Alternative splicing (AS) has emerged as a key event in tumor development and microenvironment formation. However, comprehensive analysis of AS and its clinical significance in head and neck squamous cell carcinoma (HNSC) is urgently required. Methods: Genome-wide profiling of AS events using RNA-Seq data from The Cancer Genome Atlas (TCGA) program was performed in a cohort of 464 patients with HNSC. Cancer-associated AS events (CASEs) were identified between paired HNSC and adjacent normal tissues and evaluated in functional enrichment analysis. Splicing networks and prognostic models were constructed using bioinformatics tools. Unsupervised clustering of the CASEs identified was conducted and associations with clinical, molecular and immune features were analyzed. Results: We detected a total of 32,309 AS events and identified 473 CASEs in HNSC; among these, 91 were validated in an independent cohort (n = 15). Functional protein domains were frequently altered, especially by CASEs affecting cancer drivers, such as PCSK5. CASE parent genes were significantly enriched in pathways related to HNSC and the tumor immune microenvironment, such as the viral carcinogenesis (FDR < 0.001), Human Papillomavirus infection (FDR < 0.001), chemokine (FDR < 0.001) and T cell receptor (FDR < 0.001) signaling pathways. CASEs enriched in immune-related pathways were closely associated with immune cell infiltration and cytolytic activity. AS regulatory networks suggested a significant association between splicing factor (SF) expression and CASEs and might be regulated by SF methylation. Eighteen CASEs were identified as independent prognostic factors for overall and disease-free survival. Unsupervised clustering analysis revealed distinct correlations between AS-based clusters and prognosis, molecular characteristics and immune features. Immunogenic features and immune subgroups cooperatively depict the immune features of AS-based clusters. Conclusion: This comprehensive genome-wide analysis of the AS landscape in HNSC revealed novel AS events related to carcinogenesis and immune microenvironment, with implications for prognosis and therapeutic responses.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

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