MUC16 affects the biological functions of ovarian cancer cells and induces an antitumor immune response by activating dendritic cells

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Background

Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis.

Methods

Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models.

Results

The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum-paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved.

Conclusions

In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

Construction and analysis of a reliable five-gene prognostic signature for colon adenocarcinoma associated with the wild-type allelic state of the COL6A6 gene

Background

Tumors emerge by acquiring a number of mutations over time. The first mutation provides a selective growth advantage compared to adjacent epithelial cells, allowing the cell to create a clone that can outgrow the cells that surround it. Subsequent mutations determine the risk of the tumor progressing to metastatic cancer. Some secondary mutations may inhibit the aggressiveness of the tumor while still increasing the survival of the clone. Meaningful mutations in genes may provide a strong molecular foundation for developing novel therapeutic strategies for cancer.

Methods

The somatic mutation and prognosis in colon adenocarcinoma (COAD) were analyzed. The copy number variation (CNV) and differentially expressed genes (DEGs) between the collagen type VI alpha 6 chain (COL6A6) mutation (COL6A6-MUT) and the COL6A6 wild-type (COL6A6-WT) subgroups were evaluated. The independent prognostic signatures based on COL6A6-allelic state were determined to construct a Cox model. The biological characteristics and the immune microenvironment between the two risk groups were compared.

Results

COL6A6 was found to be highly mutated in COAD at a frequency of 9%. Patients with COL6A6-MUT had a good overall survival (OS) compared to those with COL6A6-WT, who had a different CNV pattern. Significant differences in gene expression were established for 593 genes between the COL6A6-MUT and COL6A6-WT samples. Among them, MUC16, ASNSP1, PRR18, PEG10, and RPL26P8 were determined to be independent prognostic factors. The internally validated prognostic risk model, constructed using these five genes, demonstrated its value by revealing a significant difference in patient prognosis between the high-risk and low-risk groups. Specifically, patients in the high-risk group exhibited a considerably worse prognosis than did those in the low-risk group. The high-risk group had a significantly higher proportion of patients over 60 years of age and patients in stage III. Moreover, the tumor immune dysfunction and exclusion (TIDE) score and the expression of human leukocyte antigen (HLA) family genes were all higher in the high-risk group than that in the low-risk group.

Conclusions

The allelic state of COL6A6 and the five associated DEGs were identified as novel biomarkers for the diagnosis and prognosis of COAD and may be therapeutic targets in COAD.

Relevance of glyco-biomakers and glycan profiles in cancer stem cells

Altered and aberrant glycosylation signatures have been linked to being a hallmark in a variety of human disorders including cancer. Cancer stem cells (CSCs), capable of self-renewal and differentiation, have recently been credited with a unique notion of disease genesis and implicated as the cause for initiation and recurrence of the disease in a new regime of neoplastic transformations hypothesis. Many biomarkers relating to diagnostic and prognostic intents have been discovered using the ubiquitous and abundant surface glycan patterns on CSCs. Various technological advancements have been developed to identify and determine concerns with glycosylation structure. However, the nature and purpose of the glycan moiety on these glycosylation pattern have not yet been thoroughly investigated. This review, thus, summarizes the process of glycosylation in CSCs, variations in glycosylation patterns in various stem cells, aberrant glycosylation patterns in cancer, the role of glycosylation in tumor cell adhesion, cell-matrix interactions, and signaling, as well as cancer detection and treatment. The function of carbohydrates as prospective serum biomarkers, some clinically authorized biomarkers, and potential novel biomarkers relating to cancer disease diagnosis and prognosis are also discussed in the review.

