Background:Pancreatic cancer (PC) has escalating incidence and mortality rates, with poor prognosis and resistance to traditional therapies, highlighting the urgent need for new drug development. Bergamottin, a natural furanocoumarin from bergamot oil, has diverse biological activities, but its anticancer effects on PC remain scarce and limited. This study aimed to investigate the anticancer properties of bergamottin in PC and elucidate the regulatory mechanism of PPAR-γ in inducing apoptosis. Methods:In vitro, real-time cellular analysis (RTCA), immunofluorescence, transwell assay, migration assay, and flow cytometry were used to assess bergamottin's effects on proliferation, migration, invasion, and apoptosis of PANC-1 and PATU8988 cells. In vivo, tumor xenograft experiments were conducted to verify its anticancer effect. RNA sequencing (RNA-seq) analyzed gene expression changes in PANC-1 cells before and after bergamottin intervention. PPAR-γ inhibition (GW9662) was used to validate the mechanism. Results:Bergamottin significantly inhibited proliferation, invasion, and migration of PANC-1 and PATU8988 cells, and induced their apoptosis in vitro; it also suppressed PC progression in xenograft mouse models. RNA-seq identified 317 up-regulated and 111 down-regulated differentially expressed genes (DEGs), with functional analysis suggesting involvement of the PPAR signaling pathway. Validation via PPAR-γ inhibitor confirmed the regulatory role of PPAR-γ. Conclusions:Bergamottin effectively suppresses PC cell proliferation, invasion, and migration, and promotes apoptosis by regulating PPAR-γ. These findings suggest that bergamottin exhibits promising preclinical anti-tumor activity against PC.
Background:The incidence of endometrial cancer among elderly women has been increasing year by year, and endometrioid endometrial carcinoma (EC) is the predominant histological subtype of endometrial cancer. Unfortunately, elderly patients with advanced-stage endometrioid EC continue to experience unfavorable prognostic outcomes. To date, reliable instruments for individualized prognosis estimation among elderly patients with stage III-IV endometrioid EC are still lacking. Therefore, this investigation sought to determine prognostic indicators and develop nomograms for the prediction of overall survival (OS) and cancer-specific survival (CSS) in this patient population. Methods:This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database, analyzing eligible elderly patients (aged ≥60 years) with stage III-IV endometrioid EC. The Kaplan-Meier approach was employed for survival analysis, while univariate and multivariate Cox regression analyses were used to identify factors independently associated with prognosis. Based on these characteristics, nomograms were constructed and subsequently evaluated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) to assess their discriminative performance, model fit, and potential clinical utility. Results:Multivariable Cox proportional hazards regression showed that age, tumor grade, N stage, chemotherapy, radiotherapy, surgery, and brain metastasis were independently linked to OS as well as CSS. The nomograms incorporating these variables demonstrated good predictive performance in both the training and validation cohorts. The calculated area under the curve (AUC) for OS ranged from 0.76 to 0.88, while those for CSS ranged from 0.77 to 0.84. Calibration curves demonstrated excellent agreement between the predicted and observed survival probabilities. Moreover, DCA indicated that the nomograms provided favorable net clinical benefits across a broad range of threshold probabilities. Conclusions:The constructed nomograms demonstrated satisfactory performance in predicting survival outcomes among elderly patients with advanced endometrioid EC, with good discriminatory ability. These models may facilitate individualized risk stratification, although further external validation is warranted before routine clinical application.
