Comprehensive analysis of spliceosome genes and their mutants across 27 cancer types in 9070 patients: clinically relevant outcomes in the context of 3P medicine

The correlation between TRIM28 expression and immune checkpoints in CRPC

This study delves into the unexplored realm of castration-resistant prostate cancer (CRPC) by investigating the role of TRIM28 and its intricate molecular mechanisms using high-throughput single-cell transcriptome sequencing and advanced bioinformatics analysis. Our comprehensive examination unveiled dynamic TRIM28 expression changes, particularly in immune cells such as macrophages and CD8+ T cells within CRPC. Correlation analyses with TCGA data highlighted the connection between TRIM28 and immune checkpoint expression and emphasized its pivotal influence on the quantity and functionality of immune cells. Using TRIM28 knockout mouse models, we identified differentially expressed genes and enriched pathways, unraveling the potential regulatory involvement of TRIM28 in the cGAS-STING pathway. In vitro, experiments further illuminated that TRIM28 knockout in prostate cancer cells induced a notable anti-tumor immune effect by inhibiting M2 macrophage polarization and enhancing CD8+ T cell activity. This impactful discovery was validated in an in situ transplant tumor model, where TRIM28 knockout exhibited a deceleration in tumor growth, reduced proportions of M2 macrophages, and enhanced infiltration of CD8+ T cells. In summary, this study elucidates the hitherto unknown anti-tumor immune role of TRIM28 in CRPC and unravels its potential regulatory mechanism via the cGAS-STING signaling pathway. These findings provide novel insights into the immune landscape of CRPC, offering promising directions for developing innovative therapeutic strategies.

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.

Compromised transcription-mRNA export factor THOC2 causes R-loop accumulation, DNA damage and adverse neurodevelopment

We implicated the X-chromosome THOC2 gene, which encodes the largest subunit of the highly-conserved TREX (Transcription-Export) complex, in a clinically complex neurodevelopmental disorder with intellectual disability as the core phenotype. To study the molecular pathology of this essential eukaryotic gene, we generated a mouse model based on a hypomorphic Thoc2 exon 37-38 deletion variant of a patient with ID, speech delay, hypotonia, and microcephaly. The Thoc2 exon 37-38 deletion male (Thoc2Δ/Y) mice recapitulate the core phenotypes of THOC2 syndrome including smaller size and weight, and significant deficits in spatial learning, working memory and sensorimotor functions. The Thoc2Δ/Y mouse brain development is significantly impacted by compromised THOC2/TREX function resulting in R-loop accumulation, DNA damage and consequent cell death. Overall, we suggest that perturbed R-loop homeostasis, in stem cells and/or differentiated cells in mice and the patient, and DNA damage-associated functional alterations are at the root of THOC2 syndrome.

Quantitative phosphoproteomics reveals molecular pathway network alterations in human early-stage primary hepatic carcinomas: potential for 3P medical approach

Objective

Hepatic carcinoma is one of the most common types of malignant tumors in the digestive system, and its biological characteristics determine its high rate of metastasis and recurrence after radical resection, leading to a poor prognosis for patients. Increasing evidence demonstrates that phosphoproteins and phosphorylation-mediated molecular pathways influence the occurrence and development of hepatic carcinoma. It is urgent need to develop early-stage biomarkers for improving diagnosis, therapy, medical service, and prognostic assessment. We hypothesize that phosphoproteome and phosphorylation-mediated signaling pathway networks significantly differ in human early-stage primary hepatic carcinomas relative to control liver tissues, which will identify the key differentially phosphorylated proteins and phosphorylation-mediated signaling pathway network alterations in human early-stage primary hepatic carcinoma to innovate predictive diagnosis, prognostic assessment, and personalized medical services and progress beyond the state of the art in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Tandem mass tag (TMT)-based quantitative proteomics coupled with TiO2 enrichment of phosphopeptides was used to identify phosphorylation profiling, and bioinformatics was used to analyze the pathways and biological functions of phosphorylation profiling between early-stage hepatic carcinoma tissues and tumor-adjacent normal control tissues. Furthermore, the integrative analysis with transcriptomic data from TCGA database obtained differently expressed genes (DEGs) corresponding to differentially phosphorylated proteins (DPPs) and overall survival (OS)-related DPPs.