MUC1 and MUC16: critical for immune modulation in cancer therapeutics

The Mucin (MUC) family, a range of highly glycosylated macromolecules, is ubiquitously expressed in mammalian epithelial cells. Such molecules are pivotal in establishing protective mucosal barriers, serving as defenses against pathogenic assaults. Intriguingly, the aberrant expression of specific MUC proteins, notably Mucin 1 (MUC1) and Mucin 16 (MUC16), within tumor cells, is intimately associated with oncogenesis, proliferation, and metastasis. This association involves various mechanisms, including cellular proliferation, viability, apoptosis resistance, chemotherapeutic resilience, metabolic shifts, and immune surveillance evasion. Due to their distinctive biological roles and structural features in oncology, MUC proteins have attracted considerable attention as prospective targets and biomarkers in cancer therapy. The current review offers an exhaustive exploration of the roles of MUC1 and MUC16 in the context of cancer biomarkers, elucidating their critical contributions to the mechanisms of cellular signal transduction, regulation of immune responses, and the modulation of the tumor microenvironment. Additionally, the article evaluates the latest advances in therapeutic strategies targeting these mucins, focusing on innovations in immunotherapies and targeted drugs, aiming to enhance customization and accuracy in cancer treatments.

The Landscape of Adoptive Cellular Therapies in Ovarian Cancer

Ovarian cancers are typically poorly immunogenic and have demonstrated disappointing responses to immune checkpoint inhibitor (ICI) therapy. Adoptive cellular therapy (ACT) offers an alternative method of harnessing the immune system that has shown promise, especially with the success of chimeric antigen receptor T-cell (CAR-T) therapy in haematologic malignancies. So far, ACT has led to modest results in the treatment of solid organ malignancies. This review explores the possibility of ACT as an effective alternative or additional treatment to current standards of care in ovarian cancer. We will highlight the potential of ACTs, such as CAR-T, T-cell receptor therapy (TCR-T), tumour-infiltrating lymphocytes (TILs) and cell-based vaccines, whilst also discussing their challenges. We will present clinical studies for these approaches in the treatment of immunologically 'cold' ovarian cancer and consider the rationale for future research.

Development and validation of a MUC16 mutation-associated immune prognostic model for lung adenocarcinoma

Mucin 16 (MUC16) mutation ranks third among all common mutations in lung adenocarcinoma (LUAD), and it has a certain effect on LUAD development and prognostic outcome. This research aimed to analyze the effects of MUC16 mutation on LUAD immunophenotype regulation and determine the prognostic outcome using an immune prognostic model (IPM) built with immune-related genes. The MUC16 mutation status and mRNA expression profiles were analyzed using diverse platforms and among several LUAD patients (n = 691). An IPM was then constructed using differentially expressed immune-related genes (DEIRGs) in MUC16MUT LUAD cases, and the data were compared with those of MUC16WT LUAD cases. The IPM's performance in distinguishing high-risk cases from low-risk ones among 691 LUAD cases was verified. Additionally, a nomogram was built and applied in the clinical setting. Furthermore, a comprehensive IPM-based analysis of how MUC16 mutation affected the tumor immune microenvironment (TIME) of LUAD was performed. MUC16 mutation decreased the immune response in LUAD. As revealed by functional annotation, the DEIRGs in the IPM were most significantly enriched in the humoral immune response function and the immune system disease pathway. Moreover, high-risk cases were associated with increased proportions of immature dendritic cells, neutrophils, and B-cells; enhanced type I interferon T-cell response; and increased expression of PD-1, CTLA-4, TIM-3, and LAG3 when compared with low-risk cases. MUC16 mutation shows potent association with TIME of LUAD. The as-constructed IPM displays high sensitivity to MUC16 mutation status and can be applied to discriminate high-risk LUAD cases from low-risk ones.

