A machine learning-based pathomics model was investigated for its value and biological significance in predicting overall survival (OS) after surgery in pancreatic cancer patients. Data from 173 patients with pancreatic ductal adenocarcinoma (PDAC) who underwent surgery and continued follow-up in two centers were retrospectively analyzed. Pathomics parameters of both the tumor and peritumor were measured in all patients, and the optimal pathomics score (Pathscore) was calculated using five machine learning methods. The best Pathscore was then combined with multiple clinical parameters to analyze its incremental value and to construct a comprehensive nomogram. TCGA data, multiplex immunofluorescence, spatial analysis, and single-cell sequencing were used to explore the biological mechanisms of pathomics. In predicting OS, pathomics parameters from the tumor and peritumoral regions provided complementary prognostic information. The LASSO-based combined model achieved the best predictive accuracy. Multivariate Cox regression analysis identified T-stage, N-stage, CA19-9, and Pathscore as independent predictors of OS in patients with PDAC. The integrated nomogram demonstrated superior and more stable predictive performance. Analysis of the TCGA dataset suggested that the pathomics model was associated with the immune status of pancreatic cancer, a finding supported by trends in the validation cohort. Spatial analysis and single-cell analysis further revealed a strong association between the Pathscore and immune cell infiltration, in particular CD8+ T cells. Machine learning-based pathomics models can help to predict the immune status and OS of patients with PDAC. The integration of pathomics with clinical parameters provides a robust basis for immune evaluation, prognostic prediction, and therapeutic decision-making in PDAC. © 2026 The Pathological Society of Great Britain and Ireland.

Cancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment (TME) by influencing tumor progression, metastasis, and therapy resistance. Accumulating evidence suggests that CAFs undergo senescence, which can impact their effects on the TME. Here, we developed a machine learning-based prediction model, the Cellular Senescence Prediction Model (CSPM), to accurately identify senescent CAFs (sCAFs) based on single-cell RNA sequencing data. In colorectal cancer (CRC), the abundance of sCAFs strongly correlated with impaired chemotherapy responsiveness and poor prognosis. In preclinical models, including subcutaneous tumors, patient-derived organoids (PDOs), patient-derived organoid xenografts (PDOXs), and orthotopic tumors, sCAFs mediated chemoresistance through the senescence-associated secretory phenotype (SASP), with IL6 and CXCL12 being key contributors. Macrophage-derived IL1B triggered CAF senescence through the IL1B-IL1R1 interaction, promoting the accumulation of sCAFs in tumors. Spatial transcriptomics and multiplex immunohistochemistry revealed colocalization of IL1B+ macrophages and IL1R1+ sCAFs in the tumor stroma. Functional studies using fibroblast-specific Il1r1 knockout mice further confirmed that macrophage-derived IL1B induces CAF senescence via IL1R1, leading to SASP-driven chemotherapy resistance. These findings highlight the critical role of sCAFs in CRC chemoresistance and suggest that targeting the IL1B-IL1R1 axis may offer a promising strategy to enhance chemotherapy efficacy in CRC.

Background Hepatocellular carcinoma (HCC) is characterized by substantial heterogeneity and poor prognosis despite therapeutic advancements. Genes regulating tumor cell sensitivity to T cell-mediated killing (GSTTKs) have been implicated in immune evasion, yet their potential role in HCC molecular subtyping remains to be fully elucidated. Methods Multiomics data from TCGA-LIHC were integrated to identify eight prognostic GSTTKs through stringent selection criteria. Consensus clustering and UMAP were employed to define subtypes, with validation performed via nearest template prediction (NTP) algorithm across five independent cohorts. Genomic alterations, immune infiltration, pathway enrichment, and drug sensitivity were systematically analyzed. HBV infection associations were evaluated in four cohorts with complete etiology records. Subtype-specific drug responses were experimentally assessed using representative HCC cell lines. Results Three GSTTK subtypes were identified with distinct molecular and clinical characteristics, showing consistent prognostic trends across validation cohorts (p < 0.001). GSTTK2, enriched for TP53 mutations and elevated TMB, displayed an immune-excluded phenotype and poorest prognosis. GSTTK3 exhibited relatively favorable outcomes, metabolic pathway enrichment (cytochrome P450 and retinol metabolism), and features suggestive of an immune-permissive microenvironment. GSTTK1 demonstrated intermediate characteristics with LRP1B mutation enrichment. No significant association between HBV status and subtype distribution was observed across analyzed cohorts (p > 0.05), suggesting that the classification may reflect tumor-intrinsic molecular features, though the contribution of viral etiology to early tumor evolution cannot be excluded. Computational analysis predicted potential subtype-specific therapeutic vulnerabilities, and experimental validation indicated that vinorelbine exhibited enhanced cytotoxicity in GSTTK2-representative cells compared to other subtypes (p < 0.01). Conclusion This study proposes a GSTTK-based molecular classification integrating genomic, metabolic, and immune features, which may offer prognostic value and inform therapeutic stratification. Further prospective validation is warranted to establish clinical utility.