Background:The TRIM32 has been implicated in tumorigenesis across various cancers; however, its functional significance in head and neck squamous cell carcinoma (HNSCC) requires systematic investigation. This study sought to explore the expression and biological function of TRIM32 in HNSCC tissues to identify new targets or biomarkers for HNSCC diagnosis and treatment. Methods:HNSCC samples were extracted for TRIM32 expression profiling, with subsequent integration of clinical samples for validation. Gene Set Enrichment Analysis (GSEA) was performed using the c2.cp.kegg.v7.4.symbols.gmt gene set. Immune cell infiltration was evaluated using the ESTIMATE algorithm. Following TRIM32 knockdown via small interfering RNA (siRNA) in HNSCC cell lines (HSC-3, FADU), proliferation and invasion capacities were assessed using Cell Counting Kit-8 (CCK-8) and Transwell assays, respectively. Western blotting was conducted to analyse protein expression within the TRIM32-p53-LAMP1/2-LC3B pathway. Results:Integrated bioinformatics analysis and clinical sample validation revealed significantly elevated TRIM32 expression in HNSCC, correlating with poor patient prognosis. GSEA demonstrated significant enrichment of autophagy and p53 signalling pathways within the TRIM32 high-expression group. In vitro experiments confirmed that TRIM32 silencing suppressed proliferation and invasion capacities in HSC-3 and FADU cell lines. Western blotting further delineated that TRIM32 regulates autophagic flux through the TRIM32-p53-LAMP1/2-LC3B axis. The ESTIMATE algorithm indicated a significant association between TRIM32 expression and immune cell infiltration, suggesting a potential role in remodelling the tumour immune microenvironment. Conclusions:TRIM32 expression is significantly elevated in HNSCC, indicating its potential as an adverse prognostic marker. Experimental evidence demonstrates that TRIM32 facilitates cellular proliferation and migration, significantly influences lysosomal function and autophagy processes within HNSCC cells, and is verified to negatively regulate tumour protein 53 (TP53). These mechanisms contribute to the aggressive behaviour of HNSCC.
Background:Colon cancer (CC) is a heterogeneous disease with increasing morbidity and mortality. Ferroptosis, a recently identified form of regulated cell death (RCD) characterized by the massive accumulation of iron-dependent lipid peroxidation (LPO), has been proven to be closely associated with various biological behaviors of CC. Moreover, accumulating evidence reveals that ALOX12 is essential for p53-mediated ferroptosis through distinct pathways. Therefore, our study aims to explore the specific mechanisms by which ALOX12 regulates tumorigenesis in CC through ferroptosis pathway. Methods:We explored the characteristics of ALOX12 expression and its correlation with prognosis in CC patients and cell lines, followed by a series of functional assays in vitro and in vivo to investigate the mechanisms underlying the functions of ALOX12 in p53-driven ferroptosis. Results:We discovered that ALOX12 was downregulated in CC tissues and cell lines, which was correlated with poor prognosis. Furthermore, overexpression of ALOX12 suppressed cell proliferation and tumorigenesis by upregulating reactive oxygen species (ROS)-induced stress. We continuously found that increased ALOX12 levels enhanced the sensitivity of CC cells to a distinct ferroptosis, which required both p53 activation and additional ROS, and differed from that induced by erastin. Conclusions:Our study demonstrates the oncosuppressive behavior of ALOX12 in CC. Mechanistically, we discover that p53 can indirectly activate ALOX12 function by suppressing its transcriptional target SLC7A11, resulting in an ALOX12-dependent ferroptotic pathway. Targeting ALOX12 may provide a novel biomarker or new therapeutic strategies for improving the prognosis of CC.
Background:Breast cancer is the most common malignancy in women globally and a leading cause of cancer-related death. This study aimed to differentiate breast cancer lung metastasis (BCLM) from primary lung cancer (PLC) in patients with breast cancer by integrating clinicopathological characteristics and computed tomography (CT) radiomics to develop and validate a predictive model. Methods:A total of 158 patients with breast cancer who were diagnosed with lung metastasis or PLC between 2013 and 2023 were retrospectively included. The patients were randomly assigned to a training group (n=111) and a testing group (n=47). Clinical data and CT images were collected. Independent predictors were identified using logistic regression analysis. Radiomics features were extracted from intratumoral, peritumoral, and combined regions using three-dimensional (3D) Slicer software. Results:There were 100 patients diagnosed with PLC, and 58 were diagnosed with BCLM. Patients in the BCLM group demonstrated higher tumor (T) stage, node (N) stage, human epidermal growth factor receptor 2 positivity, Ki-67 levels, and serum carbohydrate antigen 153 concentrations compared with those of the PLC group. The clinical model yielded an area under the curve (AUC) of 0.704 in the training group and 0.760 in the testing group. The radiomics models achieved AUC values ranging from 0.742 to 0.952. The combined model demonstrated AUC values of 0.959 in the training group and 0.946 in the testing group. Conclusions:A combined model incorporating clinical and radiomics features demonstrated high predictive performance for distinguishing BCLM from PLC, supporting improved diagnostic differentiation and clinical decision making.
