BackgroundHepatocellular carcinoma (HCC) is a highly aggressive primary liver malignancy characterized by limited therapeutic options and poor prognosis. Within the tumor microenvironment (TME), tumor-associated macrophages (TAMs) predominantly exhibit an M2-like phenotype, contributing to immune escape and tumor progression. Zymogen granule protein 16 (ZG16) has been reported to be downregulated in HCC, but its precise biological function and molecular mechanisms remain poorly understood. Therefore, we aimed to investigate the impact of ZG16 on HCC cell metastasis and TAM infiltration, as well as to elucidate its molecular mechanism.MethodsGain- and loss-of-function assays were used to verify the effect of ZG16 on HCC cell metastasis, as well as the recruitment and M2 polarization of TAMs. The underlying mechanism of ZG16 was explored by immunoprecipitation-liquid chromatography-mass spectrometry (IP-LC/MS) analysis, co-immunoprecipitation (co-IP) assay, and GST pull-down assay.ResultsOur results demonstrated that ZG16 overexpression significantly inhibited metastasis of HCC cells while also suppressing the recruitment and M2 polarization of TAMs, suggesting its dual role in both tumor cell-intrinsic and microenvironmental regulation. Notably, sorting nexin 9 (SNX9), a facilitator of HCC, was identified as a downstream target of ZG16. Mechanistically, we uncovered that ZG16 physically interacted with SNX9 and promoted its protein degradation through the ubiquitin-proteasome pathway. Functional rescue experiments provided compelling evidence that SNX9 overexpression effectively counteracted ZG16-mediated suppression of both HCC progression and TAM M2 polarization. Further mechanism exploration confirmed that ZG16 promoted the ubiquitination and degradation of SNX9 by recruiting itchy E3 ubiquitin protein ligase (ITCH).ConclusionsOur findings certify that ZG16 suppresses tumor progression and M2 polarization of TAMs in HCC through ITCH-mediated ubiquitination and subsequent degradation of SNX9. The ZG16/ITCH/SNX9 axis may represent an important regulatory pathway and potential therapeutic target for HCC.

The pathological diagnosis of gestational trophoblastic disease(GTD) takes a long time, relies heavily on the experience of pathologists, and the consistency of initial diagnosis is low, which seriously threatens maternal health and reproductive outcomes. We developed an expert model for GTD pathological diagnosis, named GTDoctor. GTDoctor can perform pixel-based lesion segmentation on pathological slides, and output diagnostic conclusions and personalized pathological analysis results. We developed a software system, GTDiagnosis, based on this technology and conducted clinical trials. The retrospective results demonstrated that GTDiagnosis achieved a mean precision of over 0.91 for lesion detection in pathological slides (n=679 slides). In prospective studies, pathologists using GTDiagnosis attained a Positive Predictive Value of 95.59

Immunotherapy has transformed melanoma treatment, yet only a subset of patients benefit and clinically effective biomarkers remain limited. Here, we report a comprehensive analysis evaluating CD27 as a predictive and prognostic biomarker in melanoma across public datasets and a clinical cohort. Public transcriptomic datasets revealed that high CD27 mRNA expression correlates with immune checkpoint genes, enhanced immune infiltration, and favorable prognosis. In advanced melanoma patients treated with immune checkpoint inhibitors, CD27 demonstrated strong predictive performance based on receiver operating characteristic (ROC) curve analysis, with AUC values of 0.763 (PRJEB23709, n = 91) and 0.659 (GSE91061, n = 51). These findings were validated in a retrospective melanoma cohort (n = 102) from the First Affiliated Hospital of Zhengzhou University. CD27 mRNA levels measured by qRT-PCR achieved an AUC of 0.688, and protein levels assessed by immunohistochemistry yielded an AUC of 0.656, both surpassing PD-L1 (AUC = 0.460). Notably, CD27 IHC showed 69.8% sensitivity compared to 27.9% for PD-L1 in identifying responders. Patients with CD27-positive tumors exhibited significantly longer progression-free survival. Multiplex immunofluorescence confirmed CD27 expression in CD45RO⁺ memory T cells, highlighting its role in mediating anti-tumor immunity. These results establish CD27 as a clinically actionable biomarker with superior predictive value over PD-L1 for melanoma immunotherapy response.