Recent advancements in multi-omics and big-data technologies have facilitated the discovery of numerous cancer prognostic biomarkers and gene signatures. However, their clinical application remains limited due to poor reproducibility and insufficient independent validation. Despite the availability of high-quality datasets, achieving reliable biomarker identification across multiple cohorts continues to be a significant challenge. To address these issues, we developed a comprehensive platform, SurvivalML, designed to support the discovery and validation of prognostic biomarkers and gene signatures using large-scale and harmonized data from 21 cancer types. Through SurvivalML, we identified DCLRE1B as a novel prognostic biomarker for hepatocellular carcinoma, with experimental confirmation of its role in promoting tumor progression. Additionally, we developed the Chinese glioblastoma prognostic signature (CGPS) and its simplified version, SCGPS, a three-gene model. Both demonstrated superior predictive performance compared to other glioblastoma signatures in our in-house cohort and five independent Chinese datasets. The SCGPS model was further validated in 109 clinical samples using multiplex immunofluorescence, showing strong consistency with the original CGPS model. Overall, SurvivalML provides a robust platform for the identification and validation of prognostic biomarkers and gene signatures, offering a valuable resource for advancing cancer research and clinical application.

Rationale: Cancer-associated fibroblasts (CAFs) exhibit diverse functions, yet their roles in colorectal cancer liver metastasis (CRLM) remain poorly understood. Methods: Through integrated analysis of single-cell RNA sequencing and spatial transcriptomics from colorectal cancer patients (CRCP: non-metastatic primary tumors; CRCM: metastatic primary tumors with liver metastases), combined with in vitro and in vivo models to investigate the role of CAFs in CRLM. In vitro experiments included six groups to reveal the role of SFRP1-producing CAFs, comprising PBS (control) and recombinant human SFRP1 (rhSFRP1) treated SW480 cells, PBS (control) and recombinant mouse SFRP1 (rmSFRP1) treated CT26 cells, and conditioned medium (CM) derived from CAF-NC and CAF-Sfrp1 treated CT26 cells. Preclinical models were further employed to elucidate the role of SFRP1 in CRLM. Subcutaneous xenografts models were constructed from PBS (control) and rhSFRP1 treated SW480 cells. For orthotopic tumor metastasis models, CT26 cells were pre-cultured with CAF-NC or CAF-Sfrp1 and then orthotopically injected into BALB/c mice. Results: We identified an inflammatory CAF subtype (CFD+ iCAFs) associated with poor clinical outcomes, advanced staging, and metastasis. Transcriptional regulation analysis revealed FOS-mediated differentiation of CFD+ iCAFs drives SFRP1 overexpression. In vitro and in vivo experiments confirmed that SFRP1-producing CAFs promote tumor stemness and epithelial-mesenchymal transition (EMT). Mechanistically, SFRP1 from CFD+ iCAFs binds FGFR2, activating the HIF1 signaling pathway to enhance tumor stemness, EMT, and CRLM progression. Conclusion: This study highlights CFD+ iCAFs as key regulators of tumor-stromal interactions and identifies SFRP1 as a potential therapeutic target in CRLM.