Background:Locally advanced pancreatic cancer (LAPC) is mostly unresectable at initial diagnosis. Conversion therapy has become core treatment to downstage tumors for radical resection, yet consensus on optimal first-line regimen remains lacking. This real-world study compared three mainstream conversion regimens and identified independent survival predictors to guide individualized treatment. Methods:A single-center retrospective cohort of 172 unresectable LAPC patients receiving gemcitabine plus nab-paclitaxel (AG), modified FOLFIRINOX (mFOLFIRINOX), or AG sequential chemoradiotherapy (CRT) from 2019 to 2023 was enrolled. Tumor conversion, surgical, survival and safety outcomes were analyzed. Cox regression was adopted to screen independent prognostic factors, with P<0.05 defined as statistical significance. Results:The overall conversion rate reached 38.4%. mFOLFIRINOX yielded the highest conversion rate (52.8%) versus AG (32.6%) and AG + CRT (33.3%). Among 66 converted patients, 87.9% achieved R0 resection, with 34.8% major pathological response (MPR). Median overall survival (OS) of converted patients reached 32.5 months, far superior to non-converted cases (12.3 months). Multivariate analysis confirmed preoperative carbohydrate antigen 19-9 (CA19-9) normalization and prognostic nutritional index (PNI) ≥41.7 as two independent favorable prognostic markers. Grade 3-4 adverse events were most frequent in the mFOLFIRINOX group (56.6%). Conclusions:For physically fit unresectable LAPC patients, mFOLFIRINOX provides superior conversion efficacy despite higher toxicity. Normalized CA19-9 and well-maintained nutritional status are reliable prognostic biomarkers. This real-world evidence supports mFOLFIRINOX as preferred first-line conversion regimen and routine monitoring of CA19-9 and nutrition during therapy.
Background:Radiotherapy resistance remains a significant challenge in the management of locally advanced rectal cancer (LARC). To date, no universally applicable and reliable prognostic marker has been established for clinical practice. Thus, reliable biomarkers need to be identified and the molecular mechanisms underlying radiotherapy resistance need to be investigated to improve patient prognosis. The study aimed to identify a radiotherapy-related signature for evaluating radiotherapy response and predicting the overall survival (OS) of patients with rectal cancer. Methods:Multiple independent sources of transcriptomic datasets from The Cancer Genome Atlas (TCGA) (training cohort, n=154) and the Gene Expression Omnibus (GEO), including GSE35452 (containing radiotherapy response and non-response cohorts) and GSE87211 (validation cohort), were systematically integrated. A prognostic signature was developed by identifying radiotherapy-related genes through differential expression analysis and weighted gene correlation network analysis (WGCNA), followed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Functional enrichment, immune microenvironment characterization, and single-cell RNA sequencing (scRNA-seq) analyses were subsequently performed to explore the mechanisms underlying radiotherapy resistance. Results:A four-gene prognostic signature comprising CUTA, IZUMO2, PALB2, and PSCA was established. The signature effectively stratified patients into high- and low-risk groups with distinct OS outcomes (TCGA: P<0.001; GSE87211: P=0.002) and served as an independent prognostic factor [multivariate Cox hazard ratio (HR) =2.532, P<0.001]. The high-risk group exhibited distinct immune environment characteristics and enrichment of epithelial-mesenchymal transition (EMT) pathways compared to the low-risk group. The scRNA-seq analysis revealed that PSCA expression was restricted to an epithelial subpopulation characterized by enhanced cell-cell communication and a more advanced pseudotime trajectory. Conclusions:This study developed and validated a four-gene signature for survival prediction in rectal cancer (RC). Functional enrichment and immune microenvironment characterization analyses indicated that the signature was associated with tumor heterogeneity and differential treatment responses in RC. The single-cell analysis indicated that PSCA may be a key gene in this process.
