Colorectal cancer (CRC) is an aggressive malignancy with poor prognosis. It is imperative to elucidate the potential molecular mechanisms that regulate CRC cell aggressiveness. In present study, the transient receptor potential melastatin 4 (TRPM4), a calcium-activated nonselective cation channel, is downregulated in CRC as a novel methylated tumor suppressor gene (TSG). The reduced mRNA level of TRPM4 is due to the epigenetic methylation of its promoter CpG island (CGI). Moreover, ectopic expression of TRPM4 inhibited tumor growth and metastasis both in vitro and in vivo. Our experiments also demonstrate that TRPM4 restructures the CRC cytoskeleton and activates the Ca²⁺-mediated calpain pathway through enhancing calcium influx. The western blot analysis shows that the expression of focal adhesion kinase (FAK), a calpain-mediated proteolytic substrate, is markedly suppressed after ectopic overexpression of TRPM4, besides, Akt (also known as protein kinase B, PKB), phosphatidylinositol 3-kinase (PI3K) as well as its central target mTOR have significantly decreased expression accompanied by elevated E-cadherin and restrained matrix metalloproteinases (MMP2/MMP9) expression. The inhibition of protease calpain effectively relieves the retard of FAK/Akt signals and reverses the migration suppression of TRPM4. Taken together, TRPM4, identified as a novel methylated TSG, employs intracellular Ca²⁺ signals to activate calpain-mediated cleavage of FAK and impede CRC migration and invasion through modulating the PI3K/Akt/mTOR signaling cascade, providing the first evidence that TRPM4 is likely to be a significant biomarker and potential target for CRC therapy.

Background: Chat Generative Pretrained Transformer (ChatGPT) is a type of large language model (LLM) developed by OpenAI, known for its extensive knowledge base and interactive capabilities. These attributes make it a valuable tool in the medical field, particularly for tasks such as answering medical questions, drafting clinical notes, and optimizing the generation of radiology reports. However, keeping accuracy in medical contexts is the biggest challenge to employing GPT-4 in a clinical setting. This study aims to investigate the accuracy of GPT-4, which can process both text and image inputs, in generating diagnoses from pathological images.Methods: This study analyzed 44 histopathological images from 16 organs and 100 colorectal biopsy photomicrographs. The initial evaluation was conducted using the standard GPT-4 model in January 2024, with a subsequent re-evaluation performed in July 2024. The diagnostic accuracy of GPT-4 was assessed by comparing its outputs to a reference standard using statistical measures. Additionally, four pathologists independently reviewed the same images to compare their diagnoses with the model’s outputs. Both scanned and photographed images were tested to evaluate GPT-4’s generalization ability across different image types.Results: GPT-4 achieved an overall accuracy of 0.64 in identifying tumor imaging and tissue origins. For colon polyp classification, accuracy varied from 0.57 to 0.75 in different subtypes. The model achieved 0.88 accuracy in distinguishing low-grade from high-grade dysplasia and 0.75 in distinguishing high-grade dysplasia from adenocarcinoma, with a high sensitivity in detecting adenocarcinoma. Consistency between initial and follow-up evaluations showed slight to moderate agreement, with Kappa values ranging from 0.204 to 0.375.Conclusion: GPT-4 demonstrates the ability to diagnose pathological images, showing improved performance over earlier versions. Its diagnostic accuracy in cancer is comparable to that of pathology residents. These findings suggest that GPT-4 holds promise as a supportive tool in pathology diagnostics, offering the potential to assist pathologists in routine diagnostic workflows.

