Abnormal cell proliferation and growth leading to cancer primarily result from cumulative genome mutations. Single gene mutations alone do not fully explain cancer onset and progression; instead, clustered mutations-simultaneous occurrences of multiple mutations-are considered to be pivotal in cancer development and advancement. These mutations can affect different genes and pathways, resulting in cells undergoing malignant transformation with multiple functional abnormalities. Clustered mutations influence cancer growth rates, metastatic potential, and drug treatment sensitivity. This summary highlights the various types and characteristics of clustered mutations to understand their associations with carcinogenesis and discusses their potential clinical significance in cancer. As a unique mutation type, clustered mutations may involve genomic instability, DNA repair mechanism defects, and environmental exposures, potentially correlating with responsiveness to immunotherapy. Understanding the characteristics and underlying processes of clustered mutations enhances our comprehension of carcinogenesis and cancer progression, providing new diagnostic and therapeutic approaches for cancer.

High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SIGNIFICANCE: OC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.

BACKGROUND: Pancreatic adenocarcinoma (PAAD), a member of highly lethal malignant tumors, has a poor outcome and extremely poor prognosis. The transient receptor potential (TRP) superfamily, a group of nonselective cation channels, is capable of influencing cellular functions by regulating calcium homeostasis. In addition, it has been shown that TRP channels can also affect various cellular phenotypes by regulating gene transcription levels and are involved in the development of a variety of malignant tumors.
OBJECTIVE: In order to find new therapeutic targets and biomarkers to improve the clinical prognosis of pancreatic cancer, we performed genetic and immunological characterization of TRP channels in PAAD, as well as related functional and prognostic analyses.
RESULTS: We investigated the expression, genetic alterations, methylation levels, and immune infiltration levels of TRP channels in PAAD, and further also analyzed the function of TRP channels in PAAD and their prognostic value for PAAD patients. Our results suggest that TRPM8 may contribute to tumor proliferation by controlling the PI3K-AKT-mTOR signaling pathway in PAAD.
CONCLUSIONS: After careful evaluation of the accumulated data, we concluded that TRPM8 has potential as a prognostic indicator and prospective therapeutic target in PAAD.

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.

The CCN family is a group of matricellular proteins associated with the extracellular matrix. This study aims to explore the role of the CCN family in glioma development and its implications in the tumor microenvironment. Through analysis of bulk RNA-seq cohorts, correlations between CCN family expression and glioma subtypes, patient survival, and bioactive pathway enrichment were investigated. Additionally, single-cell datasets were employed to identify novel cell subgroups, followed by analyses of cell communication and transcription factors. Spatial transcriptomic analysis was utilized to validate the CCN family\'s involvement in glioma. Results indicate overexpression of CYR61,CTGF, and WISP1 in glioma, associated with unfavorable subtypes and reduced survival. Enrichment analyses revealed associations with oncogenic pathways, while CTGF and WISP1 expression correlated with increased infiltration of regulatory T cells and M2 macrophages. Single-cell analysis identified MES-like cells as the highest CCN expression. Moreover, intercellular signal transduction analysis demonstrated active pathways, including SPP1-CD44, in cell subgroups with elevated CYR61 and CTGF expression. Spatial transcriptomic analysis confirmed co-localization of CYR61,CTGF and SPP1-CD44 with high oncogenic pathway activity. These findings suggest that CCN family members may serve as potential prognostic biomarkers and therapeutic targets for glioma.

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Colorectal cancer (CRC) is a major worldwide health issue, with high rates of both occurrence and mortality. Dysregulation of the transforming growth factor-beta (TGF-β) signaling pathway is recognized as a pivotal factor in CRC pathogenesis. Notably, the INHBA gene and long non-coding RNAs (lncRNAs) have emerged as key contributors to CRC progression. The aim of this research is to explore the immunological roles of INHBA and PELATON in CRC through a combination of computational predictions and experimental validations, with the goal of enhancing diagnostic and therapeutic strategies. In this study, we utilized bioinformatics analyses, which involved examining differential gene expression (DEG) in the TCGA-COAD dataset and exploring the INHBA gene in relation to the TGF-β pathway. Additionally, we analyzed mutations of INHBA, evaluated the microenvironment and tumor purity, investigated the INHBA\'s connection to immune checkpoint inhibitors, and measured its potential as an immunotherapy target using the TIDE score. Utilizing bioinformatics analyses of the TCGA-COAD dataset beside experimental methodologies such as RT-qPCR, our investigation revealed significant upregulation of INHBA in CRC. As results, our analysis of the protein-protein interaction network associated with INHBA showed 10 interacting proteins that play a role in CRC-associated processes. We observed a notable prevalence of mutations within INHBA and explored its correlation with the response to immune checkpoint inhibitors. Our study highlights INHBA as a promising target for immunotherapy in CRC. Moreover, our study identified PELATON as a closely correlated lncRNA with INHBA, with experimental validation confirming their concurrent upregulation in CRC tissues. Thus, these findings highlight the importance of INHBA and PELATON in driving CRC progression, suggesting their potential utility as diagnostic and prognostic biomarkers. By integrating computational predictions with experimental validations, this research enhances our understanding of CRC pathogenesis and uncovers prospects for personalized therapeutic interventions.