Background:Glutathione (GSH) has been increasingly implicated in tumor progression. This study aimed to investigate the role of GSH in oral squamous cell carcinoma (OSCC) and to characterize how its metabolism is modulated. Methods:Following treatment with osteoblast-specific factor 2 (OSF-2, POSTN), HN6 cells were subjected to RNA sequencing, metabolomics analysis and protein mass spectrometry. Then the levels of GSH, glutathione disulfide (GSSG) and reactive oxygen species (ROS) were measured. Western blot analysis was employed to determine the expression of PRKRA in recombinant human POSTN (rhPOSTN)-treated HN6 cells. Statistical analysis was performed using SPSS 19.0 and P<0.05 was considered to be statistically significant. Results:Following rhPOSTN treatment, the GSH expression levels were significantly reduced, while the GSSG and ROS levels were significantly increased. Similarly, PRKRA expression was also significantly decreased. Overexpression of PRKRA markedly elevated the GSH levels and reduced the GSSG and ROS levels in OSCC cells. However, this effect was reversed by rhPOSTN. Finally, multiple pathways were implicated in POSTN-induced GSH reduction, of which the first three were apoptotic signaling, Wnt signaling and I-kappaB kinase/NF-kappaB signaling pathways. Conclusions:Collectively, these findings indicate that POSTN reduced GSH levels through downregulating PRKRA, suggesting that GSH may serve as a potential therapeutic target in OSCC, thereby informing novel strategies for cancer intervention.
Background:Retinoblastoma (RB) is one of the most common primary intraocular malignancies in children. Its pathogenesis involves multiple signaling pathways that remain incompletely understood, and effective treatment strategies are still limited. This study aims to identify ferroptosis-related biomarkers and explore their roles in immune infiltration and therapeutic response in RB using bioinformatics approaches. Methods:To investigate the role of ferroptosis in RB, we analyzed the microarray dataset GSE166173 to identify differentially expressed genes (DEGs) between healthy controls and RB patients. Ferroptosis-related DEGs were further screened, and least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate genes at the intersection of RB and ferroptosis. Key genes were subsequently examined by gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), immune infiltration analysis, and drug sensitivity prediction. Results:Three ferroptosis-associated genes-NFE2L2, HSPB1, and JUN-were identified as potential diagnostic biomarkers for RB. Immune infiltration analysis revealed their potential roles in shaping the tumor immune microenvironment. Drug sensitivity analysis suggested AUY922, AG.014699, and AMG.706 as candidate therapeutic agents. Reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) validation confirmed significant downregulation of JUN, HSPB1, and NFE2L2 in RB Y79 cells compared with retinal pigment epithelial (RPE) cells. Conclusions:This study identified three ferroptosis-related genes (FRGs) as potential diagnostic biomarkers of RB. Their association with immune cell infiltration provides new insights into the molecular mechanisms of RB and highlights potential therapeutic opportunities.