Cervical squamous intraepithelial lesions (SILs) are precursor lesions of cervical cancer, and their accurate diagnosis enables patients to be treated before malignancy manifests. However, the identification of SILs is usually laborious and has low diagnostic consistency due to the high similarity of pathological SIL images. Although artificial intelligence (AI), especially deep learning algorithms, has drawn a lot of attention for its good performance in cervical cytology tasks, the use of AI for cervical histology is still in its early stages. The feature extraction, representation capabilities, and use of p16 immunohistochemistry (IHC) among existing models are inadequate. Therefore, in this study, we first designed a squamous epithelium segmentation algorithm and assigned the corresponding labels. Second, p16-positive area of IHC slides were extracted with Whole Image Net (WI-Net), followed by mapping the p16-positive area back to the H&E slides and generating a p16-positive mask for training. Finally, the p16-positive areas were inputted into Swin-B and ResNet-50 to classify the SILs. The dataset comprised 6171 patches from 111 patients; patches from 80% of the 90 patients were used for the training set. The accuracy of the Swin-B method for high-grade squamous intraepithelial lesion (HSIL) that we propose was 0.914 [0.889–0.928]. The ResNet-50 model for HSIL achieved an area under the receiver operating characteristic curve (AUC) of 0.935 [0.921–0.946] at the patch level, and the accuracy, sensitivity, and specificity were 0.845, 0.922, and 0.829, respectively. Therefore, our model can accurately identify HSIL, assisting the pathologist in solving actual diagnostic issues and even directing the follow-up treatment of patients.

Microbial communities significantly inhabit the human body. Evidence shows the interaction between the human microbiome and host cells plays a central role in multiple physiological processes and organ microenvironments. However, the majority of related studies focus on gut microbiota or specific tissues/organs, and the component signature of intratumor microbiota across various cancer types remains unclear. Here, we systematically analyzed the correlation between intratumor microbial signature with survival outcomes, genomic features, and immune profiles across 32 cancer types based on the public databases of Bacteria in Cancer (BIC) and The Cancer Genome Atlas (TCGA). Results showed the relative abundance of microbial taxa in tumors compared to normal tissues was observed as particularly noticeable. Survival analysis found that specific candidate microbial taxa were correlated with prognosis across various cancers. Then, a microbial-based scoring system (MS), which was composed of 64 candidate prognostic microbes, was established. Further analyses showed significant differences in survival status, genomic function, and immune profiles among the distinct MS subgroups. Taken together, this study reveals the diversity and complexity of microbiomes in tumors. Classifying cancer into different subtypes based on intratumor microbial signatures might reasonably reflect genomic characteristics, immune features, and survival status.

Background: Breast cancer (BC) is the most common malignant tumor and the leading cause of cancer-related death in women worldwide. Pyroptosis and long noncoding RNAs (lncRNAs) have been demonstrated to play vital roles in the tumorigenesis and development of BC. However, the clinical significance of pyroptosis-related lncRNAs in BC remains unclear.Methods: Using the mRNA and lncRNA profiles of BC obtained from TCGA dataset, a risk model based on the pyroptosis-related lncRNAs for prognosis was constructed using univariate and multivariate Cox regression model, and least absolute shrinkage and selection operator. Patients were divided into high- and low-risk groups based on the risk model, and the prognosis value and immune response in different risk groups were analyzed. Furthermore, functional enrichment annotation, therapeutic signature, and tumor mutation burden were performed to evaluate the risk model we established. Moreover, the expression level and clinical significance of the selected pyroptosis-related lncRNAs were further validated in BC samples.Results: 3,364 pyroptosis-related lncRNAs were identified using Pearson’s correlation analysis. The risk model we constructed comprised 10 pyroptosis-related lncRNAs, which was identified as an independent predictor of overall survival (OS) in BC. The nomogram we constructed based on the clinicopathologic features and risk model yielded favorable performance for prognosis prediction in BC. In terms of immune response and mutation status, patients in the low-risk group had a higher expression of immune checkpoint markers and exhibited higher fractions of activated immune cells, while the high-risk group had a highly percentage of TMB. Further analyses in our cohort BC samples found that RP11-459E5.1 was significantly upregulated, while RP11-1070N10.3 and RP11-817J15.3 were downregulated and significantly associated with worse OS.Conclusion: The risk model based on the pyroptosis-related lncRNAs we established may be a promising tool for predicting the prognosis and personalized therapeutic response in BC patients.