Identification and validation of an alternative splicing-based prognostic signature for head and neck squamous cell carcinoma

RNA-Based Liquid Biopsy in Head and Neck Cancer

Head and neck cancer (HNC) is a prevalent and diverse group of malignancies with substantial morbidity and mortality rates. Early detection and monitoring of HNC are crucial for improving patient outcomes. Liquid biopsy, a non-invasive diagnostic approach, has emerged as a promising tool for cancer detection and monitoring. In this article, we review the application of RNA-based liquid biopsy in HNC. Various types of RNA, including messenger RNA (mRNA), microRNA (miRNA), long non-coding RNA (lncRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), circular RNA (circRNA) and PIWI-interacting RNA (piRNA), are explored as potential biomarkers in HNC liquid-based diagnostics. The roles of RNAs in HNC diagnosis, metastasis, tumor resistance to radio and chemotherapy, and overall prognosis are discussed. RNA-based liquid biopsy holds great promise for the early detection, prognosis, and personalized treatment of HNC. Further research and validation are necessary to translate these findings into clinical practice and improve patient outcomes.

Identification of tumor antigens and immune subtypes in head and neck squamous cell carcinoma for mRNA vaccine development

The mRNA vaccines have been considered effective for combating cancer. However, the core components of the mRNA vaccines against head and neck squamous cell carcinoma (HNSCC) and the effects remain unclear. Our study aims to identify effective antigens in HNSCC to develop mRNA vaccines for corresponding potential patients. Here, we analyzed alternative splicing and mutation of genes in TCGA-HNSCC samples and identified seven potential tumor antigens, including SREBF1, LUC7L3, LAMA5, PCGF3, HNRNPH1, KLC4, and OFD1, which were associated with nonsense-mediated mRNA decay factor expression, overall survival prognosis and the infiltration of antigen-presenting cells. Furthermore, to select suitable patients for vaccination, immune subtypes related to HNSCC were identified by consensus clustering analysis, and visualization of the HNSCC immune landscape was performed by graph-learning-based dimensionality reduction. To address the heterogeneity of the population that is suitable for vaccination, plot cell trajectory and WGCNA were also utilized. HNSCC patients were classified into three prognostically relevant immune subtypes (Cluster 1, Cluster 2, and Cluster 3) possessing different molecular and cellular characteristics, immune modulators, and mutation statuses. Cluster 1 had an immune-activated phenotype and was associated with better survival, while Cluster 2 and Cluster 3 were immunologically cold and linked to increased tumor mutation burden. Therefore, HNSCC patients with immune subtypes Cluster 2 and Cluster 3 are potentially suitable for mRNA vaccination. Moreover, the prognostic module hub genes screened seven genes, including IGKC, IGHV3-15, IGLV1-40, IGLV1-51, IGLC3, IGLC2, and CD79A, which could be potential biomarkers to predict prognosis and identify suitable patients for mRNA vaccines. Our findings provide a theoretical basis for further research and the development of anti-HNSCC mRNA vaccines and the selection of suitable patients for vaccination.

Construction of the prognostic signature of alternative splicing revealed the prognostic predictor and immune microenvironment in head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinoma (HNSC) is a prevalent and heterogeneous malignancy with poor prognosis and high mortality rates. There is significant evidence of alternative splicing (AS) contributing to tumor development, suggesting its potential in predicting prognosis and therapeutic efficacy. This study aims to establish an AS-based prognostic signature in HNSC patients.

Methods: The expression profiles and clinical information of 486 HNSC patients were downloaded from the TCGA database, and the AS data were downloaded from the TCGA SpliceSeq database. The survival-associated AS events were identified by conducting a Cox regression analysis and utilized to develop a prognostic signature by fitting into a LASSO-regularized Cox regression model. Survival analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to evaluate the signature and an independent cohort was used for validation. The immune cell function and infiltration were analyzed by CIBERSORT and the ssGSEA algorithm.