Background:Asciminib, a novel BCR::ABL1 inhibitor that functions by specifically targeting the myristoyl pocket, has shown superior efficacy and favorable safety and tolerability compared with adenosine triphosphate-competitive tyrosine kinase inhibitors (TKIs) in patients with newly diagnosed chronic myeloid leukemia in chronic phase (CML-CP). Flumatinib, a second-generation TKI available exclusively in China, does not have a head‑to‑head comparison with asciminib till date with asciminib. Therefore, this study aimed to conduct an anchored matching‑adjusted indirect comparison using data from the ASC4FIRST and FESTnd trials to fill this evidence gap. Methods:Imatinib was the common comparator across the ASC4FIRST (NCT04971226) and FESTnd (NCT02204644) trials. To match the two populations, effect modifiers and baseline variables were identified. Adjusted estimates were derived from individual patient-level data (ASC4FIRST) for asciminib and imatinib and from aggregate data for flumatinib (FESTnd). Outcomes were compared based on early molecular response (EMR), major molecular response (MMR), and treatment discontinuation due to adverse events (AEs). Results:Asciminib demonstrated significantly higher EMR rates at 12 weeks (adjusted: 90.0%) than did flumatinib (82.1%), yielding a significantly higher odds ratio [odds ratio (OR): 1.95; 95% confidence interval (CI): 1.05-3.73; P=0.03]. MMR rates, both at 48 and 96 weeks, were significantly higher for asciminib (adjusted: 66.5% and 74.0%, respectively) than for flumatinib (an estimated 52.6% and 61.3%, respectively), with an OR of 1.79 (95% CI: 1.17-2.75; P=0.006). Safety analysis showed fewer discontinuations due to AEs at 48 weeks with asciminib (5.5%) than with flumatinib (10.2%), corresponding to a significantly lower risk of discontinuation due AEs (risk ratio: 0.29; 95% CI: 0.10-0.84; P=0.02). Conclusions:A robust statistical model indicated that asciminib provides consistently superior efficacy and safety over flumatinib, supporting its value as a first-line treatment option for patients with CML-CP.
Background:Oral squamous cell carcinoma (OSCC) is the predominant histopathological subtype of oral malignancies, and histopathology-based diagnosis remains central to clinical management. With the rapid development of digital pathology and deep learning, automated analysis of histopathology images has shown considerable potential for improving screening efficiency and reducing inter-observer variability. However, in both public datasets and real-world research settings, only image-level labels are commonly available, whereas region-level annotations are often lacking. This limitation makes weakly supervised modeling necessary and also restricts rigorous validation of spatial interpretability. Methods:To address these challenges, we developed a weakly supervised deep learning framework based on patch-level representation learning and multiple-instance aggregation for image-level OSCC diagnosis with exploratory heatmap visualization. Specifically, histopathology images were divided into patches and encoded into feature embeddings. UNI 2, a recently developed pathology foundation model with highly competitive performance in contemporary computational pathology benchmarks, was adopted as the primary feature extractor to obtain informative patch representations. ResNet-50 was additionally implemented as a conventional baseline encoder for comparative evaluation. A self-attention-based multiple-instance aggregation module was then introduced to capture dependencies among instances within each patch set and to generate binary OSCC predictions. Results:In five-fold cross-validation, the UNI-2-based model achieved a mean area under the curve (AUC) of 0.9919, compared with 0.9899 for the ResNet-50 baseline. On the independent external validation cohort, UNI 2 further achieved an AUC of 0.9992, outperforming ResNet-50, which achieved an AUC of 0.9564. As an exploratory interpretability analysis, attention responses were visualized as heatmaps to inspect model focus patterns and support qualitative error review, without assuming verified lesion-level correspondence in the absence of region-level annotations. Conclusions:This framework demonstrates strong image-level diagnostic performance for OSCC and provides exploratory visualization for qualitative model review without requiring additional fine-grained annotations, offering a reusable technical pathway for weakly supervised OSCC histopathology modeling and future multicenter validation.