An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma

Background: Ovarian Serous Adenocarcinoma is a malignant tumor originating from epithelial cells and one of the most common causes of death from gynecological cancers. The objective of this study was to develop a prediction model based on extracellular matrix proteins, using artificial intelligence techniques. The model aimed to aid healthcare professionals to predict the overall survival of patients with ovarian cancer (OC) and determine the efficacy of immunotherapy. Methods: The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection was used as the study dataset, whereas the TCGA-Pancancer dataset was used for validation. The prognostic importance of 1068 known extracellular matrix proteins for OC were determined by the Random Forest algorithm and the Lasso algorithm establishing the ECM risk score. Based on the gene expression data, the differences in mRNA abundance, tumour mutation burden (TMB) and tumour microenvironment (TME) between the high- and low-risk groups were assessed. Results: Combining multiple artificial intelligence algorithms we were able to identify 15 key extracellular matrix genes, namely, AMBN, CXCL11, PI3, CSPG5, TGFBI, TLL1, HMCN2, ESM1, IL12A, MMP17, CLEC5A, FREM2, ANGPTL4, PRSS1, FGF23, and confirm the validity of this ECM risk score for overall survival prediction. Several other parameters were identified as independent prognostic factors for OC by multivariate COX analysis. The analysis showed that thyroglobulin (TG) targeted immunotherapy was more effective in the high ECM risk score group, while the low ECM risk score group was more sensitive to the RYR2 gene-related immunotherapy. Additionally, the patients with low ECM risk scores had higher immune checkpoint gene expression and immunophenoscore levels and responded better to immunotherapy. Conclusion: The ECM risk score is an accurate tool to assess the patient's sensitivity to immunotherapy and forecast OC prognosis.

MUC16 mutation is associated with tumor grade, clinical features, and prognosis in glioma patients

MUC16 is a member of the attached mucin family that encodes cancer antigen 125 (CA-125), but the association of MUC16 status with grade and subtypes of glioma patients has not yet been established. Data for MUC16 mRNA expression in 37 different cancer types were considered, and genomic data from the Cancer Genome Atlas (TCGA) from 1051 low-grade glioma (LGG) patients and 833 glioblastoma (GBM) patients were analyzed. LGG and GBM has low expression of MUC16, but it is frequently mutated in GBM. Kaplan-Meier survival analysis, glioma subtypes, methylation, and isocitrate dehydrogenase (IDH1) status were all performed. We found that mutated-MUC16 in LGG patients is associated with better prognosis considering overall survival (OS), IDH1, methylation, 1p/19q, and 10q status. Conversely, MUC16 mutation were related with worse prognosis in GBM patients upon analyzing those same parameters. Therefore, MUC16 mutations may assist in glioma diagnosis and prognosis and should be further studied in this tumor type.

Nanotechnology-Based Nucleic Acid Vaccines for Treatment of Ovarian Cancer

Anticancer vaccines represent a promising approach for effective treatment of cancer and along with recent advantages of nucleic acid-based vaccines for other diseases form a prospective and potentially efficacious direction of the research, development and clinical applications. Despite the ongoing several clinical trials of mRNA vaccines for the treatment of various types of cancer, to-date no cancer vaccines were approved by the US Food and Drug Administration. The present review analyzes and summarizes major approaches for treating of different forms of ovarian cancer including mRNA-based vaccines as well as nanotechnology-based approaches for their delivery.

Whole-exome sequencing identifies a set of genes as markers of hepatocellular carcinoma early recurrence

BACKGROUND

Hepatocellular carcinoma (HCC) is characterized by a high recurrence rate and poor prognosis. In recent years, the therapeutic regimen of programmed cell death protein 1 (PD-1) antibody combined with multi-targeted tyrosine kinase inhibitors (mTKIs) has achieved better results in the clinical application of hepatocellular carcinoma. Whole-exome sequencing can reflect the mutational characteristics of patients' exons and guide the clinical selection of molecular targeting drugs more accurately, which is in line with the concept of precision medicine.

METHODS

We performed exome sequencing on 63 patients with HCC treated with radical surgery at our hospital and collected their clinical indexes and postoperative follow-up data. Using machine learning, a prediction model for recurrence within 1 year was constructed and the model was presented in a nomogram. Patients treated with PD-1 antibodies in combination with mTKIs after relapse were grouped by prognosis, and the valuable mutated genes were screened according to whole-exome sequencing data. The tumor tissue immune cells were analyzed using the UCSC Xena database. The expressions of target proteins were verified by Polymerase Chain Reaction (PCR) and Immunohistochemistry (IHC), respectively, on commercial HCC cell lines and pathological specimens of hepatocellular carcinoma collected clinically.