The significant heterogeneity of multiple myeloma (MM) profoundly impacts patient prognosis, with cytogenetic abnormalities serving as a core driver. Based on the International Myeloma Working Group and 2024 International Myeloma Society consensus, this study enrolled 52 newly diagnosed MM patients. Six cytogenetic abnormalities-RB1 deletion, D13S319 deletion, immunoglobulin heavy chain (IGH) translocation, 1q21 gain/amplification, P53 deletion, and 1p32 deletion-were detected using fluorescence in situ hybridisation. Integrating clinical data and immunohistochemical analysis, we investigated the clinical associations and prognostic significance of these abnormalities. Results demonstrated that the positive detection rate of cytogenetic abnormalities ranged from 11.5% to 46.2% and was significantly associated with advanced tumour stage [International Staging System, revised International Staging System (R-ISS), and R2-ISS]. Patients with cytogenetic abnormalities frequently exhibited adverse haematological parameters, including low haemoglobin and elevated β2-microglobulin and were associated with aggressive tumour biological behaviours such as enhanced anti-apoptotic capacity, high proliferation index, and increased microvessel density. Survival analysis confirmed that all abnormalities except IGH translocation significantly shortened both overall survival (OS) and progression-free survival (PFS), with P53 deletion identified as an independent risk factor for OS. Among patients receiving bortezomib, lenalidomide, and dexamethasone (VRd) induction therapy, those with cytogenetic abnormalities showed significantly lower deep response rates (complete response + very good partial response). According to the latest 'multiple-hit' definition, patients classified as 'triple hit' had significantly shorter OS and reduced response rates compared to those receiving VRd therapy. Furthermore, RB1 deletion or D13S319 deletion combined with other high-risk indicators further shortened PFS. These findings indicate that integrating expanded cytogenetic markers optimises MM risk stratification and provides a basis for individualised treatment strategies.

Multiple myeloma (MM) is a highly heterogeneous plasma cell malignancy whose progression and relapse are influenced by clonal heterogeneity. Although serum free light chains (sFLCs) are key biomarkers for tumour burden assessment, the clinical value of λ, κ and their ratio remains controversial. This study investigated the associations of sFLC κ, λ and the κ/λ ratio with clinical features, cytogenetic abnormalities, prognosis and response to the VRd regimen (bortezomib, lenalidomide, dexamethasone) in newly diagnosed MM (NDMM) patients, aiming to optimize risk stratification. A retrospective analysis of 113 NDMM patients was conducted. Baseline clinical data, cytogenetic markers (RB1/D13S319/IGH/1q21/P53/1p32) and treatment responses were collected, with stratification by renal function. Tumour proliferation, apoptosis and angiogenesis were assessed by immunohistochemistry. Prognostic factors were evaluated using Kaplan-Meier and Cox regression analyses. A modified revised International Staging System (R-ISS) (MR-ISS) model incorporating sFLC κ was then developed and temporally externally validated. Abnormal sFLC κ indicated poor prognosis. Among NDMM patients with normal renal function, abnormal sFLC κ was significantly associated with adverse clinical features, high tumour burden, an anti-apoptotic and angiogenic tumour microenvironment, D13S319 deletion and a lower rate of deep remission after VRd induction. Moreover, the MR-ISS model demonstrated superior risk discrimination compared with the R-ISS. In conclusion, abnormal sFLC κ is a potential marker of poor prognosis and suboptimal response to VRd therapy in NDMM. Integrating sFLC κ with the R-ISS improves risk stratification, providing new evidence for clinical application of this biomarker.

