Shenzhen Genoimmune Medical Institute

CD8 + T cells are central to effective antitumor immunity, yet in cancer, they often undergo progressive transcriptional, epigenetic, and metabolic reprogramming that leads to an exhausted state and limits current immunotherapy. A deeper understanding of the molecular mechanisms that govern CD8 + T cell differentiation and function within the tumor microenvironment is essential to overcome this barrier. This review outlines the current knowledge of the transcriptional and epigenetic programs that shape T cell heterogeneity in cancer and chronic infection, with a focus on the formation and maintenance of exhausted T cell subsets. We highlight how T cell–intrinsic factors such as transcription factors and chromatin regulators and extrinsic factors such as nutrient availability converge to influence T cell fate decisions and function, as well as how these are affected in cancer. Finally, we discuss emerging therapeutic strategies aimed at reprogramming the epigenome to restore T cell function, offering new avenues to enhance the efficacy and durability of cancer immunotherapy.

BackgroundThe Monopterus albus serves as a unique model for studying sex reversal, transitioning naturally from female to male. However, the origins of male germline stem cells (GSCs) and the roles of somatic cells during sex reversal remain poorly understood.MethodsWe performed single-cell RNA sequencing (scRNA-seq) on ovarian, ovotestis, and testicular tissues to construct a gonadal cell atlas. RNA fluorescence in situ hybridization (RNA-FISH) validated cell subpopulations.ResultsCell types of germ cells and somatic cells in gonads were identified, and their differentiation trajectories during sex reversal were depicted. Our results show that GSCswdr17+tep1− in the ovaries possess bipotential differentiation capacity and can transform into GSCswdr17+ tep1+ with the capability to differentiating into sperm during sex reversal. Two theca cell subpopulations cooperate to synthesize steroid precursors in ovaries, with Leydig cells in testes likely originating from theca cell transitions. Proliferative gdf9+ follicle cells promoted folliculogenesis and persisted in male gonads with reduced hsd17b1 expression. Stromal heterogen-eity analysis revealed bmp4+ mesenchymal stem cells (MSCsbmp4+) as potential precursors for Sertoli cells in testes.ConclusionsThis study provides a comprehensive cellular roadmap of gonadal cell dynamics during sex reversal in Monopterus albus. Our findings unveil the molecular mechanisms underlying germline and somatic cell fate determination, offering novel insights into vertebrate sex reversal and potential therapeutic strategies for disorders of sex development (DSD).Supplementary InformationThe online version contains supplementary material available at 10.1186/s13287-025-04810-8.

The effectiveness of diagnosis-related groups (DRG) system is pivotal to the implementation of medical insurance payment standards. However, existing methods for constructing DRGs face challenges such as violations of grouping rules and imbalances in multiobjective optimization, which limit their ability to support payment standards that accurately reflect clinical complexities. To address these challenges, this paper proposes a multiconstraint multiobjective optimization model and a tree-structured multiobjective optimization framework, grounded in interpretability theory. The model mathematically defines the dual objectives of enhancing intragroup homogeneity and intergroup heterogeneity, subject to grouping rules. The framework utilizes nonnegative adaptive LASSO regression to accurately quantify clinical complexity, while integrating tree structures with multiobjective optimization algorithms to generate Pareto-optimal DRG sets by solving the model. Empirical results demonstrate that the proposed framework satisfies the grouping constraints and effectively reflects clinical complexity. This framework is expected to provide a paradigm for constructing DRGs, offering decision-makers efficient DRG sets.

BackgroundOnline adaptive radiotherapy (OART) can address the reduction in radiation therapy precision caused by changes such as tumor shrinkage, rectal gas filling, and bladder distension. Currently, the determination of OART triggers during the fractions of radiotherapy requires clinicians to spend considerable effort and time on‐site for assessments.ObjectiveTo reduce the clinical workload while maintaining the precision of radiotherapy, this study aims to explore the feasibility of developing a triggered OART prediction model for postoperative prostate cancer by combining radiomics and dosimetric features using machine learning.MethodsA total of 244 fan‐beam computed tomography (FBCT) images from 30 postoperative prostate cancer patients who received image‐guided radiotherapy (IGRT) at least once per week at a single center were retrospectively collected. Two radiation oncologists (ROs) defined OART trigger labels based on morphological changes and dose differences between the current treatment fraction's FBCT and the planning CT for critical organs at risk (OARs). CT radiomics features and dosimetric features were calculated for five regions of interest (ROIs): clinical target volume (CTV), planning target volume (PTV), PTV minus CTV (PTV‐CTV), bladder, and rectum. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. Support vector machine (SVM) algorithms were employed to establish CT radiomics feature models (R models) and dosimetric feature models (D models), resulting in 10 OART trigger prediction models. Finally, we utilized model integration methods to create a comprehensive predictive model that integrated the 10 OART trigger prediction models, achieving optimal performance. Five‐fold cross‐validation was used for preliminary evaluation of the model performance, and further assessment was conducted on an independent test set.ResultsEight hundred fifty‐one CT radiomics features were extracted from each ROI. Additionally, 14 dosimetric features were calculated for the target‐related ROIs (CTV, PTV, and PTV‐CTV), and 9 dosimetric features were calculated for the OARs (bladder and rectum). Among the CT radiomics models, the PTV‐CTV‐R model had the highest AUC at 0.824; among the dosimetric models, the Bladder‐D model had the highest AUC at 0.85. The comprehensive predictive model for triggered OART (Comprehensive‐E model), which integrated all radiomics and dosimetric features, achieved an AUC of 0.893, significantly outperforming models that used only CT radiomics or dosimetric features.ConclusionThis study is the first to combine radiomics and dosimetric features using machine learning to establish a triggered OART prediction model, demonstrating the feasibility of using CT radiomics and dosimetric features for OART trigger prediction. By employing model integration techniques, we addressed the issue of one‐dimensional feature bias, significantly enhancing model performance. This research provides new data analysis methods for adaptive radiotherapy triggering in postoperative prostate cancer, offering substantial clinical value.