OBJECTIVE:Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients. METHODS:A retrospective analysis of 160 gastric cancer patients undergoing radical surgery with continuous follow-up was conducted. Pathomic parameters of the primary lesion and surrounding area were assessed. Six machine learning methods were employed to develop an optimal pathomics model and calculate the pathomics score (Pathscore). The Pathscore was integrated with clinical parameters to construct a comprehensive nomogram for accurate prognosis prediction. The TCGA and GEO databases were utilized for model validation and underlying mechanism exploration. RESULTS:The combined GBM-based pathomics model exhibited superior predictive performance, with 1-, 3-, and 5-year area under the curve (AUC) values of 0.837, 0.970, and 0.963, respectively, in the training set. The pathomics model demonstrated significant incremental value for clinical predictors. The nomogram incorporating age, M stage, TNM stage, and Pathscore achieved even higher AUCs of 0.954, 0.939, and 0.898 for 1-, 3-, and 5-year OS prediction, respectively. Bioinformatics analysis suggested that the pathomics model could predict OS by reflecting tumor immune status and NRP1 expression. CONCLUSION:Machine learning-based pathomics models can effectively predict OS in gastric cancer patients post-surgery. The integration of pathomics and clinical parameters enhances predictive accuracy compared to clinical factors alone, providing a reliable basis for prognostic prediction and personalized treatment decisions.

Background:Papillary thyroid cancer (PTC) patients with capsule invasion have a risk of poor clinical outcomes. The aim of this study was to assess the diagnostic accuracy of quantitative contrast-enhanced ultrasound (CEUS) for detecting capsule invasion and identifying the relationship between capsule invasion and sonographic features of PTC. Methods:Patients confirmed as having PTC underwent conventional ultrasound (US) and CEUS examinations. The conventional US and qualitative CEUS features of the PTC nodules and capsule invasion were evaluated and classified separately. The accuracy of conventional US and qualitative CEUS in predicting capsule invasion was compared separately. Moreover, quantitative parameters were analyzed in different groups. The presence or absence of capsule invasion and cervical lymph node metastasis (LNM) was confirmed by the pathological results. Results:The study finally included 107 patients with 109 PTC nodules. A total of 58 (53.2%) of PTC nodules had no capsule invasion, whereas the remaining 51 had capsule invasion [including 19 patients with extracapsular extension (ECE)]. A significant difference was found in nodules with non-capsule invasion, single capsule invasion, and ECE between the LNM and non-LNM groups (χ2=11.34, P<0.01). Single capsule invasion and ECE demonstrated a significant linear trend with cervical LNM (r=0.309, P=0.001). Independent predictive conventional US and qualitative CEUS features included nodule size, contour bulging, and capsular abutment on CEUS and discontinuous capsular enhancement on CEUS. In quantitative CEUS parameters, peak intensity (PI) was most valuable for predicting capsule invasion, and significant differences were found between the non-capsule invasion group (12.7±1.2) and the single capsule invasion group (29.2±5.2), and the non-capsule invasion group (12.7±1.2) and the ECE group (35.5±6.4). Moreover, the quantitative CEUS-assisted US equation demonstrated the best diagnostic performance compared with the conventional US and qualitative CEUS approach. Conclusions:Compared with conventional US and qualitative CEUS, quantitative CEUS is a more reliable and objective imaging method to predict capsule invasion of PTC.

Background : Hepatocellular carcinoma (HCC) remains a therapeutic challenge due to high post-resection recurrence rates and heterogeneous outcomes. We developed and validated a digital pathology-based prognostic model combining pathomics signatures with clinical parameters to predict recurrence and elucidate biological mechanisms. Methods : In this multicenter retrospective study, 294 HCC patients (training set: n=198; validation set: n=96) undergoing curative hepatectomy were analyzed. Pathomics features were quantitatively extracted from H&E-stained whole-slide images. Predictive modeling incorporated machine learning approaches (DT, KNN, LASSO, NB, RF, SVM) with clinical variables. Model performance was evaluated through ROC analysis, calibration, and decision curve analysis. Biological interpretation leveraged TCGA transcriptomic data analyzed via GSEA and WGCNA. Results : Tumor and peri-tumor pathomics parameters showed some complementarity in the prediction of HCC recurrence. The combined LASSO-based model showed the best predictive efficacy, with AUCs of 0.850 and 0.807 in the training and validation sets, respectively. The integrated pathomics-clinical model achieved AUCs of 0.893 and 0.860 in training and validation sets. Bioinformatics analysis suggested that the pathomics was correlated with the tumor immune microenvironment, as verified by multiple immunofluorescence staining of the validation set. Conclusion : This study establishes a robust digital pathology framework that not only improves HCC recurrence prediction beyond conventional biomarkers but also provides mechanistic insights into tumor-immune crosstalk.