Angiogenesis is essential for esophageal squamous cell carcinoma (ESCC) progression, yet clinically actionable regulators remain limited. We investigated the expression, functional role, and mechanism of CCM3 in ESCC and its relationship with the HIF-1α/VEGFA angiogenic axis. CCM3, HIF-1α, VEGFA and microvessel density (MVD, CD31) were assessed by immunohistochemistry in 53 paired ESCC and adjacent non-cancerous tissues. CCM3 knockdown was optimized using four siRNAs in KYSE-70 and KYSE270 cells. CCM3 siRNA 1064-A was selected as the most effective and optimal knockdown achieved at 8 nM, 48 h. Tumor-conditioned HUVECs (referred to as tumor-conditioned endothelial cells, TECs) were generated using conditioned media from knockdown cells; TEC proliferation (CCK8), migration (scratch), invasion (Transwell) and tube formation were evaluated in vitro. A subcutaneous xenograft model using KYSE-70 cells was employed in vivo, followed by intratumoral silencing of CCM3 to assess tumour development, proliferation (Ki-67), and microvessel density (CD31). High endogenous CCM3 expression was significantly upregulated in ESCC tissues compared to adjacent non-cancerous tissues and correlated with clinicopathological features like depth of invasion, lymph node metastasis and advanced stage. CCM3 expression positively correlated with HIF-1α and VEGFA. CCM3 knockdown siRNA 1064-A attenuates TEC proliferation, migration, invasion and tube formation in vitro. In vivo, CCM3 knockdown reduced tumor growth, tumor weight, Ki-67, CD31 staining and levels of HIF-1α and VEGFA. High endogenous CCM3 expression associates with aggressive ESCC clinicopathological features and angiogenesis markers. CCM3 knockdown inhibits tumor-conditioned endothelial function, xenograft growth, intratumoral vascularization, and HIF-1α/VEGFA expression, warranting further mechanistic investigation.

Endometrial mesonephric-like adenocarcinoma (MLA) is a rare subtype of uterine corpus endometrial carcinoma (UCEC) first described in 2016. The clinicopathological features, treatment options, and prognosis of endometrial MLA remain poorly understood. In this study, we retrospectively analyzed the clinicopathological characteristics, molecular features, treatment regimens, and outcomes of 11 patients diagnosed with endometrial MLA. The most prevalent symptom observed was postmenopausal bleeding. Notably, 78% (7 out of 9) of patients were diagnosed at advanced FIGO stages (II-IV), with four cases presenting with distant metastasis upon initial examination. Multivisceral metastases were identified in three cases, with lung metastases being the most common, occurring in 45% of patients. The median progression-free survival (PFS) was 16 months (95% confidence intervals: 6-26). All tumors tested negative for progesterone receptors (PR), while 91% of patients (10 out of 11) were negative for estrogen receptors (ER). Most patients exhibited positive immunohistochemical staining for "mesonephric-like" markers, including GATA-binding protein 3 (GATA-3), thyroid transcription factor-1 (TTF-1), and CD10. Furthermore, 91% of patients showed a wild-type p53 immunostaining pattern. Among the 11 patients, five underwent KRAS mutation testing, revealing KRAS mutations in all tested individuals (p.G12D in 2/5, p.G12A in 1/5, p.G12V in 1/5, and p.G13D in 1/5). These findings indicate that 78% of endometrial MLA patients were diagnosed at an advanced stage and suggest that this subtype may exhibit more aggressive behavior compared to endometrial endometrioid carcinoma. The consistent presence of KRAS mutations in patients who underwent testing highlights the potential role of KRAS in the initiation and progression of endometrial MLA, positioning it as a promising therapeutic target.

Objectives To investigate preoperative dual-energy CT (DECT)-derived independent risk factors affecting progression-free survival (PFS) in patients with locally advanced gastric cancer (LAGC) undergoing gastrectomy, and to reveal the underlying histopathologic changes. Methods This prospective study included patients who underwent preoperative DECT scan and gastrectomy. Clinical data, DECT-derived morphological characteristics and iodine-related parameters were comprehensively collected. Univariate and multivariate analyses were carried out to identify independent risk factors associated with PFS. The prognostic performance of various parameters was evaluated using the bootstrap-based consistency index (C-index) and time-dependent receiver operating characteristic (ROC) analysis. Kaplan-Meier curves were used to assess the differences in survival analysis. The histopathologic underpinnings of the DECT-based combined parameter for evaluating PFS were explored. Results 120 LAGC patients (63.3 ± 10.9 years; 94 men) were analyzed. Age, arterial enhancement fraction (AEF), serosal invasion, and tumor thickness were identified as preoperative independent risk factors affecting PFS (all p < 0.05). The combined parameters based on these risk factors achieved a C-index of 0.75, significantly or slightly superior to that of any single risk factor (all p < 0.05) or postoperative pathological staging (C-index, 0.67; p > 0.05). For predicting the 0.5-, 1- and 2-year PFS, the combined parameter had an area-under-the-curve (AUC) of 0.72, 0.77, and 0.74, respectively. PFS significantly differed between patients of high- and low-risks assessed with the combined parameter (p < 0.001). Histopathologically, the combined parameter was associated with tumor microvessel density (r = 0.31, p < 0.001). Conclusion The combination of DECT-derived morphological characteristics, iodine-related parameters, and clinical data helped accurately stratify PFS in LAGC before surgery and is associated with tumor angiogenesis. Clinical relevance statement Dual-energy CT was promising in the preoperative evaluation of the progression-free survival in LAGC patients after gastrectomy.