Background and Objective:Triple-negative breast cancer (TNBC) remains one of the most aggressive and therapeutically challenging breast cancer subtypes. Lack of targeted therapies in TNBC mandates identification of novel molecular and therapeutic vulnerabilities. Human epidermal growth factor 2 (HER2)-low breast cancer, defined by immunohistochemistry (IHC) score 1+ or 2+ with negative in situ hybridization (ISH), represents an intermediate phenotype between HER2-positive and HER2-negative disease. This manuscript aims at summarizing the biology, epidemiology, and clinical management of HER2-low TNBC, discussing recent advances, challenges, and future directions. Methods:We queried the PubMed database, Google Scholar and other databases for studies published in English (up to April 2026) and available online, using search terms (such as TNBC, breast cancer, HER2-low, HER2-ultralow, trastuzumab deruxtecan, T-DXd, T-DM1), and selected publications based on their relevance to the topic. We also hand-searched reference lists of relevant papers to provide the most updated and comprehensive review of the topic, and utilized "ClinicalTrials.gov" to identify studies exploring anti-HER2 directed treatments in HER2-low and HER2-ultralow breast cancers, with an emphasis on TNBC. Key Content and Findings:HER2-low TNBC represents a relatively large proportion of TNBC cases, and is gaining recognition as a distinct therapeutic and biological subtype, affecting clinical management. In HER2-low TNBC patients, utilization of anti-HER2 targeted therapies, similar to those in HER2-positive breast cancer patients, has been recently shown to offer clinical advantages. Namely, antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd), confers survival benefits in patients with HER2-low breast cancer, including HER2-low TNBC patients with metastatic disease. Ongoing clinical trials investigate T-DXd in various clinical settings as a monotherapy or in combination with chemotherapeutic, immunotherapeutic and targeted drugs in patients with hormone (HR) receptor-negative and HER2-low expressing breast cancer. New HER2-directed ADCs are also being developed, with several being investigated in clinical trials. Conclusions:The development of new HER2-directed treatments is revolutionizing the management of TNBC patients, and mandates further research to better detect and target clinically meaningful HER2-positive signals. This manuscript provides analysis and synthesis of current literature to potentially guide future research and clinical efforts directed at improving outcomes of patients with TNBC.
Background:Triple-negative breast cancer (TNBC) is a highly aggressive subtype characterized by significant heterogeneity, making accurate prognostic assessment essential for clinical management. However, existing predictive models predominantly rely on single-modality data and lack external validation, which may limit their generalizability and clinical applicability. In light of these limitations, this study sought to explore the feasibility of developing a multi‑center prognostic model incorporating clinical and multimodal imaging data, and to preliminarily examine its potential value in supporting risk stratification and treatment decision-making for TNBC patients. Methods:This retrospective, two-center study aimed to develop and externally validate a machine learning-based multimodal radiomics model. This study analyzed 108 patients with pathologically confirmed TNBC between June 2017 and November 2022. Inclusion criteria: pathologically confirmed TNBC, complete clinical/imaging data, no prior anticancer therapy. Exclusion criteria: incomplete follow-up, poor image quality. Pathological confirmation of TNBC [estrogen receptor (ER)/progesterone receptor (PR)/human epidermal growth factor receptor 2 (HER2) negativity by American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines] served as the reference standard. Clinical predictors screened included age, menopausal status, tumor size, nodal status, World Health Organization (WHO) grade, lymphovascular invasion, perilesional edema, and Breast Imaging Reporting and Data System (BI-RADS) features. Clinical, pathological, and imaging [magnetic resonance imaging (MRI) and digital breast tomosynthesis (DBT)] data were collected. Progression-free survival (PFS) risk factors were identified using Cox regression and Kaplan-Meier analysis with log-rank tests. Regions of interest (ROIs), encompassing the primary tumor as well as 5- and 10-mm peritumoral areas, were manually delineated on MRI [T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC)] and DBT [craniocaudal (CC) and mediolateral oblique (MLO) views] images, followed by radiomic feature extraction. Feature selection was performed using the two-sample t-test and least absolute shrinkage and selection operator (LASSO) regression. Five machine learning algorithms [AdaBoost (AB), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), logistic regression (LR), and random forest (RF)] were used to construct models based on clinicopathological, conventional imaging, and radiomic features (tumor-only, tumor + peritumoral, and peritumoral). Rad scores derived from multimodal features were integrated with significant clinicopathological and conventional imaging variables through multivariate Cox stepwise backward regression to construct a predictive nomogram. The nomogram's performance was subsequently evaluated using calibration curves and decision curve analysis. Results:The study included 108 TNBC patients (mean age, 50.42 years; range, 25-79 years) with a median follow-up of 56 months (range, 12-86 months) until August 31, 2024. The cohort was divided into a training set (n=75; 15 progressions) and an external validation set (n=33; 7 progressions). A nomogram incorporating five independent risk factors for PFS-perilesional edema, WHO grade, lymphovascular invasion, peritumoral 10-mm DBT Rad score, and tumor MRI Rad score-demonstrated strong predictive performance, with areas under the curve (AUCs) of 0.858 [training, 95% confidence interval (CI): 0.788-0.928] and 0.736 (validation, 95% CI: 0.676-0.796), and concordance indices (C-indices) of 0.836 (training) and 0.712 (validation). Calibration curves showed good agreement between predicted and observed outcomes, and decision curve analysis confirmed clinical utility across a range of threshold probabilities. Conclusions:The proposed nomogram exhibits promising predictive performance for PFS in TNBC and may offer a useful reference for future prognostic investigations.