BACKGROUND AND AIMS:Hepatocellular carcinoma (HCC) is a malignant tumor with a poor prognosis and is characterized by severe intratumoral heterogeneity. Identifying key genomic features and more reliable classifications is helpful for clinical management. METHODS:Cancer essential genes (CEGs) were identified using genome-scale CRISPR-Cas9 and univariate Cox regression analyses. Based on gene expression, nonnegative matrix factorization (NMF) was used to generate distinct molecular subtypes. The nearest template prediction (NTP) algorithm was used to validate the accuracy and robust classifications among three independent cohorts, including GSE14520, GSE54236, and ICGC-LIRI. Specifically, potential biomarkers were screened for clinical transformation based on their prognostic characteristics and biological function features. EdU, colony formation, and Transwell assays were utilized to confirm the effect of biomarkers in vitro. RESULTS:The C1 subtype had the worst prognosis and was characterized by advanced AJCC stages and high genomic instability. The NTP approach confirmed that the molecular subtypes were practical, robust, and reproducible. We further identified NDC80 as a gene specifically expressed in C1 subtype, indicative of prognosis solely for this subtype. Based on overrepresentation analysis (ORA), it was found that the biological function of NDC80 was mainly enriched in proliferation. In vitro cellular assays verified that promoted tumor growth and migration. CONCLUSIONS:Our study identified three robust molecular subtypes and revealed tumor heterogeneity. Meanwhile, the potential biomarker NDC80 served as a characteristic gene of the C1 subtype, correlating with poor prognosis and promoting tumor growth and migration, providing new insights for prognostic treatment strategies in HCC.

Cancer-associated fibroblasts (CAFs) exert multiple tumor-promoting functions and are key contributors to drug resistance. The mechanisms by which specific subsets of CAFs facilitate oxaliplatin resistance in colorectal cancer (CRC) have not been fully explored. This study found that THBS2 is positively associated with CAF activation, epithelial-mesenchymal transition (EMT), and chemoresistance at the pan-cancer level. Together with single-cell RNA sequencing and spatial transcriptomics analyses, we identified THBS2 specifically derived from subsets of CAFs, termed THBS2 + CAFs, which could promote oxaliplatin resistance by interacting with malignant cells via the collagen pathway in CRC. Mechanistically, COL8A1 specifically secreted from THBS2 + CAFs directly interacts with the ITGB1 receptor on resistant malignant cells, activating the PI3K-AKT signaling pathway and promoting EMT, ultimately leading to oxaliplatin resistance in CRC. Moreover, elevated COL8A1 promotes EMT and contributes to CRC oxaliplatin resistance, which can be mitigated by ITGB1 knockdown or AKT inhibitor. Collectively, these results highlight the crucial role of THBS2 + CAFs in promoting oxaliplatin resistance of CRC by activating EMT and provide a rationale for a novel strategy to overcome oxaliplatin resistance in CRC.