PurposeMicrosatellite instability (MSI) plays a crucial role in determining the therapeutic outcomes of gastroesophageal junction (GEJ) adenocarcinoma. This study aimed to develop a deep learning model based on H&E-stained pathological specimens to accurately identify MSI-H in GEJ adenocarcinomas patients.MethodsA total of 416 H&E-stained slides of 212 GEJ adenocarcinoma patients were collected to establish an artificial intelligence (AI) model using digital pathology (DP) for of MSI-H prediction. Simple Vit and ResNet18 Neural networks were trained and tested on models developed from patch-level images. A whole-slide image (WSI)-level AI model was constructed by integrating deep learning- generated pathological features with six machine learning algorithms.ResultsThe MLP model showed demonstrated the highest performance in predicting MSI-H in the test cohort, achieving an AUC of 93.3%, a sensitivity of 0.841, and a specificity of 0.952. Similarly, Decision Curve Analysis (DCA) revealed that WSI-level H&E-stained slides offered significant clinical MSI-H prediction in GEJ adenocarcinoma patients.ConclusionThe AI model based on digital pathology exhibits great potential for predicting MSI-H in GEJ adenocarcinoma, suggesting promising clinical applications.

Esophageal squamous cell carcinoma (ESCC) is a lethal malignancy with limited therapeutic options, primarily due to its aggressive metastatic behavior. This study aimed to elucidate the molecular drivers of ESCC metastasis by identifying a critical oncogene and investigate its functional mechanisms. Using integrated bioinformatics screening, we identified NDC80 as a potential regulator of metastasis. The role and underlying mechanisms of NDC80 in ESCC progression have remained to be fully elucidated. We conducted in vitro functional assays—including real-time PCR, western blotting, and flow cytometry—to assess the effects of NDC80 on epithelial-mesenchymal transition (EMT) and malignant behavior. Mechanistic studies such as co-culture systems and ELISA, were used to evaluate tumor-associated macrophages (TAMs) polarization, with a specific focus on the PI3K/AKT pathway. In vivo, we established subcutaneous xenograft models in immunocompromised mice to validate the impact of NDC80 on ESCC progression. Clinical validation was performed using immunohistochemistry analysis of ESCC tissue samples. NDC80 was identified as a clinically significant oncogene, showing marked overexpression in ESCC tissues that was associated with advanced invasion depth, lymphatic metastasis, and vascular invasion. Functionally, NDC80 promoted tumor cell migration, invasion, and EMT progression. Mechanistically, NDC80 fostered a tumor-promoting microenvironment by inducing M2 macrophages polarization via secretion of M-CSF and CXCL-2, which in turn activated the PI3K/AKT pathway to further amplify EMT. Our findings established NDC80 as a master regulator of ESCC metastasis by activating TAM-mediated PI3K/AKT and promoting EMT. These insights positioned NDC80 as a promising therapeutic target, offering a potential strategy to prevent ESCC progression.

