Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignment between image and transcriptomic features. To address these limitations, we propose a unified multimodal learning framework that integrates histological imaging and spatial transcriptomics through a shared latent representation space. Specifically, histological H&E images are encoded by a ResNet50-based convolutional stem and a MobileViT Transformer backbone to extract hierarchical visual representations. Both modalities are projected into a shared latent space via linear-GELU-dropout transformation blocks, enabling cross-modal alignment through a contrastive learning objective that maximizes agreement between the corresponding image and the spot embeddings. Experimental results on the 10x Genomics Visium dataset of human liver tissue demonstrate that MViTGene achieves significantly higher prediction accuracy than existing methods across multiple gene subsets, with improvements of 20%, 33%, and 12% in predicting marker genes, highly expressed genes, and highly variable genes, respectively. The significant improvement in relevance indicates that the model can more accurately capture the true correspondence between tissue morphology and gene expression, therefore enabling more reliable biological interpretation. It provides a computational tool for high-throughput spatial gene expression prediction that balances performance and interpretability.

Accurate molecular subtyping of breast cancer is essential for personalized therapy, yet inconsistencies between biopsy and postoperative immunohistochemistry (IHC) due to tumor heterogeneity and sampling limitations pose clinical challenges. We developed Ultrasound-Guided Pathomic Subtyping (US-GPS), a cross-modal framework enabling subtype prediction using ultrasound (US) alone, guided by IHC-stained whole-slide images (WSIs) during training. A total of 1283 patients from four centers were retrospectively included, with 454 cases (paired US and WSIs) for model training/validation and 829 US-only cases for external testing. US-GPS aligned WSI-derived patch- and region-level features (from HER2, ER, PR, Ki-67 IHC stains) with handcrafted and deep US features via co-attention and contrastive learning. Using US alone, the model achieved an AUROC of 0. 966 (95% CI 0.960–0.991) in internal validation, comparable to pathology-only (0. 971) and joint models (0.980), and superior to US-only (0.861). Performance remained robust across both internal (AUROC range: 0.930–0.942) and all external cohorts (AUROC range: 0.915–0.963). US-GPS enables scalable, non-invasive, and interpretable subtype classification, with potential for both pre-treatment decision-making and longitudinal monitoring of molecular phenotypic shifts.

Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical Diagnosis Model (PTs-HDM) for preoperative identification and grading. Ultrasound images from five hospitals were retrospectively collected, with all patients having undergone surgical pathological confirmation of either PTs or fibroadenomas (FAs). PTs-HDM follows a two-stage classification: first distinguishing PTs from FAs, then grading PTs into benign or borderline/malignant. Model performance metrics including AUC and accuracy were quantitatively evaluated. A comparative analysis was conducted between the algorithm’s diagnostic capabilities and those of radiologists with varying clinical experience within an external validation cohort. Through the provision of PTs-HDM’s automated classification outputs and associated thermal activation mapping guidance, we systematically assessed the enhancement in radiologists’ diagnostic concordance and classification accuracy. A total of 712 patients were included. On the external test set, PTs-HDM achieved an AUC of 0.883, accuracy of 87.3 • Accurate preoperative diagnosis and grading of Phyllodes tumors remain challenging. • An ultrasound-based deep learning model enables preoperative hierarchical diagnosis of Phyllodes tumors. • PTs-HDM improves radiologists’ accuracy and consistency, reducing diagnostic variability.

Background: We developed a machine learning-based predictive model for diagnosing prostate cancer within the gray zone of prostate-specific antigen (PSA) levels, leveraging transrectal prostate ultrasound video clips. Methods: Data were collected for patients with suspected prostate cancer, characterized by intermediate PSA levels between 4 and 10 ng/mL, who visited the Department of Urology, Dongyang People's Hospital, which is affiliated with Wenzhou Medical University, from 20 August 2021 to 30 September 2023. Among the final selection of 508 patients, a total of 851 features were extracted from the ultrasound video clips, reduced the dimensionality using least absolute shrinkage and selection operator regression, and finally selected 25 features. The selected features were employed to construct radiomics models based on four machine learning algorithms support vector machine (SVM), random forest (RF), adaptive boosting (ADB) and gradient boosting machine (GBM). The performance of the model was comprehensively assessed using receiver operating characteristic (ROC) curve analysis, with diagnostic effectiveness measured through metrics such as the area under the curve (AUC), sensitivity, specificity and overall accuracy. Results: The RF model demonstrated an AUC of 0.89, accuracy of 0.81, sensitivity of 0.81, specificity of 0.79, positive predictive value of 0.91 and F1 score of 0.77. As compared to the RF model, the SVM, ADB and GBM models showed similar values for AUC (range 0.80-0.86), accuracy (range 0.75-0.79), sensitivity (range 0.80-0.81), specificity (range 0.65-0.75), positive predictive value (range 0.83-0.89) and F1 score (range 0.72-0.76). In the validation set, following comprehensive evaluation, the RF model exhibited the best performance among the four models. Conclusions: The four machine learning models each had diagnostic value for detecting prostate cancer in patients within the PSA "gray zone", with the RF model demonstrating the highest predictive performance.

Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI models lack transparency and interpretability. We developed a multimodal generative pre-trained transformer for thyroid nodules (ThyGPT), aiming to provide a transparent and interpretable AI copilot model for thyroid nodule risk assessment and management. Ultrasound data from 59,406 patients across nine hospitals were retrospectively collected to train and test the model. After training, ThyGPT was found to assist in reducing biopsy rates by more than 40% without increasing missed diagnoses. In addition, it detects errors in ultrasound reports 1,610 times faster than humans. With the assistance of ThyGPT, the area under the curve for radiologists in assessing thyroid nodule risks improved from 0.805 to 0.908 (p < 0.001). As an AI-generated content-enhanced computer-aided diagnosis (AIGC-CAD) model, ThyGPT has the potential to revolutionize how radiologists use such tools.

Background:Globally, lung cancer is the most frequently diagnosed malignancy, for which solid pulmonary nodules (SPNs) are a common radiographic finding. Given the high false-positive rates of computed tomography (CT) screening, we aimed to develop a multimodal diagnostic model combining CT radiomics features and serum biomarkers via machine learning. Methods:This retrospective study included patients receiving both preoperative CT screening and serum biomarker testing. All pulmonary nodules (PNs) were divided into training and validation sets randomly at a ratio of 7:3. We developed a multimodal diagnosis model based on the CT radiomics and protein biomarkers of SPNs in the training cohort. The CT radiomics features were derived from the integration of traditional radiomics analysis methods and three-dimensional (3D) deep learning techniques. The accuracy of this multimodal diagnosis model for the prediction of SPNs was verified in the validation set. Model performances were evaluated in terms of the area under the curve (AUC), accuracy, positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and calibration curve. Results:Between February 2016 and December 2020, imaging data of 638 eligible PNs from CT scans of 633 different patients were collected. The multimodal model had satisfactory accuracy in differentiating benign and malignant SPNs in the training set [AUC =0.944; 95% confidence interval (CI): 0.924-0.964]. In the validation set, the multimodal model yielded an AUC of 0.926 (95% CI: 0.889-0.964), an accuracy of 0.885, an NPV of 0.812, and a PPV of 0.927. The multimodal model also significantly outperformed the single-modality diagnostic models, including the traditional radiomics CT model (AUC =0.843; 95% CI: 0.780-0.906), the serum biomarker model (AUC =0.783; 95% CI: 0.718-0.847), and the 3D deep learning model (AUC =0.820; 95% CI: 0.754-0.885) (all P values <0.01). Conclusions:This study developed a novel multimodal that demonstrated superior performance in classifying SPNs. It may thus enhance the diagnosis of benign and malignant lesions and provide support for clinical decision-making.

This retrospective study evaluated the efficacy of large language models (LLMs) in improving the accuracy of Chinese ultrasound reports. Data from three hospitals (January-April 2024) including 400 reports with 243 errors across six categories were analyzed. Three GPT versions and Claude 3.5 Sonnet were tested in zero-shot settings, with the top two models further assessed in few-shot scenarios. Six radiologists of varying experience levels performed error detection on a randomly selected test set. In zero-shot setting, Claude 3.5 Sonnet and GPT-4o achieved the highest error detection rates (52.3% and 41.2%, respectively). In few-shot, Claude 3.5 Sonnet outperformed senior and resident radiologists, while GPT-4o excelled in spelling error detection. LLMs processed reports faster than the quickest radiologist (Claude 3.5 Sonnet: 13.2 s, GPT-4o: 15.0 s, radiologist: 42.0 s per report). This study demonstrates the potential of LLMs to enhance ultrasound report accuracy, outperforming human experts in certain aspects.

