA) remains elusive. Here we sought to explore the molecular function of SRSF1 and identify the key AS events regulated by SRSF1 in BRCA.

Methods

We conducted a comprehensive analysis of the expression and clinical correlation of SRSF1 in BRCA based on the TCGA dataset, Metabric database and clinical tissue samples. Functional analysis of SRSF1 in BRCA was conducted in vitro and in vivo. SRSF1-mediated AS events and their binding motifs were identified by RNA-seq, RNA immunoprecipitation-PCR (RIP-PCR) and in vivo crosslinking followed by immunoprecipitation (CLIP), which was further validated by the minigene reporter assay. PTPMT1 exon 3 (E3) AS was identified to partially mediate the oncogenic role of SRSF1 by the P-AKT/C-MYC axis. Finally, the expression and clinical significance of these AS events were validated in clinical samples and using the TCGA database.

Results

SRSF1 expression was consistently upregulated in BRCA samples, positively associated with tumor grade and the Ki-67 index, and correlated with poor prognosis in a hormone receptor-positive (HR+) cohort, which facilitated proliferation, cell migration and inhibited apoptosis in vitro and in vivo. We identified SRSF1-mediated AS events and discovered the SRSF1 binding motif in the regulation of splice switching of PTPMT1. Furthermore, PTPMT1 splice switching was regulated by SRSF1 by binding directly to its motif in E3 which partially mediated the oncogenic role of SRSF1 by the AKT/C-MYC axis. Additionally, PTPMT1 splice switching was validated in tissue samples of BRCA patients and using the TCGA database. The high-risk group, identified by AS of PTPMT1 and expression of SRSF1, possessed poorer prognosis in the stage I/II TCGA BRCA cohort.

Conclusions

SRSF1 exerts oncogenic roles in BRCA partially by regulating the AS of PTPMT1, which could be a therapeutic target candidate in BRCA and a prognostic factor in HR+ BRCA patient.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-01978-8.

SRSF1 mediates cytokine-induced impaired imatinib sensitivity in chronic myeloid leukemia

Patients with chronic myeloid leukemia (CML) who are treated with tyrosine kinase inhibitors (TKIs) experience significant heterogeneity regarding depth and speed of responses. Factors intrinsic and extrinsic to CML cells contribute to response heterogeneity and TKI resistance. Among extrinsic factors, cytokine-mediated TKI resistance has been demonstrated in CML progenitors, but the underlying mechanisms remain obscure. Using RNA-sequencing, we identified differentially expressed splicing factors in primary CD34⁺ chronic phase (CP) CML progenitors and controls. We found SRSF1 expression to be increased as a result of both BCR-ABL1- and cytokine-mediated signaling. SRSF1 overexpression conferred cytokine independence to untransformed hematopoietic cells and impaired imatinib sensitivity in CML cells, while SRSF1 depletion in CD34⁺ CP CML cells prevented the ability of extrinsic cytokines to decrease imatinib sensitivity. Mechanistically, PRKCH and PLCH1 were upregulated by elevated SRSF1 levels, and contributed to impaired imatinib sensitivity. Importantly, very high SRSF1 levels in the bone marrow of CML patients at presentation correlated with poorer clinical TKI responses. In summary, we find SRSF1 levels to be maintained in CD34⁺ CP CML progenitors by cytokines despite effective BCR-ABL1 inhibition, and that elevated levels promote impaired imatinib responses. Together, our data support an SRSF1/PRKCH/PLCH1 axis in contributing to cytokine-induced impaired imatinib sensitivity in CML.

Genome-Wide Profiling of Prognostic Alternative Splicing Signature in Colorectal Cancer

Background: This study was to explore differential RNA splicing patterns and elucidate the function of the splice variants served as prognostic biomarkers in colorectal cancer (CRC).

Methods: Genome-wide profiling of prognostic alternative splicing (AS) events using RNA-seq data from The Cancer Genome Atlas (TCGA) program was conducted to evaluate the roles of seven AS patterns in 330 colorectal cancer cohort. The prognostic predictors models were assessed by integrated Cox proportional hazards regression. Based on the correlations between survival associated AS events and splicing factors, splicing networks were built.

Results: A total of 2,158 survival associated AS events in CRC were identified. Interestingly, most of these top 20 survival associated AS events were adverse prognostic factors. The prognostic models were built by each type of splicing patterns, performing well for risk stratification in CRC patients. The area under curve (AUC) of receiver operating characteristic (ROC) for the combined prognostic predictors model could reach 0.963. Splicing network also suggested distinguished correlation between the expression of splicing factors and AS events in CRC patients.

Conclusion: The ideal prognostic predictors model for risk stratification in CRC patients was constructed by differential splicing patterns of 13 genes. Our findings enriched knowledge about differential RNA splicing patterns and the regulation of splicing, providing generous biomarker candidates and potential targets for the treatment of CRC.