Infection is the leading cause of morbidity and mortality in patients with multiple myeloma (MM). Studying the relationship between different traits of Coronavirus 2019 (COVID-19) and MM is critical for the management and treatment of MM patients with COVID-19. But all the studies on the relationship so far were observational and the results were also contradictory. Using the latest publicly available COVID-19 genome-wide association studies (GWAS) data, we performed a bidirectional Mendelian randomization (MR) analysis of the causality between MM and different traits of COVID-19 (SARS-CoV-2 infection, COVID-19 hospitalization, and severe COVID-19) and use multi-trait analysis of GWAS(MTAG) to identify new associated SNPs in MM. We performed co-localization analysis to reveal potential causal pathways between diseases and over-representation enrichment analysis to find involved biological pathways. IVW results showed SARS-CoV-2 infection and COVID-19 hospitalization increased risk of MM. In the reverse analysis, the causal relationship was not found between MM for each of the different symptoms of COVID-19. Co-localization analysis identified LZTFL1, MUC4, OAS1, HLA-C, SLC22A31, FDX2, and MAPT as genes involved in COVID-19-mediated causation of MM. These genes were mainly related to immune function, glycosylation modifications and virus defense. Three novel MM-related SNPs were found through MTAG, which may regulate the expression of B3GNT6. This is the first study to use MR to explore the causality between different traits of COVID-19 and MM. The results of our two-way MR analysis found that SARS-CoV-2 infection and COVID-19 hospitalization increased the susceptibility of MM.

BACKGROUND:Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy. Since the advent of whole slide images (WSIs), the transition from traditional histopathology to digital pathology has aroused the application of convolutional neural networks (CNNs) in histopathological recognition and diagnosis. HookNet can make full use of macroscopic and microscopic information for pathological diagnosis, but it cannot integrate other excellent CNN structures. The new version of HookEfficientNet is based on a combination of HookNet structure and EfficientNet that performs well in the recognition of general objects. Here, a high-precision artificial intelligence-guided histopathological recognition system was established by HookEfficientNet to provide a basis for the intelligent differential diagnosis of NSCLC. METHODS:A total of 216 WSIs of lung adenocarcinoma (LUAD) and 192 WSIs of lung squamous cell carcinoma (LUSC) were recruited from the First Affiliated Hospital of Zhengzhou University. Deep learning methods based on HookEfficientNet, HookNet and EfficientNet B4-B6 were developed and compared with each other using area under the curve (AUC) and the Youden index. Temperature scaling was used to calibrate the heatmap and highlight the cancer region of interest. Four pathologists of different levels blindly reviewed 108 WSIs of LUAD and LUSC, and the diagnostic results were compared with the various deep learning models. RESULTS:The HookEfficientNet model outperformed HookNet and EfficientNet B4-B6. After temperature scaling, the HookEfficientNet model achieved AUCs of 0.973, 0.980, and 0.989 and Youden index values of 0.863, 0.899, and 0.922 for LUAD, LUSC and normal lung tissue, respectively, in the testing set. The accuracy of the model was better than the average accuracy from experienced pathologists, and the model was superior to pathologists in the diagnosis of LUSC. CONCLUSIONS:HookEfficientNet can effectively recognize LUAD and LUSC with performance superior to that of senior pathologists, especially for LUSC. The model has great potential to facilitate the application of deep learning-assisted histopathological diagnosis for LUAD and LUSC in the future.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This article has been retracted at the request of the editors. Following a request from the authors, the editors of eBioMedicine are retracting the above article. A Corrigendum for the paper was originally issued on May 2020. However, the editors became aware of several potential figure panel duplications between Figure 7c of the paper and Figure 7a of an unrelated manuscript published by Zheng and colleagues, entitled "MicroRNA-433 inhibits oral squamous cell carcinoma cells by targeting FAK" Oncotarget. 2017; 8:100227–100241. https://doi.org/10.18632/oncotarget.22151. Given the nature and extent of the image duplications found, the editors of eBioMedicine no longer have confidence in the integrity of the data and retract both the paper and related Corrigendum. RETRACTED: MicroRNA-134 prevents the progression of esophageal squamous cell carcinoma via the PLXNA1-mediated MAPK signalling pathwayThis article has been retracted: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/locate/withdrawalpolicy ). Full-Text PDF Open Access

In order to improve the cavitation phenomenon of deflector jet servo valve in the front stage, based on the principle of orthogonal experiment, a SIMULINK simulation model was established for the structural parameters of deflector jet servo valve, such as the thickness of jet disk, the length of guide tube, the height of wedge and the radius of receiving hole. The research results show that the cavitation phenomenon in the pre-stage of deflector jet servo valve can be weakened by appropriately reducing the height of wedge tip, the radius of receiving hole and the thickness of jet disk, and the cavitation phenomenon in the pre-stage can be enhanced by increasing the length of the deflector jet servo valve, providing a basis for improving the stability and reliability of deflector jet servo valve.