Objective:This study aimed to evaluate the effectiveness of deep-learning models using transrectal ultrasound (TRUS) video clips in predicting prostate cancer. Methods:We manually segmented TRUS video clips from consecutive men who underwent examination with EsaoteMyLab™ Class C ultrasonic diagnostic machines between January 2021 and October 2022. The deep learning-inflated 3D ConvNet (I3D) model was internally validated using split-sample validation on the development set through cross-validation. The final performance was evaluated on two external test sets using geographic validation. We compared the results obtained from a ResNet 50 model, four ML models, and the diagnosis provided by five senior sonologists. Results:A total of 815 men (median age: 71 years; IQR: 67-77 years) were included. The development set comprised 552 men (median age: 71 years; IQR: 67-77 years), the internal test set included 93 men (median age: 71 years; IQR: 67-77 years), external test set 1 consisted of 96 men (median age: 70 years; IQR: 65-77 years), and external test set 2 had 74 men (median age: 72 years; IQR: 68-78 years). The I3D model achieved diagnostic classification AUCs greater than 0.86 in the internal test set as well as in the independent external test sets 1 and 2. Moreover, it demonstrated greater consistency in sensitivity, specificity, and accuracy compared to pathological diagnosis (kappa > 0.62, p < 0.05). It exhibited a statistically significant superior ability to classify and predict prostate cancer when compared to other AI models, and the diagnoses provided by sonologists (p<0.05). Conclusion:The I3D model, utilizing TRUS prostate video clips, proved to be valuable for classifying and predicting prostate cancer.

IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, characterized by immune complex deposition in the glomerular mesangium, leading to mesangial hypercellularity, persistent microhematuria, proteinuria, and progressive renal impairment. Given its common occurrence, diagnosis normally involves renal biopsy, with its accompanying risks of bleeding and infection. In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. This retrospective study included 292 patients with IgAN and 310 individuals with different nephropathies, utilizing 82 clinical variables, with kidney pathology results serving as ML labels. A random forest (RF) regression model addressed missing values. Subjects were divided into a development set (n = 542) and a test set (n = 60). The RF method was applied to select 17 key features for building diagnostic models, including the RF model, support vector machine (SVM), adaptive boosting (ADB), and traditional doctor judgment. Performance was evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The random forest model performed best with an accuracy of 82.3

In the present study, zinc oxide nanoparticles (ZnO NPs) were synthesized via the electrodeposition method and characterized. Then, the anticancer effects of ZnO NPs against cervical cancer HeLa cells were assessed by cell viability, oxidative stress, caspase activity, qRT-PCR, MMP, and ELISA assays. XRD analysis revealed the hexagonal wurtzite phase of ZnO. SEM image of ZnO NPs showed the homogeneous spherical/hexagonal-like structures of ZnO NPs, while TEM imaging revealed the successful synthesis of ZnO NPs with an average diameter of about 8 nm. The UV–vis absorption spectrum of ZnO NPs showed a characteristic absorption band around the wavelength of 368 nm with a band gap energy (Eg) of 3.36 eV. DLS study displayed that the obtained particle size of ZnO NPs has an average size of 29.24 nm and an average zeta potential value of −19 mV. Additionally, cellular findings indicated that the proliferation of cervical cancer HeLa cells was markedly mitigated after incubation with ZnO NPs at different concentrations of 10, 50 and 100 µg/ml, however, these concentrations were not able to trigger an apparent cytotoxic effect on HUVEC non-malignant cells. Also, it was detected that ZnO NPs led to overexpression of Bax/ Bcl-2, caspase-9/-3 genes, increased level of caspase-9/-3 activity, overproduction of MDA level, inhibition of SOD and CAT activity and reduction of GSH content, MMP collapse, and upregulation of cytoplasmic cytochrome c release. In general, these findings suggested that electrodeposited synthesized ZnO NPs can induce anticancer effects in cervical cancer HeLa cells through the mitochondrial-mediated apoptosis signaling pathway.

