Oligonucleotide-directed STAT3 alternative splicing switch drives anti-tumorigenic outcomes in MCF10 human breast cancer cells

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach

Background

Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer.

Methods

The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC.

Results

Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells.

Conclusions

This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

Therapeutic Targeting of RNA Splicing in Cancer

RNA splicing is a key regulatory step in the proper control of gene expression. It is a highly dynamic process orchestrated by the spliceosome, a macro-molecular machinery that consists of protein and RNA components. The dysregulation of RNA splicing has been observed in many human pathologies ranging from neurodegenerative diseases to cancer. The recent identification of recurrent mutations in the core components of the spliceosome in hematologic malignancies has advanced our knowledge of how splicing alterations contribute to disease pathogenesis. This review article will discuss our current understanding of how aberrant RNA splicing regulation drives tumor initiation and progression. We will also review current therapeutic modalities and highlight emerging technologies designed to target RNA splicing for cancer treatment.

Differential silencing of STAT3 isoforms leads to changes in STAT3 activation

Signal transducer and activator of transcription 3 (STAT3) is a transcription factor involved in multiple fundamental biological processes and a key player in cancer development and progression. STAT3 is activated upon tyrosine phosphorylation and is constitutively active in various malignancies; therefore, the expression of pSTAT3 has been recognized as a predictor of poor survival. STAT3 encodes two alternatively-spliced STAT3 isoforms: the full-length STAT3α isoform and the truncated STAT3β isoform. These isoforms have been suggested as the reason for the occasionally observed opposing roles of STAT3 in cancer: an oncogene, on one hand, and a tumor suppressor on the other. To investigate their roles in aggressive breast cancer, we separately silenced each isoform and found that they affect each other's activation, impacting cell viability, cytokine expression, and migration. Silencing specific isoforms can lead to a more favorable balance of activated STAT3 proteins in the cell. Distinguishing between the two isoforms and their active forms is crucial for STAT3-related cancer diagnosis and therapy.

Dysregulation of splicing variants and spliceosome components in breast cancer

The dysregulation of the splicing process has emerged as a novel hallmark of metabolic and tumor pathologies. In breast cancer (BCa), which represents the most diagnosed cancer type among women worldwide, the generation and/or dysregulation of several oncogenic splicing variants have been described. This is the case of the splicing variants of HER2, ER, BRCA1, or the recently identified by our group, In1-ghrelin and SST5TMD4, which exhibit oncogenic roles, increasing the malignancy, poor prognosis, and resistance to treatment of BCa. This altered expression of oncogenic splicing variants has been closely linked with the dysregulation of the elements belonging to the macromolecular machinery that controls the splicing process (spliceosome components and the associated splicing factors). In this review, we compile the current knowledge demonstrating the altered expression of splicing variants and spliceosomal components in BCa, showing the existence of a growing body of evidence supporting the close implication of the alteration in the splicing process in mammary tumorigenesis.

Nonsense-mediated RNA decay and its bipolar function in cancer

Nonsense-mediated decay (NMD) was first described as a quality-control mechanism that targets and rapidly degrades aberrant mRNAs carrying premature termination codons (PTCs). However, it was found that NMD also degrades a significant number of normal transcripts, thus arising as a mechanism of gene expression regulation. Based on these important functions, NMD regulates several biological processes and is involved in the pathophysiology of a plethora of human genetic diseases, including cancer. The present review aims to discuss the paradoxical, pro- and anti-tumorigenic roles of NMD, and how cancer cells have exploited both functions to potentiate the disease. Considering recent genetic and bioinformatic studies, we also provide a comprehensive overview of the present knowledge of the advantages and disadvantages of different NMD modulation-based approaches in cancer therapy, reflecting on the challenges imposed by the complexity of this disease. Furthermore, we discuss significant advances in the recent years providing new perspectives on the implications of aberrant NMD-escaping frameshifted transcripts in personalized immunotherapy design and predictive biomarker optimization. A better understanding of how NMD differentially impacts tumor cells according to their own genetic identity will certainly allow for the application of novel and more effective personalized treatments in the near future.

STAT3, the Challenge for Chemotherapeutic and Radiotherapeutic Efficacy

Chemoradiotherapy is one of the most effective and extensively used strategies for cancer treatment. Signal transducer and activator of transcription 3 (STAT3) regulates vital biological processes, such as cell proliferation and cell growth. It is constitutively activated in various cancers and limits the application of chemoradiotherapy. Accumulating evidence suggests that STAT3 regulates resistance to chemotherapy and radiotherapy and thereby impairs therapeutic efficacy by mediating its feedback loop and several target genes. The alternative splicing product STAT3β is often identified as a dominant-negative regulator, but it enhances sensitivity to chemotherapy and offers a new and challenging approach to reverse therapeutic resistance. We focus here on exploring the role of STAT3 in resistance to receptor tyrosine kinase (RTK) inhibitors and radiotherapy, outlining the potential of targeting STAT3 to overcome chemo(radio)resistance for improving clinical outcomes, and evaluating the importance of STAT3β as a potential therapeutic approach to overcomes chemo(radio)resistance. In this review, we discuss some new insights into the effect of STAT3 and its subtype STAT3β on chemoradiotherapy sensitivity, and we explore how these insights influence clinical treatment and drug development for cancer.

A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia

There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug development, and it plays a key role in drug discovery. Based on network pharmacology and virtual screening, candidates were selected from the molecular database. Nine artificial intelligence algorithm models were used to further verify the candidates' potential. The 350 training results of the deep learning model showed higher credibility, and the R-square of the training set and test set of the optimal model reached 0.89 and 0.84, respectively. The random forest model has an R-square of 0.91 and a mean square error of only 0.003. The R-square of the Adaptive Boosting model and the Bagging model reached 0.92 and 0.88, respectively. Molecular dynamics simulation evaluated the stability of the ligand-protein complex and achieved good results. Artificial intelligence models had unearthed the promising candidates for STAT3 inhibitors, and the good performance of most models showed that they still had practical value on small data sets.