Results: Univariate Cox regression analysis identified 2726 survival-associated AS events from 1714 genes. The correlation network reported DDX39B, PRPF39, and ARGLU1 as key splicing factors (SF) regulating these AS events. Eight survival-associated AS events were selected and validated by LASSO regression to develop a prognostic signature. It was confirmed that this signature could predict HNSC outcomes independent of other variables via multivariate Cox regression analysis. The risk score AUC was more than 0.75 for 3 years, highlighting the signature’s prediction capability. Immune infiltration analysis reported different immune cell distributions between the two risk groups. The immune cell content was higher in the high-risk group than in the low-risk group. The correlation analysis revealed a significant correlation between risk score, immune cell subsets, and immune checkpoint expression.

Conclusion: The prognostic signature developed from survival-associated AS events could predict the prognosis of HNSC patients and their clinical response to immunotherapy. However, this signature requires further research and validation in larger cohort studies.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis.

Methods

The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method.

Results

Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage).

Conclusion

The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

Alternative RNA Splicing-The Trojan Horse of Cancer Cells in Chemotherapy

Almost all transcribed human genes undergo alternative RNA splicing, which increases the diversity of the coding and non-coding cellular landscape. The resultant gene products might have distinctly different and, in some cases, even opposite functions. Therefore, the abnormal regulation of alternative splicing plays a crucial role in malignant transformation, development, and progression, a fact supported by the distinct splicing profiles identified in both healthy and tumor cells. Drug resistance, resulting in treatment failure, still remains a major challenge for current cancer therapy. Furthermore, tumor cells often take advantage of aberrant RNA splicing to overcome the toxicity of the administered chemotherapeutic agents. Thus, deciphering the alternative RNA splicing variants in tumor cells would provide opportunities for designing novel therapeutics combating cancer more efficiently. In the present review, we provide a comprehensive outline of the recent findings in alternative splicing in the most common neoplasms, including lung, breast, prostate, head and neck, glioma, colon, and blood malignancies. Molecular mechanisms developed by cancer cells to promote oncogenesis as well as to evade anticancer drug treatment and the subsequent chemotherapy failure are also discussed. Taken together, these findings offer novel opportunities for future studies and the development of targeted therapy for cancer-specific splicing variants.

Identification and Characterization of Alternatively Spliced Transcript Isoforms of IRX4 in Prostate Cancer

Alternative splicing (AS) is tightly regulated to maintain genomic stability in humans. However, tumor growth, metastasis and therapy resistance benefit from aberrant RNA splicing. Iroquois-class homeodomain protein 4 (IRX4) is a TALE homeobox transcription factor which has been implicated in prostate cancer (PCa) as a tumor suppressor through genome-wide association studies (GWAS) and functional follow-up studies. In the current study, we characterized 12 IRX4 transcripts in PCa cell lines, including seven novel transcripts by RT-PCR and sequencing. They demonstrate unique expression profiles between androgen-responsive and nonresponsive cell lines. These transcripts were significantly overexpressed in PCa cell lines and the cancer genome atlas program (TCGA) PCa clinical specimens, suggesting their probable involvement in PCa progression. Moreover, a PCa risk-associated SNP rs12653946 genotype GG was corelated with lower IRX4 transcript levels. Using mass spectrometry analysis, we identified two IRX4 protein isoforms (54.4 kDa, 57 kDa) comprising all the functional domains and two novel isoforms (40 kDa, 8.7 kDa) lacking functional domains. These IRX4 isoforms might induce distinct functional programming that could contribute to PCa hallmarks, thus providing novel insights into diagnostic, prognostic and therapeutic significance in PCa management.

Genome-Wide Analyses of Prognostic and Therapeutic Alternative Splicing Signatures in Bladder Urothelial Carcinoma

Background

Alternative splicing (AS) is an indispensable post-transcriptional modification applied during the maturation of mRNA, and AS defects have been associated with many cancers. This study was designed to thoroughly analyze AS events in bladder urothelial carcinoma (BLCA) at the genome-wide level.