RESULTS

The proportion of patients who relapsed within a year was 41% and the prognosis of those patients was poor. The characteristic exon mutation profile with a high frequency of variants in multiple mucin genes was present in Chinese HCC patients. Multiple nidi and 30 exon variants were brought into the prediction model with an area under the curve (AUC) = 0.94. MUC6 gene mutation was obvious in patients with an early recurrence, and MUC3A and MUC4 gene mutations were evident in patients with poorer responses to PD-1 antibodies combined with mTKIs. Those three mucins were negatively correlated with immune infiltrating cells.

CONCLUSIONS

We depicted the exon characteristics of hepatocellular carcinoma in the Chinese population and established a predictive model for recurrence within 1 year after radical surgical treatment. Moreover, we found that mucins were worthy targets of hepatocellular carcinoma.

[MUC16: The Novel Target for Tumor Therapy]

Mucin16 (MUC16), also known as carbohydrate antigen 125 (CA125), is a glycoprotein antigen that can be recognized by the monoclonal antibody OC125 detected from epithelial ovarian carcinoma antigen by Bast et al in 1981. CA125 is not present in normal ovarian tissue but is usually elevated in the serum of epithelial ovarian carcinoma patients. CA125 is the most commonly used serologic biomarker for the diagnosis and recurrence monitoring of epithelial ovarian carcinoma. MUC16 is highly expressed in varieties of tumors. MUC16 can interact with galectin-1/3, mesothelin, sialic acid-binding immunoglobulin-type lectins-9 (Siglec-9), and other ligands. MUC16 plays an important role in tumor genesis, proliferation, migration, invasion, and tumor immunity through various signaling pathways. Besides, therapies targeting MUC16 have some significant achievements. Related preclinical studies and clinical trials are in progress. MUC16 may be a potential novel target for tumor therapy. This article will review the mechanism of MUC16 in tumor genesis and progression, and focus on the research actuality of MUC16 in tumor therapy. This article also provides references for subsequent tumor therapy studies targeting MUC16.
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Downregulated ADARB1 Facilitates Cell Proliferation, Invasion and has Effect on the Immune Regulation in Ovarian Cancer

Ovarian cancer (OC) is typically diagnosed at an advanced stage and poses a significant challenge to treatment and recovery. Rencently, Adenosine deaminase RNA-specific B1 (ADARB1), an adenosine-to-inosine (A-to-I) RNA-editing enzyme, has been found to play an essential role in the development of cancer. However, the specific function of ADARB1 in ovarian cancer is still not fully understood. Here, we investigated the effects of ADARB1 on OC biology. By conducting bioinformatics analyses of several public databases, we found significantly decreased ADARB1 expression in OC cells and tissues. Moreover, RT-PCR and western blot showed lower ADARB1 expression in OVCAR3, HO8910pm and A2780 OC cells compared to human normal ovarian epithelial cell IOSE. Cell proliferation assay and clone formation assay showed that overexpression of ADARB1 (ADARB1-OE) inhibited the proliferation of tumor cells. Wound healing and transwell assay indicated that ADARB1-OE could suppress OC cell invasion and metastasis. Kaplan-Meier methods revealed that the patients with low level of ADARB1 displayed poor prognosis. TISIDB databases were further used to analyze the roles of ADARB1 in tumor-immune system interactions in OC patients. Furthermore, ADARB1-OE down-regulated the expression of phosphorylated AKT. Combination of ADARB1-OE and AKT inhibitor MK2206 exerted stronger cell growth inhibition. Thus, our investigation demonstrated that low levels of ADARB1 might be a potential target in the tumorigenesis and prognostic evaluation of OC patients.