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

Abstract

Relevance: Spliceosome machinery plays important roles in cell biological processes, and its alterations are significantly associated with cancer pathophysiological processes and contribute to the entire healthcare process in the framework of predictive, preventive, and personalized medicine (PPPM/3P medicine).

Purpose: To understand the expression and mutant status of spliceosome genes (SGs) in common malignant tumors and their relationship with clinical characteristics, a pan-cancer analysis of these SGs was performed across 27 cancer types in 9070 patients to discover biomarkers for cancer early diagnosis and prognostic assessment, effectively stratify patients, and improve the survival and prognosis of patients in 3P medical practice.

Methods: A total of 150 SGs were collected from the KEGG database. The Python and R language were combined to process the transcriptional data of SGs and clinical data of 27 cancer types in The Cancer Genome Atlas (TCGA) database. Mutations of SGs in 27 cancer types were analyzed to identify the most common mutated SGs, as well as survival-related SGs. Different SGs were screened out, and SGs with survival significance in different types of tumors were found. Furthermore, TCGA and GTEx datasets were used to further confirm the expressions of SGs in different tumors. Western blot assay was performed to verify the expression of SNRPB protein in colon cancer and lung adenocarcinoma. Three SGs were screened out to establish the Bagging model for tumor diagnosis.

Results: Among 150 SGs, THOC2, PRPF8, SNRNP200, and SF3B1 had the highest mutation rate. The survival time of mutant THOC2 and SF3B1 was better than that of wild type, respectively. The differential expression analysis of 150 SGs between 674 normal tissue samples and 9,163 tumor tissue samples with 27 cancer types of 9070 patients showed that 13 SGs were highly expressed and 1 was low-expressed. For all cancer types, the prognosis (survival time) of the low-expression group of three SGs (SNRPB, LSM7, and HNRNPCL1) was better than the high expression group, respectively (p < 0.05). Cox hazards model showed that male, over 60 years old, clinical stages III-IV, and with highly expressed SNRPB and HNRNPCL1 had a poor prognosis. GEPIA2 website analysis showed that SNRPB and LSM7 were highly expressed in most tumors but not in LAML, showing low expression. Compared with the control group, the expression of SNRPB protein in colon cancer was increased by Western blot (p < 0.05). Enrichment analysis showed that the differential SGs were mainly enriched in RNA splicing and binding. The average error of 10-fold cross-validation of the Bagging model for diagnosed cancer was 0.093, which demonstrates that the Bagging model can effectively diagnose cancer with a small error rate.

Conclusions: This study provided the first landscape of spliceosome changes across 27 cancer types in 9070 patients and revealed that spliceosome was related to tumor progression. Spliceosome may play important an important role in cancer biological processes. These findings are the important scientific data to demonstrate the common and specific changes of spliceosome genes across 27 cancer types, which is a valuable biomarker resource to under the common or specific molecular mechanisms among different cancer types and establish biomarkers and therapeutic targets for the common or specific management of different types of cancer patients to benefit the research and practice of 3P medicine in cancers.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00279-0.