BACKGROUND:With fatal malignant peculiarities and poor survival rate, outcomes of pancreatic adenocarcinoma (PAAD) were frustrated by non-response and even resistance to therapy due to heterogeneity across clinical patients. Nevertheless, pharmacogenomics has been developed for individualized-treatment and still maintains obscure in PAAD. METHODS:A total of 964 samples from 10 independent multi-center cohorts were enrolled in our study. With drug response data from the profiling of relative inhibition simultaneously in mixtures (PRISM) and genomics of drug sensitivity in cancer (GDSC) databases, we established and validated multidimensionally three pharmacogenomics-classified subtypes using non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms, separately. The heterogenous biological characteristics and precision medicine strategies among subtypes were further investigated. RESULTS:Three pharmacogenomics-classified subtypes after stable and reproducible validation, distinguished in six aspects of prognosis, biological peculiarities, immune landscapes, genomic variations, immunotherapy and individualized management strategies. Subtype 2 was close to immunocompetent phenotype and projected to immunotherapy; Subtype 3 held most favorable outcomes and metabolic pathways distinctively, promising to be treated with first-line agents. Subtype 1 with worst prognosis, was anticipated to chromosome instability (CIN) phenotype and resistant to chemotherapeutic agents. In addition, ITGB6 contributed to subtype 1 resistance to 5-fluorouracil, and knockdown of ITGB6 enhanced sensitivity to 5-fluorouracil in in vitro experiments. Ultimately, appropriate clinical stratified treatments were assigned to corresponding subtypes according to pharmacogenomic transcripts. Some limitations were not taken into account, thus needs to be supported by more research. CONCLUSION:A span-new molecular subtype exploited for PAAD uncovered an insight into precise medication on ground of pharmacogenomics, and highly refined multiple clinical management strategies for specific patients.

Hypoxia in the tumor microenvironment promotes lymphatic metastasis, yet the role of cancer-associated fibroblasts (CAFs) in this process remains insufficiently elucidated in colorectal cancer (CRC). In this study, we developed a large language model-based cellular hypoxia-predicting classifier to identify hypoxic CAFs (HCAFs) at single-cell resolution. Our findings revealed that HCAFs enhance CRC lymphatic metastasis by secreting CLEC11A, a protein that binds to the LGR5 receptor on tumor cells, subsequently activating the WNT/β-catenin signaling pathway. This promotes epithelial-mesenchymal transition and lymphangiogenesis, facilitating the spread of tumor cells via the lymphatic system. Furthermore, we demonstrate that the hypoxia-induced transcription factor HIF1A regulates the conversion of normoxic CAFs to HCAFs, driving CLEC11A expression and promoting metastasis. In vivo and vitro experiments confirmed the pro-metastatic role of CLEC11A in CRC, with its inhibition reducing lymphatic metastasis. This effect was markedly reversed by targeting the LGR5 receptor on tumor cells or inhibiting the WNT/β-catenin pathway, further elucidating the underlying mechanisms of CLEC11A-driven metastasis. These findings underscore the potential of targeting the CLEC11A-LGR5 axis to prevent lymphatic dissemination in CRC. Our study highlights the role of HCAFs in CRC progression and reveals mechanisms of lymphatic metastasis for intervention.

Local invasion is considered a premonitor of tumor metastasis which cause curative difficulties and undesired prognosis in patients with Pancreatic ductal adenocarcinoma (PDAC). The importance of mRNA N6-methyladenosine (m6A) modification during tumor invasion is controversial as it plays distinct roles which mainly attributed to different m6A reader proteins exert function. In current study, the level of m6A expression in PDAC was analyzed by IHC and ELISA, all m6A-regulated genes in PDAC detected by qPCR. The downstream gene EMP1 was screened by analyzing IGF2BP3 knockdown RNA-seq, IGF2BP3-RIP, MeRIP-seq, and PDAC-survival related genes. And m6A modification sites of EMP1 RNA was verified by MeRIP-qPCR, RIP-qPCR, and dual luciferase assays. EMP1-binding protein VASP was identified by mass spectrometry. Cell migration and invasive activity were detected using cytoskeletal staining, scratch assay, transwell assay, subcutaneous tumor and lung metastasis models. Finally, prognosis and immune microenvironment was analyzed in PDAC by IHC and multiple immunofluorescence staining. This work shows that m6A reader IGF2BP3 is remarkably upregulated in local invasion PDAC and indicates worse prognosis of patients. Mechanistically, IGF2BP3 recognized m6A-modified EMP1 mRNAs to prolong stability of them, which inhibits the hindrance of SMAD7 to SMAD3/4 phosphorylation by promoting the binding of VASP and SMAD7. Finally, a tight correlation of the local invasion/IGF2BP3/EMP1 and infiltration of immune cells in the tumor microenvironment is evidenced in clinical PDAC. In conclusions, IGF2BP3 functions as an invasion driver that induces PDAC development via the EMP1/TGF-β axis. And IGF2BP3/EMP1 axis may be involved in regulating microenvironmental remodeling in pancreatic cancer.