Multiple myeloma (MM) is a frequent haematological malignancy of the bone marrow. Carfilzomib, a first-line treatment for MM, has excellent antitumour effects, but its efficacy eventually decreases due to primary or acquired chemoresistance. Therefore, the regulatory mechanism of carfilzomib resistance has attracted much attention for improving the survival outcomes of patients with MM. By using database analysis combined with quantitative real-time polymerase chain reaction (qRT‒PCR), aberrant lncRNAs in MM were screened in carfilzomib-resistant cells versus carfilzomib-sensitive cells, and the resistance index of cultured carfilzomib-resistant cells was analysed compared to that of parental cells. Furthermore, cell viability, proliferation, and apoptosis in response to treatment with carfilzomib were measured after treatment with LINC00461 by the cell counting kit-8 (CCK-8) method and flow cytometry. The expression of LINC00461 was also verified through qRT‒PCR. Then, the possible miRNA molecules on which LINC00461 may act were investigated by RNA immunoprecipitation (RIP) and dual-luciferase reporter assays. Next, tumorigenesis in mice was evaluated to verify the effect of LINC00461 on carfilzomib-resistant cells. Increased expression of LINC00461 was related to drug resistance in MM patients. Mechanistically, LINC00461 overexpression attenuated the effect of miR-539-3p overexpression and decreased the expression of the downstream protein RAB5A. Moreover, compared with the control group, the LINC00461 knockdown group treated with carfilzomib exhibited decreases in tumour volume and weight. Furthermore, LINC00461 sensitized carfilzomib-sensitive cells by promoting the release of exosomes. These data suggest that LINC00461 plays an important role in the development of carfilzomib resistance.

Background: Vitamin B12 (VB12) is an essential biomarker for population health, and its deficiency status leads to severe health burdens. A comprehensive and updated investigation on the distribution of VB12 levels and status is critically needed to address public health and clinical concerns. Methods: We conducted a cross-sectional analysis using data obtained from the Precision Medical Program, which encompassed 14 provinces in China. Specific criteria based on previously published cutoffs and our study population’s characteristics are adopted to define B12 deficiency. Biochemical B12 deficiency was identified with circulating VB12 levels <148 pmol/L. Metabolic B12 deficiency was determined with circulating VB12 levels ≥148 and ≤258 pmol/L with concomitant elevated plasma total homocysteine (tHcy ≥14 µmol/L). Normal B12 status included VB12 levels >258 pmol/L or VB12 levels ranging from 148 to 258 pmol/L with tHcy levels below 14 µmol/L. VB12 levels and status were analyzed and stratified by age, sex, and geographical region. Results: This study enrolled a total of 2174 hypertensive adults, with a mean age of 63.0 years (±13.4) and 45.8% female. The overall mean VB12 levels were 322.3 (85.0) pmol/L. The prevalence rates of B12 biochemical deficiency, metabolic deficiency, metabolic deficiency with excluded renal dysfunction, and adequate status were 0.7%, 13.3%, 11.5%, and 85.9%, respectively. Females exhibited higher VB12 levels than males, with a β value of 19.72 (95% confidence interval [CI], 10.59–28.84) in the multivariate model. The southern population demonstrated higher VB12 levels than people from the north, with a β value of −18.14 (95% CI, −25.72 to −10.76). Males had a higher prevalence of metabolic VB12 deficiency in comparison to females (15.8% vs. 10.4%). Conclusions: Our study revealed that Chinese hypertensive adults exhibit relatively higher levels of VB12. Additionally, while biochemical B12 deficiency is uncommon, a noteworthy proportion of individuals were affected by metabolic VB12 deficiency. This study has important implications for nutritional counseling and vitamin B supplement strategies.

BackgroundLittle is known about the relationship between sleep quality and lung cancer incidence. Thus, this study was conducted to investigate the potential connection between sleep quality and lung cancer incidence.MethodsWe performed and selected a nested case–control study that included 150 lung cancer cases and 150 matched controls based on the Lianyungang cohort. Univariate and multivariate logistic regression was utilized to investigate the connection between potential risk factors and lung cancer incidence risk.ResultsIn this study, the average age of participants was 66.5 ± 9.1 years, with 58.7% being male, and 52.7% reportedly experiencing sleep quality problems. The results of multivariate logistic regression showed that poor sleep quality was connected to an increased lung cancer incidence risk (P = 0.033, odds ratio = 1.83, 95% confidence interval = [1.05–3.19]) compared with those with good sleep quality. The stratified analyses showed a significantly positive connection between poor sleep quality (vs. good sleep quality) and cancer risk in smokers (vs. non-smoker, P for interaction = 0.085). The combined effect analysis indicated that smokers with poor sleep quality suffered from a 2.79-fold increase in cancer incidence rates when compared with non-smokers with good sleep quality.ConclusionsPoor sleep quality was positively connected to an increased lung cancer incidence risk. In addition, among those individuals with poor sleep quality, smoking increased the lung cancer incidence risk.