Rationale Cancer of unknown primary (CUP) is a group of rare malignancies with poor prognosis and unidentifiable tissue-of-origin. Distinct DNA methylation patterns in different tissues and cancer types enable the identification of the tissue of origin in CUP patients, which could help risk assessment and guide site-directed therapy. Methods Using genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) and machine learning methods, we developed a 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP). MFCUP was further validated with public-available methylation array data of 2977 specimens and targeted methylation sequencing of 78 Formalin‐fixed paraffin‐embedded (FFPE) samples from a single center. Results MFCUP achieved an accuracy of 97.2% in a validation cohort ( n = 5923) representing 25 cancer types. When applied to an Infinium 450 K array dataset ( n = 1052) and an Infinium EPIC (850 K) array dataset ( n = 1925), MFCUP achieved an overall accuracy of 93.4% and 84.8%, respectively. Based on MFCUP, we established a targeted bisulfite sequencing panel and validated it with FFPE sections from 78 patients of 20 cancer types. This methylation sequencing panel correctly identified tissue of origin in 88.5% (69/78) of samples. We also found that the methylation levels of specific CpGs can distinguish one cancer type from others, indicating their potential as biomarkers for cancer diagnosis and screening. Conclusion Our methylation-based cancer classifier and targeted methylation sequencing panel can predict tissue of origin in diverse cancer types with high accuracy.

Vascular dementia (VaD) is a cognitive disorder characterized by a decline in cognitive function resulting from cerebrovascular disease. The hippocampus is particularly susceptible to ischemic insults, leading to memory deficits in VaD. Astaxanthin (AST) has shown potential therapeutic effects in neurodegenerative diseases. However, the mechanisms underlying its protective effects in VaD and against hippocampal neuronal death remain unclear. In this study, We used the bilateral common carotid artery occlusion (BCCAO) method to establish a chronic cerebral hypoperfusion (CCH) rat model of VaD and administered a gastric infusion of AST at 25 mg/kg per day for 4 weeks to explore its therapeutic effects. Memory impairments were assessed using Y-maze and Morris water maze tests. We also performed biochemical analyses to evaluate levels of hippocampal neuronal death and apoptosis-related proteins, as well as the impact of astaxanthin on the PI3K/Akt/mTOR pathway and oxidative stress. Our results demonstrated that AST significantly rescued memory impairments in VaD rats. Furthermore, astaxanthin treatment protected against hippocampal neuronal death and attenuated apoptosis. We also observed that AST modulated the PI3K/Akt/mTOR pathway, suggesting its involvement in promoting neuronal survival and synaptic plasticity. Additionally, AST exhibited antioxidant properties, mitigating oxidative stress in the hippocampus. These findings provide valuable insights into the potential therapeutic effects of AST in VaD. By elucidating the mechanisms underlying the actions of AST, this study highlights the importance of protecting hippocampal neurons and suggests potential targets for intervention in VaD. There are still some unanswered questions include long-term effects and optimal dosage of the use in human. Further research is warranted to fully understand the therapeutic potential of AST and its application in the clinical treatment of VaD.