IntroductionCampylobacter jejuni (C. jejuni), a commensal food-borne pathogen, poses severe threat to human health and poultry industry. N6-methyladenosine (m6A) mRNA modification is associated with innate immunity. However, the mechanism of m6A modification in C. jejuni chicken cecum inoculation remains unclear.MethodsHere, we characterized the cecal m6A modification landscape of chicken in the C. jejuni-resistant (R) and susceptible (S) groups using methylated RNA immunoprecipitation sequencing and RNA sequencing (RNA-seq), and further conducted the in vitro C. jejuni inflammatory model based on chicken macrophage-like cell line (HD11) to elucidate the specific mechanism.ResultsIn the S group, the level of proinflammatory cytokines (IL-8, IL-1β, IL-18, TNF-α, IL-17A) and global RNA methylation were significantly decreased (P < 0.05). A total of 30,427 and 30,367 m6A peaks were identified in R and S groups, which were primarily located in 3'UTR and CDS regions. Among these, 514 differential m6A peaks (270 hypermethylated peaks and 244 hypomethylated peaks) were identified, which mainly correlated with the regulation of canonical NF-kappaB signal transduction, apoptotic signaling pathway, and MyD88-dependent toll-like receptor signaling pathway. Moreover, we identified 365 differentially expressed genes (DEGs), which were mainly associated with regulation of autophagy, and toll-like receptor 9 signaling pathway, intraciliary transport involved in cilium assembly, positive regulation of mTOR signaling, defense response to bacteria. The correlation analysis revealed that m6A methylation level correlated positively with gene expression. Further analysis identified 58 differentially methylated genes (DMGs), and mainly involved in apoptosis, autophagy, Notch signaling pathway and defense response to bacteria, which mainly enriched by DMGs including IFT74, SUSD5, WDR41, STAB2, EPG5 and FOS. Furthermore, we found that YTHDC2 could involve in regulating the apoptosis and autophagy process of HD11 cells through altering the expression of DMGs including IFT74, SUSD5, STAB2, EPG5 and FOS, which was confirmed by experiments in vitro.ConclusionThis result suggested the regulatory role of m6A methylation in chicken responds to C. jejuni inoculation. Collectively, the current study characterized the m6A modification landscape of chicken cecum and identified YTHDC2 acting key regulator responsible for C. jejuni inoculation.

PURPOSE:Molecular subtype of hepatocellular carcinoma (HCC) is primarily identified via high throughput expression profiles, largely ignoring the dynamic changes of gene expressions. Yet, biological networks remain steadily characterize disease state irrespective of time and conditions. We aim to utilize a metabolic genes interaction perturbation network-based approach to facilitate the subtyping and precision treatment of HCC patients. METHODS:We employed the metabolic genes interaction perturbation network-based approach to identify metabolic reprogramming (MR) subtypes in 922 HCC samples from four independent public datasets and further investigated their clinical and biofunctional implications, immune landscape, multi-omics features and biomarker. RESULTS:We stratified patients into three unique MR subtypes: (i) MR1 ("immune-deficiency"), frequent CTNNB1 mutation, and moderate prognosis; (ii) MR2 ("immune-activated"), advanced pathological staging and histological grading, frequent TP53 mutation, response to anti-PD-1 therapy, and the worst prognosis; (iii) MR3 (high metabolic activity), low-grade pathological staging and histological grading, fewer mutations and copy number variations, and the best prognosis. Besides, CD24 was identified and validated as a biomarker for MR2 which indicated a poor prognosis with higher expression. CONCLUSION:Taken together, the interactome taxonomy could effectively facilitate the stratified management and precise treatment of heterogeneous HCC patients.