Background:Glioma remains a challenging malignancy with limited therapeutic options, and the underlying molecular mechanisms driving its progression are not fully understood. Although miR-506-5p has been implicated in various tumors, its role in glioma progression and the associated mechanisms warrant further investigation. This study aims to explore whether miR-506-5p suppresses glioma growth and invasion by targeting MAPK7, thereby regulating matrix metalloproteinases (MMPs) and epithelial-mesenchymal transition (EMT). Methods:Expression levels of miR-506-5p and MAPK7 in glioma tissues and cell lines were examined by reverse transcription quantitative polymerase chain reaction (RT-qPCR). The direct interaction between miR-506-5p and MAPK7 was validated using dual-luciferase reporter assays. Gain- and loss-of-function approaches were employed in U87 glioma cells, followed by 5-ethynyl-2'-deoxyuridine (EdU), wound healing, and Transwell assays to assess proliferation, migration, and invasion. Western blotting was performed to evaluate MAPK7, MMPs, and EMT-related markers. Rescue experiments were conducted to confirm the involvement of MAPK7 in miR-506-5p-mediated effects. Additionally, a xenograft model was established to evaluate the anti-tumor activity of miR-506-5p in vivo. Results:MiR-506-5p expression was significantly downregulated, while MAPK7 expression was markedly upregulated, in glioma tissues and cell lines compared with controls. Dual-luciferase reporter assays confirmed that miR-506-5p directly targeted the 3'-UTR of MAPK7. Overexpression of miR-506-5p suppressed cell proliferation, migration, invasion, and EMT, accompanied by decreased expression of MAPK7, MMP9, MMP12, N-cadherin, and vimentin, and increased E-cadherin expression. Conversely, miR-506-5p knockdown produced opposite effects. Rescue experiments demonstrated that MAPK7 overexpression reversed the suppressive effects induced by miR-506-5p, whereas MAPK7 knockdown reversed the pro-tumorigenic effects of miR-506-5p inhibition. In vivo, miR-506-5p overexpression significantly inhibited xenograft tumor growth, reduced Ki-67 positivity, and recapitulated the molecular changes observed in vitro. Conclusions:MiR-506-5p functions as a tumor suppressor in glioma by directly targeting MAPK7, thereby inhibiting MMP expression and EMT to suppress tumor growth, migration, and invasion. The miR-506-5p/MAPK7 axis represents a potential therapeutic target for glioma intervention.
