Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6) is an immunoglobulin superfamily protein primarily expressed on epithelial surfaces and myeloid cells. It plays a significant role in cancer progression by inhibiting apoptosis, promoting drug resistance, and facilitating cancer cell invasion and metastasis. Overexpression of CEACAM6 has been observed in various cancers, including lung, breast, colorectal, and hepatocellular cancers, and is associated with poorer overall survival and disease-free survival. Its differential expression on tumor cell surfaces makes it a promising cancer marker. This review aims to provide a comprehensive summary of CEACAM6’s role in different cancer types, its involvement in signaling pathways, and recent advancements in CEACAM6-targeted treatments.

Aims: We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD). Background: STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD. Objective: The objective of this study is to investigate the molecular subtypes and prognostic model for STAD. Methods: A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature. Results: We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets. Conclusion: This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

This article examines the multifaceted roles of the S100P gene in pan‑cancer, with the aim of exploring its biological functions and related mechanisms in depth. S100P is a small calcium‑binding protein that recent studies have identified as playing a significant role in the occurrence and progression of various cancers. As research on cancer biomarkers advances, the relationship between S100P expression levels and cancer prognosis, metastasis and invasiveness has garnered increasing attention. However, the specific mechanisms underlying the role of S100P in different cancer types remain elusive and related research is still in the exploratory phase. Therefore, this review systematically summarizes the biological functions of S100P, clarifying its signaling pathways and regulatory mechanisms. This work provides new insights and strategies for targeted therapy and establishes a theoretical basis for subsequent clinical applications. Through this summary, the present review aims to enhance personalized treatment approaches for S100P‑related cancers and strengthen future explorations of S100P.

Microsatellite instability (MSI) serves as an important therapeutic and prognostic marker. Immunohistochemistry (IHC) and polymerase chain reaction (PCR) have long been regarded as the “gold standard” for MSI detection. In recent years, next-generation sequencing (NGS)-based methods, offering expanded target coverage of microsatellite (MS) loci and improved analytical performance, have gained widespread acceptance. However, discrepancies between NGS and traditional methods have been occasionally reported. In this study, we conducted a large-scale retrospective analysis of MSI results of 35,563 Chinese pan-cancer cases which underwent a NGS test in a central clinical laboratory. Our work introduced a novel algorithm for NGS-based MSI detection, examined MSI-H prevalence and value distribution across cancer types, identifies MSI-associated genes and variants, evaluates the MSI detection discordance between PCR and NGS in a pan-cancer context, and distilled 7 MS loci suitable for pan-cancer MSI detection.

Tumor metabolism plays a critical role in tumor progression. However, the interaction between metabolism and tumor microenvironment (TME) has not been comprehensively revealed in colon adenocarcinoma (COAD). We used unsupervised consensus clustering to establish three molecular subtypes (clusters) based on the enrichment score of four major metabolism pathways in TCGA-COAD dataset. GSE17536 was used as a validation dataset. Single-cell RNA sequencing data (GSE161277) was employed to further verify the reliability of subtyping and characterize the correlation between metabolism and TME. Three clusters were identified and they performed distinct prognosis and molecular features. Clust3 had the worst overall survival and the highest enrichment score of glycolysis. 86 differentially expressed genes (DEGs) were identified, in which 11 DEGs were associated with favorable prognosis and 75 DEGs were associated with poor prognosis. Striking correlations were observed between hypoxia and glycolysis, clust3 and hypoxia, and clust3 and angiogenesis ( ). We constructed a molecular subtyping system which was effective and reliable for predicting COAD prognosis. The 86 identified key DEGs may be greatly involved in COAD progression, and they provide new perspectives and directions for further understanding the mechanism of metabolism in promoting aggressive phenotype by interacting with TME.