Centrosomal protein of 55 kDa (CEP55) overexpression has been linked to tumor stage, aggressiveness of the tumor, poor prognosis, and metastasis. This study aims to elucidate the action of CEP55 in ovarian cancer (OC) and the regulation by the alpha-ketoglutarate-dependent dioxygenase alkB homolog 5 (ALKBH5)/Forkhead box protein P2 (FOXP2) axis. Differentially expressed genes in OC were identified using in silico identification, followed by prognostic value assessment. Lentiviral vectors were constructed to downregulate CEP55 in OC cells, and colony formation, EdU, TUNEL, flow cytometry, Transwell assays, and Phalloidin staining were conducted. Transcription factors regulating CEP55 were predicted and verified, and rescue experiments were performed. The effect of ALKBH5-mediated demethylation on FOXP2 mRNA stability and OC cell cycle and EMT were analyzed. High expression of CEP55 in OC was linked to unsatisfactory prognosis of patients. Knockdown of CEP55 repressed proliferation, invasiveness, and epithelial-mesenchymal transition (EMT) while inducing apoptosis and cell cycle arrest in OC cells. FOXP2 bound to the promoter of CEP55 to repress CEP55 transcription. FOXP2 regulated transcriptional repression of CEP55 to impede the malignant progression of OC and inhibit tumor metastasis. ALKBH5-mediated demethylation modification induced mRNA degradation of FOXP2. Knockdown of ALKBH5 induced cell cycle arrest and inhibited EMT in OC cells. ALKBH5 hinders FOXP2-mediated transcriptional repression of CEP55 to promote the malignant progression of OC via cell cycle and EMT.

Objective:To develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm. Methods:A dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients). Results:TNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal test set and 0.941 (95 % CI: 0.921, 0.957) on the external test set, significantly outperforming traditional deep convolutional neural network models and single-channel swin transformer model, whose AUCs ranged from 0.800 (95 % CI: 0.759, 0.837) to 0.856 (95 % CI: 0.819, 0.881). Furthermore, feature heatmap visualization showed that TNT-Net could extract richer and more energetic malignant nodule patterns. Conclusion:The proposed TNT-Net model significantly improved the recognition capability for thyroid nodules with size less than 1 cm. This model has the potential to reduce overdiagnosis and overtreatment of such nodules, providing essential support for precise management of thyroid nodules while complementing fine-needle aspiration biopsy.

Background To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis.Methods A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted.Results The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values.Conclusions The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

An artificial intelligence-generated content-enhanced computer-aided diagnosis (AIGC-CAD) model, designated as ThyGPT, has been developed. This model, inspired by the architecture of ChatGPT, could assist radiologists in assessing the risk of thyroid nodules through semantic-level human-machine interaction. A dataset comprising 19,165 thyroid nodule ultrasound cases from Zhejiang Cancer Hospital was assembled to facilitate the training and validation of the model. After training, ThyGPT could automatically evaluate thyroid nodule and engage in effective communication with physicians through human-computer interaction. The performance of ThyGPT was rigorously quantified using established metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, and specificity. The empirical findings revealed that radiologists, when supplemented with ThyGPT, markedly surpassed the diagnostic acumen of their peers utilizing traditional methods as well as the performance of the model in isolation. These findings suggest that AIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally transform the diagnostic workflows of radiologists in forthcoming years.

Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined. This imprecise diagnosis creates difficulties in selecting the follow-up treatment. In this retrospective study, we collected ultrasound (US) image data of Bethesda IV thyroid nodules from 2006 to 2022 from five hospitals. Then, US image-based artificial intelligence (AI) models were trained to identify the specific category of Bethesda IV thyroid nodules. We tested the models using two independent datasets, and the best AI model achieved an area under the curve (AUC) between 0.90 and 0.95, demonstrating its potential value for clinical application. Our research findings indicate that AI could change the diagnosis and management process of Bethesda IV thyroid nodules.

Ovarian cancer (OC) is one of the most common gynecological malignancies with high morbidity and mortality. The peritoneum is one of the most common metastatic sites in ovarian cancer, involving large amounts of ascites. However, its mechanism is unclear. The peritoneal microenvironment composed of peritoneal effusion and peritoneum creates favorable conditions for ovarian cancer progression and metastasis. Here, we reviewed the peritoneal metastasis patterns and molecular mechanisms of ovarian cancer, as well as major components of the peritoneal microenvironment, peritoneal effusion, and immune microenvironment, and investigated the relationship between the peritoneal microenvironment and ovarian cancer metastasis.