Background:Hepatocellular carcinoma (HCC) remains a significant global health burden, with high mortality rates due to late diagnosis and limited therapeutic options. Palmitoylation, a reversible post-translational modification, has emerged as a critical regulator of cancer progression, yet its role in HCC remains underexplored. This study aims to investigate the diagnostic and prognostic value of palmitoylation-related genes (acyltransferase and acylthioesterase) in HCC, focusing on their potential as biomarkers and therapeutic targets. Methods:Transcriptomic and clinicopathologic data from 377 HCC patients in The Cancer Genome Atlas (TCGA) cohort and 231 HCC patients in the International Cancer Genome Consortium (ICGC) cohort were analyzed. Differential gene expression analysis identified palmitoylation-related genes significantly associated with HCC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to construct a predictive model based on these genes. The model's performance was validated using the ICGC dataset. Functional enrichment, immune microenvironment analysis, and machine learning approaches were further applied to explore the biological roles and clinical relevance of the identified genes. Results:We identified 20 differentially expressed palmitoylation-related genes in HCC, of which eight were significantly associated with overall survival (OS). A predictive model incorporating ZDHHC18, ZDHHC23, and PPT1 was developed, stratifying patients into high- and low-risk groups with distinct survival outcomes. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.700 (1 year), 0.653 (2 years), and 0.634 (3 years) in the TCGA cohort, and 0.773 (1 year), 0.705 (2 years), and 0.703 (3 years) in the ICGC cohort. Functional enrichment analysis revealed significant differences in immune-related pathways between risk groups, highlighting the model's potential in guiding immunotherapy strategies. Conclusions:This study establishes a novel predictive model based on palmitoylation-related genes, offering valuable insights into HCC prognosis and potential therapeutic targets. The model's ability to stratify patients by risk and its association with immune microenvironment characteristics underscores its clinical relevance, paving the way for personalized treatment strategies in HCC.
Background:Steroid 5α-reductase type II (SRD5A2) is an enzyme that plays a significant role in steroid metabolism. It is mainly located in the endoplasmic reticulum membrane and can convert testosterone (T) into more active dihydrotestosterone (DHT). It plays a critical role in gender differentiation and androgen physiology, and is implicated in tumorigenesis and progression. It has been reported in various types of tumors. Among the most common gastrointestinal malignancies, colorectal cancer (CRC) imposes a significant global health burden. Nevertheless, the expression profile and clinical significance of SRD5A2 in CRC are poorly characterized, and its precise functional role requires further investigation. This study aimed to investigate the expression profile, prognostic significance, biological functions, and potential mechanisms of SRD5A2 in CRC. Methods:This retrospective cohort study included 180 patients with CRC. Tissue microarrays (TMAs) were constructed and subjected to SRD5A2 immunohistochemistry (IHC), and H-scores were used to stratify patients into high- and low-expression groups for clinicopathological and overall survival (OS) analyses. Univariate and multivariate Cox regression analyses, as well as subgroup Kaplan-Meier analyses, were performed to evaluate the prognostic value and stability of SRD5A2. In vitro, SRD5A2 knockdown was performed in CRC cells, followed by assays of cell viability, migration, invasion, and apoptosis. Epithelial-mesenchymal transition (EMT)-related proteins, classical oncogenic signaling pathways, and metabolic homeostasis were further evaluated by western blotting (WB), glucose consumption, adenosine triphosphate (ATP), and reactive oxygen species (ROS) assays. Results:SRD5A2 expression was significantly upregulated in CRC tissues compared with paired distal normal mucosa (P<0.001). High SRD5A2 expression was associated with adverse clinicopathological features and significantly shorter OS (P<0.001). Multivariate Cox regression analysis further showed that high SRD5A2 expression remained independently associated with poorer OS. Subgroup Kaplan-Meier analyses demonstrated that this adverse prognostic association was generally maintained across several T-, N-, and M-based strata. In vitro, SRD5A2 knockdown suppressed cell proliferation, migration, and invasion, while promoting apoptosis. Mechanistically, SRD5A2 silencing reversed EMT-related molecular changes, suppressed the MAPK/ERK, JNK, NF-κB, and AKT/mTOR pathways, reduced glucose consumption and intracellular ATP levels, and increased intracellular ROS levels. Conclusions:SRD5A2 is upregulated in CRC tissues, and its high expression is related to aggressive clinicopathological features and poor OS. Multivariable survival analysis further suggests that SRD5A2 may serve as an independent prognostic factor in CRC. Functional experiments suggest that SRD5A2 has a pro-tumorigenic effect. Preliminary mechanistic analyses further suggest that SRD5A2 may exert its pro-tumorigenic effects through EMT-related molecular changes, activation of classical oncogenic signaling pathways, and maintenance of metabolic homeostasis.