Methods

We adopted a gap analysis to screen for significant differential AS events (DASEs) associated with BLCA. DASEs with prognostic value for OS and the disease-free interval (DFI) were identified by Cox analysis. In addition, a differential AS network and AS clusters were identified using unsupervised cluster analysis. We examined differences in the sensitivity to chemotherapy and immunotherapy between BLCA patients with high and low overall survival (OS) risk.

Results

An extensive number of DASEs (296) were found to be clinically relevant in BLCA. A prognosis model was established based prognostic value of OS and DFI. CUGBP elav-like family member 2 (CELF2) was identified as a hub splicing factor for AS networks. We also identified AS clusters associated with OS using unsupervised cluster analysis, and we predicted that the effects of cisplatin and gemcitabine chemotherapy would be different between high- and low-risk groups based on OS prognosis.

Conclusion

We completed a comprehensive analysis of AS events in BLCA at the genome-wide level. The present findings revealed that DASEs and splicing factors tended to impact BLCA patient survival and sensitivity to chemotherapy drugs, which may provide novel prospects for BLCA therapies.

Genetic Drivers of Head and Neck Squamous Cell Carcinoma: Aberrant Splicing Events, Mutational Burden, HPV Infection and Future Targets

Head and neck cancers include cancers that originate from a variety of locations. These include the mouth, nasal cavity, throat, sinuses, and salivary glands. These cancers are the sixth most diagnosed cancers worldwide. Due to the tissues they arise from, they are collectively named head and neck squamous cell carcinomas (HNSCC). The most important risk factors for head and neck cancers are infection with human papillomavirus (HPV), tobacco use and alcohol consumption. The genetic basis behind the development and progression of HNSCC includes aberrant non-coding RNA levels. However, one of the most important differences between healthy tissue and HNSCC tissue is changes in the alternative splicing of genes that play a vital role in processes that can be described as the hallmarks of cancer. These changes in the expression profile of alternately spliced mRNA give rise to various protein isoforms. These protein isoforms, alternate methylation of proteins, and changes in the transcription of non-coding RNAs (ncRNA) can be used as diagnostic or prognostic markers and as targets for the development of new therapeutic agents. This review aims to describe changes in alternative splicing and ncRNA patterns that contribute to the development and progression of HNSCC. It will also review the use of the changes in gene expression as biomarkers or as the basis for the development of new therapies.

Development and validation of a hypoxia-related gene pair signature to predict overall survival in head and neck squamous cell carcinoma

OBJECTIVE

Head and neck squamous cell carcinoma (HNSCC) are a highly aggressive tumor with an extremely poor prognosis. Thus, we aimed to develop and validate a robust prognostic signature that can estimate the prognosis for HNSCC.

METHODS

Data on gene expressions and clinical were downloaded from TCGA and GEO database. To develop the best prognosis signature, a LASSO Cox Regression model was employed. Time-dependent receiver-operating characteristic (ROC) was used to determine the best cut-off value. Patients were divided into high-risk and low-risk hypoxia groups according to cut-off value. Survival differences were evaluated by log-rank test, while multivariate analysis was performed by a Cox proportional hazards model.

RESULTS

A 17-HRGPs composed of 24 unique genes was constructed, which was significantly related to OS. In the TCGA and GEO datasets, patients in the high hypoxia risk group have a poor prognosis (TCGA: P < 0.001, GEO: P < 0.05). After adjusting for other clinicopathological parameters, the 17-HRGP signature was independent prognostic factors in patients with HNSCC (P < 0.05). Functional analysis revealed that mRNA binding, gene silencing by RNA, RNA binding involved in posttranscriptional gene silencing signaling pathway were enriched in the low-risk groups. For this model, C-index was 0.684, which was higher than that of many established risk models. Macrophages M0, Mast cells activated, NK cells resting, T cells CD4 memory resting, etc. were significantly higher in the high-risk group, and B cells memory, Plasma cells, T cells follicular helper, T cells gamma delta, T cells CD8, etc. were significantly higher in the low-risk group.

CONCLUSION

In summary, our study constructed a robust HRGPs signature as molecular markers for predicting the outcome of HNSCC patients.