Results

A total of 1326 phosphopeptides derived from 858 DPPs in human early-stage primary hepatic carcinoma were identified. KEGG pathway network analysis of 858 DPPs revealed 33 statistically significant signaling pathways, including spliceosome, glycolysis/gluconeogenesis, B-cell receptor signaling pathway, HIF-1 signaling pathway, and fatty acid degradation. Gene Ontology (GO) analysis of 858 DPPs revealed that protein phosphorylation was involved in 57 biological processes, 40 cellular components, and 37 molecular functions. Protein-protein interaction (PPI) network constructed multiple high-combined scores and co-expressed DPPs. Integrative analysis of transcriptomic data and DPP data identified 105 overlapped molecules (DPPs; DEGs) between hepatic carcinoma tissues and control tissues and 125 OS-related DPPs. Overlapping Venn plots showed 14 common molecules among datasets of DPPs, DEGs, and OS-related DDPs, including FTCD, NDRG2, CCT2, PECR, SLC23A2, PNPLA7, ANLN, HNRNPM, HJURP, MCM2, STMN1, TCOF1, TOP2A, and SSRP1. The drug sensitivities of OS-related DPPs were identified, including LMOD1, CAV2, UBE2E2, RAPH1, ANXA5, HDLBP, CUEDC1, APBB1IP, VCL, SRSF10, SLC23A2, EPB41L2, ESR1, PLEKHA4, SAFB2, SMARCAD1, VCAN, PSD4, RDH16, NOP56, MEF2C, BAIAP2L2, NAGS, SRSF2, FHOD3, and STMN1.

Conclusions

Identification and annotation of phosphoproteomes and phosphorylation-mediated signaling pathways in human early-stage primary hepatic carcinoma tissues provided new directions for tumor prevention and treatment, which (i) helps to enrich phosphorylation functional research and develop new biomarkers; (ii) enriches phosphorylation-mediated signaling pathways to gain a deeper understanding of the underlying mechanisms of early-stage primary hepatic carcinoma; and (iii) develops anti-tumor drugs that facilitate targeted phosphorylated sites. We recommend quantitative phosphoproteomics in early-stage primary hepatic carcinoma, which offers great promise for in-depth insight into the molecular mechanism of early-stage primary hepatic carcinoma, the discovery of effective therapeutic targets/drugs, and the construction of reliable phosphorylation-related biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized medical services in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00335-3.

Alternatively Spliced Isoforms of MUC4 and ADAM12 as Biomarkers for Colorectal Cancer Metastasis

There is a pertinent need to develop prognostic biomarkers for practicing predictive, preventive and personalized medicine (PPPM) in colorectal cancer metastasis. The analysis of isoform expression data governed by alternative splicing provides a high-resolution picture of mRNAs in a defined condition. This information would not be available by studying gene expression changes alone. Hence, we utilized our prior data from an exon microarray and found ADAM12 and MUC4 to be strong biomarker candidates based on their alternative splicing scores and pattern. In this study, we characterized their isoform expression in a cell line model of metastatic colorectal cancer (SW480 & SW620). These two genes were found to be good prognostic indicators in two cohorts from The Cancer Genome Atlas database. We studied their exon structure using sequence information in the NCBI and ENSEMBL genome databases to amplify and validate six isoforms each for the ADAM12 and MUC4 genes. The differential expression of these isoforms was observed between normal, primary and metastatic colorectal cancer cell lines. RNA-Seq analysis further proved the differential expression of the gene isoforms. The isoforms of MUC4 and ADAM12 were found to change expression levels in response to 5-Fluorouracil (5-FU) treatment in a dose-, time- and cell line-dependent manner. Furthermore, we successfully detected the protein isoforms of ADAM12 and MUC4 in cell lysates, reflecting the differential expression at the protein level. The change in the mRNA and protein expression of MUC4 and ADAM12 in primary and metastatic cells and in response to 5-FU qualifies them to be studied as potential biomarkers. This comprehensive study underscores the importance of studying alternatively spliced isoforms and their potential use as prognostic and/or predictive biomarkers in the PPPM approach towards cancer.

Identification of immune and stromal cell infiltration-related gene signature for prognosis prediction in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common and life-threatening hematologic malignancy, its occurrence and progression are closely related to immune/stromal cell infiltration in the bone marrow (BM) microenvironment. However, no studies have described an immune/stromal cell infiltration-related gene (ISCIRG)-based prognostic signature for ALL. A total of 444 patients involving 437 bulk and 7 single-cell RNA-seq datasets were included in this study. Eligible datasets were searched and reviewed from the database of TCGA, TARGET project and GEO. Then an integrated bioinformatics analysis was performed to select optimal prognosis-related genes from ISCIRGs, construct a nomogram model for predicting prognosis, and assess the predictive power. After LASSO and multivariate Cox regression analyses, a seven ISCIRGs-based signature was proved to be able to significantly stratify patients into high- and low-risk groups in terms of OS. The seven genes were confirmed that directly related to the composition and status of immune/stromal cells in BM microenvironment by analyzing bulk and single-cell RNA-seq datasets. The calibration plot showed that the predicted results of the nomogram were consistent with the actual observation results of training/validation cohort. This study offers a reference for future research regarding the role of ISCIRGs in ALL and the clinical care of patients.