Objective To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI). Methods We systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis. Results The AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant ( p < 0.05). Conclusion The prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

Background:Epithelial ovarian cancer (EOC) has the lowest survival rate among female reproductive cancers present with symptoms of aggressive malignancies, poor prognosis, drug resistance and postoperative recurrence. The majority of patients with EOC are diagnosed at an advanced stage due to the therapeutic challenges including lack of early diagnosis and effective therapeutic targets for EOC. Methods:Pan-cancer analyses were performed to explore the features of forkhead-box (FOX) A1 (FOXA1) using data from TCGA and GTEx databases. R package "clusterprofiler" was used to perform the enrichment analysis of FOXA1 in EOC. Data downloaded from Drug Sensitivity in Cancer (GDSC) database were used to evaluate the association between FOXA1 and antitumor drug sensitivity. In experimental verification, FOXA1 expression was detected using qRT-PCR and western blot assays. Western blot, immunofluorescence staining, and Transwell assays were used to assess the influence of FOXA1 silencing on epithelial-mesenchymal transition (EMT) of EOC cells. Results:We found that FOXA1 was highly expressed in EOC and predicted poorer survival of EOC patients. We observed that FOXA1 expression was positively correlated EMT-related pathways. Through experimental verification, we found the underlying function of FOXA1 to promote EMT in ovarian cancers. The results from western blot, immunofluorescence staining, and Transwell assays showed that FOXA1 silencing impeded the progression of EMT and invasiveness of the cancer cells. Furthermore, CCK-8 and invasion assays suggested that siRNA-FOXA1 attenuated the ability of cancer cells to metastasize and proliferate. Dual-luciferase reporter assays confirmed the binding activity of FOXA1 to the promoter of connective tissue growth factor (CTGF). In addition, we found that FOXA1 was closely correlated immunosuppressive microenvironment of EOC. High FOXA1 expression may contribute to the resistance of many anticancer drugs. Conclusions:Our results predict and validate the function of FOXA1 in promoting EMT and the progression of disease in EOC. Targeting FOXA1 may improve the sensitivity of EOC treatment.

Fc fragment of IgG-binding protein (FCGBP) is differentially expressed in various tumors. However, the correlation between FCGBP and immune cell infiltration in ovarian cancer remains unclear. FCGBP expression was analyzed using The Cancer Genome Atlas (TCGA) pan-cancer data, and the ovarian cancer expression profile was analyzed using the Gene Expression Omnibus database. The clinical prognostic value of FCGBP was evaluated using clinical survival data from TCGA. Enrichment analysis of FCGBP was performed using the R package clusterProfiler. Based on known immune cell infiltration scores for samples found in TCGA, we analyzed the association between immune cell infiltration level and FCGBP expression. FCGBP was highly expressed and associated with poorer overall survival (p = 0.00051) and disease-specific survival (p = 0.0012) in ovarian cancer and other tumors. Additionally, high FCGBP expression correlated significantly with immune-related gene sets, including those involved in chemokine signaling pathways and innate and adaptive immunity. Further analysis showed that M2 macrophage infiltration increased and M1 macrophage infiltration decreased in tissues with high FCGBP expression. Our study suggests that FCGBP contributes to M2 macrophage polarization by acting as an oncogene in ovarian cancer. FCGBP may represent a clinically helpful biomarker for predicting overall survival of ovarian cancer patients.

Background: Transient receptor potential cation channel subfamily V member 4 (TRPV4) has been reported to regulate tumor progression in many tumor types. However, its association with the tumor immune microenvironment remains unclear. Methods: TRPV4 expression was assessed using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The clinical features and prognostic roles of TRPV4 were assessed using TCGA cohort. Gene set enrichment analysis (GSEA) of TRPV4 was conducted using the R package clusterProfiler. We analyzed the association between TRPV4 and immune cell infiltration scores of TCGA samples downloaded from published articles and the TIMER2 database. The IC50 values of 192 anti-cancer drugs were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database and the correlation analysis was performed. Results: TRPV4 was highly expressed and associated with worse overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in colon adenocarcinoma (COAD) and ovarian cancer. Furthermore, TRPV4 expression was closely associated with immune regulation-related pathways. Moreover, tumor-associated macrophage (TAM) infiltration levels were positively correlated with TRPV4 expression in TCGA pan-cancer samples. Immunosuppressive genes such as PD-L1, PD-1, CTLA4, LAG3, TIGIT, TGFB1, and TGFBR1 were positively correlated with TRPV4 expression in most tumors. In addition, patients with high expression of TRPV4 might be resistant to the treatment of Cisplatin and Oxaliplatin. Conclusion: Our results suggest that TRPV4 is an oncogene and a prognostic marker in COAD and ovarian cancer. High TRPV4 expression is associated with tumor immunosuppressive status and may contribute to TAM infiltration based on TCGA data from pan-cancer samples. Patients with high expression of TRPV4 might be resistant to the treatment of Cisplatin and Oxaliplatin.