BACKGROUND:Pancreatic cancer (PC) is a lethal malignancy characterized by poor prognosis and high mortality. We found the highly expressed RNA-binding motif protein 47 (RBM47) in PC progression. The RBM47 expression was negatively correlated with natural killer (NK) cell infiltrate in PC. Moreover, RBM47 was predicted to bind to the 3'-UTR region of Protein Disulfide Isomerase Family A Member 6 (PDIA6), an oncogene of the development of PC. Therefore, we supposed that RBM47 might affect PC progression by regulating PDIA6. METHODS:Bioinformatics analysis was performed to screen the candidate gene affecting PC progression using public databases. Loss- and gain-of-function effects of RBM47 on cell proliferation, tumor growth, and immune evasion were determined by CCK-8, EdU incorporation, colony formation assays, the xenogeneic tumor model, and co-culture system of PC and NK-92 cells. RBM47-RNA immunoprecipitation (RIP) followed by PCR and dual luciferase reporter assay were used to detect whether RBM47 could interact with the PDIA6 mRNA and how RBM47 would regulate the transcriptional activity of PDIA6, respectively. Simultaneous overexpression of PDIA6 in RBM47 knockdown PC cells was conducted to clarify whether PDIA6 would mediated effects of RBM47. Given the important role of cellular metabolism in cells proliferation and immune evasion, PC cells with RBM47 knockdown were subjected to metabolomics analysis to further investigate how RBM47 regulate PC progression. RESULTS:RBM47 overexpression drove PC progression by promoting cell proliferation and xenografted tumor growth. Consistently, our results showed that RBM47 overexpression weakened sensitivity of PC cells to cytotoxic NK cells. However, RBM47 knockdown exhibited the opposite effects on proliferation and immune evasion of PC cells. RBM47 was able to bind to the 3'-UTR region of PDIA6, maintained PDIA6 mRNA stability, and increased the PDIA6 expression in PC cells. Rescue experiments supported that PDIA6 overexpression reversed the suppressing effects of RBM47 knockdown on cell proliferation and immune evasion. RBM47 knockdown significantly changed metabolites of PC cells. CONCLUSIONS:In summary, our findings demonstrate that RBM47 contributes to PC progression, which might be mediated by the upregulated PDIA6 expression and the altered cellular metabolites in PC cells, offering a potential therapeutic target for PC treatment.

The proliferation of tumors is not merely self-regulated by the cancer cells but is also intrinsically connected to the tumor microenvironment (TME). Within this complex TME, cancer-associated fibroblasts (CAFs) are pivotal in the modulation of tumor onset and progression. Rich signaling interactions exist between CAFs and tumor cells, which are crucial for tumor regulation. Long non-coding RNAs (LncRNAs) emerge from cellular transcription as a class of functionally diverse RNA molecules. Recent studies have revealed that LncRNAs are integral to the crosstalk between CAFs and tumor cells, with the capacity to modify cellular transcriptional activity and secretion profiles, thus facilitating CAFs activation, tumor proliferation, metastasis, drug resistance, and other related functionalities. This comprehensive review revisits the latest research on LncRNA-mediated interactions between CAFs and tumor cells, encapsulates the biological roles of LncRNAs, and delves into the molecular pathways from a broader perspective, aspiring to offer novel perspectives for a deeper comprehension of the etiology of tumors and the enhancement of therapeutic approaches.

Identifying lncRNA-protein interactions (LPIs) is an important biomedical task, facilitating the comprehension of the biological functions and mechanisms of lncRNAs. Many computational methods have been developed for this task, especially graph neural network (GNN)-based methods have attracted increasing attention. Typically, the LPI network involves two types of interaction domains: the interactive domain capturing the direct interaction information between lncRNAs and proteins, and the collaborative domain reflecting the collaboration information among lncRNAs or proteins. However, existing GNN-based methods only leverage one of them to obtain topological information, which cannot fully characterize lncRNAs and proteins, resulting in suboptimal node representations. Moreover, each domain contains task-irrelevant redundant information, posing a challenge in effectively integrating information from different domains. To address these issues, we propose a novel Cross-domain Contrastive Graph Neural Network (CCGNN) for predicting potential LPIs. CCGNN employs a multi-domain encoder that consists of an interactive domain encoder and two collaborative domain encoders to capture valuable information from each interaction domain. Subsequently, domain-adaptive fusion is designed to integrate information from different domains to acquire comprehensive node representations. Furthermore, cross-domain contrastive learning is devised to enrich the node representations, drawing inspiration from the information bottleneck principle by retaining as much task-relevant information as possible within each domain and minimizing mutual information between representations across different domains. Extensive experiments on four real-world datasets demonstrate the superiority of CCGNN over state-of-the-art methods, and a further case study and generalization analysis illustrate the effectiveness of CCGNN in the biomedical link prediction tasks.