The aim of this study was to explore the qualitative and quantitative characteristics of PTC on contrast-enhanced ultrasound (CEUS) in predicting central cervical lymph node metastases (CLNM). This prospective study analyzed a dataset of grayscale US and CEUS images in 201 nodules with biopsy-confirmed PTC. Seven grayscale US features and five qualitative CEUS parameters were employed to develop the approaches. Four quantitative CEUS parameters of the time-intensity curve were obtained and compared between PTC and adjacent thyroid tissue (ATT), as well as within different PTC groups. The diagnostic performance of an equation with quantitative CEUS parameters were compared with grayscale US features and qualitative CEUS for predicting central CLNM. The patients were divided into three groups based on their final pathological results: 28 patients in the macro-metastases group, 95 patients in micro-metastasis group, 78 patients confirmed to have no metastases. Independent predictive grayscale US and qualitative CEUS features included size, capsule contact and heterogeneity on CEUS. In quantitative CEUS parameters, there were significant differences in peak intensity (PI) between PTC and ATT in all three groups (p < 0.05). Significant differences in PI were also observed among three groups (p < 0.05). A PI ratio of PTC and ATT greater than or equal to 1 was found to be a more sensitive index for predicting central CLNM. The quantitative CEUS-assisted US equation demonstrated the best diagnostic performance. A grayscale US and CEUS equation with PI ratio based on quantitative CEUS was developed for predicting occult central CLNM and it is considered highly valuable in the clinical management of PTC.

Solid cancer contains a complicated communication network between cancer cells and components in the tumor microenvironment (TME), significantly influencing the progression of cancer. Exosomes function as key carriers of signaling molecules in these communications, including the intricate signalings of tumor-associated macrophages (TAMs) on cancer cells and the TME. With their natural lipid bilayer structures and biological activity that relates to their original cell, exosomes have emerged as efficient carriers in studies on cancer therapy. Intrigued by the heterogeneity and plasticity of both macrophages and exosomes, we regard macrophage-derived exosomes in cancer as a double-edged sword. For instance, TAM-derived exosomes, educated by the TME, can promote resistance to cancer therapies, while macrophage-derived exosomes generated in vitro have shown favorable potential in cancer therapy. Here, we depict the reasons for the heterogeneity of TAM-derived exosomes, as well as the manifold roles of TAM-derived exosomes in cancer progression, metastasis, and resistance to cancer therapy. In particular, we emphasize the recent advancements of modified macrophage-derived exosomes in diverse cancer therapies, arguing that these modified exosomes are endowed with unique advantages by their macrophage origin. We outline the challenges in translating these scientific discoveries into clinical cancer therapy, aiming to provide patients with safe and effective treatments.

AbstractAimsIn an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early‐stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early‐stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression.Methods and resultsThree hundred eighty‐five samples from four independent cohorts were systematically retrieved. The weighted gene co‐expression network analysis, differential expression analysis (|log2(foldchange)| > 0.5 and adjusted P < 0.05), and protein–protein interaction network were sequentially performed to identify HCM‐related hub genes. With a machine learning algorithm, the least absolute shrinkage and selection operator regression algorithm, a stable diagnostic model was developed. The immune‐cell infiltration and biological functions of HCM were also explored to characterize its underlying pathogenic mechanisms and the immune signature. Two key modules were screened based on weighted gene co‐expression network analysis. Pathogenic mechanisms relevant to extracellular matrix and immune pathways have been discovered. Twenty‐seven co‐regulated genes were recognized as HCM‐related hub genes. Based on the least absolute shrinkage and selection operator algorithm, a stable HCM diagnostic model was constructed, which was further validated in the remaining three cohorts (n = 385). Considering the tight association between HCM and immune‐related functions, we assessed the infiltrating abundance of various immune cells and stromal cells based on the xCell algorithm, and certain immune cells were significantly different between high‐risk and low‐risk groups.ConclusionsOur study revealed a number of hub genes and novel pathways to provide potential targets for the treatment of HCM. A stable model was developed, providing an efficient tool for the diagnosis of HCM.