Solute carrier family 7 member 11 (SLC7A11) is a major transporter regulating cysteine metabolism and is widely expressed in a variety of tumor cells. SLC7A11 plays an important role in the occurrence, development, invasion and metastasis of tumors by regulating the transport of cysteine in the tumor microenvironment. SLC7A11 is expected to become a new therapeutic target and prognostic indicator for the individualized treatment of patients. According to relevant research reports, SLC7A11 can predict the stages and metastasis of liver, breast and lung cancer. Therefore, an in‑depth exploration of the role of SLC7A11 in tumors may be important for the screening, early diagnosis, treatment and prognosis of patients with tumors. The current review summarizes the research progress on SLC7A11 in liver cancer, lung cancer and other tumors on the basis of previous primary studies. In addition, the present review systematically elaborates on the three main aspects of SLC7A11 pathways in some tumors, the cancer‑promoting mechanisms, and the therapeutic relationship between SLC7A11 and tumors. Finally, the present review aims to provide a reference for assessing whether SLC7A11 can be used as a prognostic indicator and treatment target for tumor patients, and the future research direction with regard to SLC7A11 in tumors.

Asthma is a chronic inflammatory airway disease and the airway epithelium is involved in airway inflammation and innate immunity. However, whether circular RNA (circRNA) is involved in the pathogenesis of asthma remains unclear. The present study aimed to determine the functions and molecular mechanisms of circRNA targeting dehydrogenase E1 (circDHTKD1) in the inflammation response of human bronchial epithelial cells. BEAS‑2B cells were stimulated with lipopolysaccharide (LPS) to establish a model of in vitro airway inflammation. Cell viability was assessed using Cell Counting Kit‑8 assay. CircDHTKD1 was characterised by nucleocytoplasmic isolation and Sanger sequencing. The RNA expression levels of circDHTKD1, microRNA (miR)‑338‑3p and potential ERK pathway downstream genes were evaluated by reverse transcription‑quantitative polymerase chain reaction. Western blot analysis was performed to measure associated protein levels. The levels of inflammatory cytokines were detected by ELISA. The interaction between circDHTKD1 and miR‑338‑3p was confirmed by dual‑luciferase reporter assay. circDHTKD1 expression was significantly upregulated by LPS treatment, whereas miR‑338‑3p expression was decreased. Furthermore, circDHTKD1 directly targeted miR‑338‑3p, which negatively regulated expression of E26 transformation specific‑1 (ETS1). Inflammatory cytokine and ETS1 expression levels decreased following transfection with small interfering RNA targeting circDHTKD1 or miR‑338‑3p mimics. In addition, co‑transfection with miR‑338‑3p inhibitor reversed the effects caused by circDHTKD1 knockdown. The knockdown of ETS1 in LPS‑induced BEAS‑2B cells resulted in decreased cytokine production and inhibition of the ERK signalling pathway. Overall, these results suggested that the knockdown of circDHTKD1 alleviated the LPS‑induced production of inflammatory cytokines and activation of the ERK pathway in BEAS‑2B cells through the miR‑338‑3p/ETS1 axis. In summary, circDHTKD1 exacerbated LPS‑triggered inflammation responses in BEAS‑2B cells by regulating ETS1 expression by interacting with miR‑338‑3p, suggesting that circDHTKD1 may serve as a potential therapeutic target against asthma.

Background: Triple-negative breast cancer (TNBC), a highly heterogeneous breast cancer subtype, poses significant challenges to human health. Intra-tumor heterogeneity (ITH) limits the reliability of conventional prognostic models.Methods: Using multi-region RNA-seq, we quantified TNBC transcriptomic heterogeneity through an integrative heterogeneity score (IHS). After evaluating inter-patient heterogeneity (IPH) and ITH, prognostic and low-heterogeneity genes were identified and used to build a prognostic risk model with a random survival forest (RSF) algorithm. This model was combined with TNM staging into a nomogram for clinical applicability. We further revealed the distinct immune microenvironment features, somatic mutations, and chemotherapy responses between risk subgroups. Gene expression was validated via RT-qPCR.Results: Spatial characterization uncovered substantial ITH, evidenced by sharp shifts in PAM50 subtypes and immune infiltration. Two low-heterogeneity biomarkers, CYP4B1 and GBP1, were identified to develop a robust prognostic signature with consistent predictive performance across 3- to 9-year survival endpoints (AUC > 0.6). The high-risk subgroup exhibited reduced immune infiltration, reduced immune checkpoint molecule expression, and poor immunotherapy response rates. Integration of the risk signature with TNM staging created a clinically practical nomogram with superior predictive accuracy (C-index >0.67). Therapeutic vulnerability profiling identified six targeted agents showing increased efficacy in high-risk patients. Dysregulation of signature genes was demonstrated in two TNBC cell lines.Conclusions: This study established a transcriptomic heterogeneity-resilient prognostic model for TNBC, enabling precise survival stratification and immune microenvironment assessment. The integrative nomogram and risk-guided therapeutic predictions address clinical challenges in TNBC management, advancing personalized treatment strategies.

Background: Breast cancer (BC) remains a significant threat to human health, with substantial variations in prognosis and treatment responses. Intra-tumor heterogeneity (ITH) presents a critical challenge in developing reliable prognostic models.Methods: This study integrated multi-region RNA sequencing data from BC patients with the TCGA BC dataset. Genes resistant to sampling bias were identified by evaluating inter-patient heterogeneity (IPH) and ITH. A machine learning framework incorporating ten algorithms was used to construct a prognostic signature.The expression levels and oncogenic function of the prognostic genes were validated through RT-qPCR and in vitro experiments.Results: The signature, comprising CFL2 and SPNS2, demonstrated stable predictive performance in both training and validation cohorts (C-index > 0.6). High-risk patients exhibited enriched immune infiltration, particularly CD8+ T cells, and higher expression of immune checkpoint molecules, suggesting sensitivity to immunotherapy. A nomogram integrating risk score with clinical variables further improved prognostic accuracy. The dysregulation of signature genes was confirmed in BC cell lines.Conclusion: By minimizing ITH interference, this study developed a robust prognostic signature for BC, offering insights into the tumor immune microenvironment and potential therapeutic strategies.

Background: The prevalence of metabolic dysfunction–associated steatotic liver disease (MASLD) is increasing, and the disease is often asymptomatic in its early stages. Insulin resistance is central, but fasting-insulin–based indices are impractical for routine screening. The single-point insulin sensitivity estimator (SPISE) is simple and inexpensive, but its longitudinal association with MASLD and predictive utility remain unclear. Methods: A two-stage study was performed. Cross-sectional analyses examined SPISE and prevalent MASLD; longitudinal analyses among participants without MASLD at baseline assessed incident MASLD and developed prediction models. MASLD was defined as ultrasound-confirmed steatosis plus ≥1 cardiometabolic risk factor. Logistic regression estimated odds ratios (ORs) for prevalent MASLD; Hazard ratios (HRs) for incident MASLD were estimated using Cox proportional hazards models, with restricted cubic splines used to characterize non-linear exposure–response patterns. Improvement in prediction after adding SPISE was compared with TyG, METS-IR, and TG/HDL-C using NRI and IDI metrics. Discriminative ability and clinical net benefit were examined with time-dependent ROC analysis and decision curve analysis, respectively. Results: Higher SPISE was independently associated with lower odds of prevalent MASLD (fully adjusted OR = 0.43, 95% CI 0.38–0.48). Higher SPISE also predicted lower incident MASLD risk (fully adjusted HR = 0.49, 95% CI 0.46–0.52), with a significant nonlinear association (P for non-linearity < 0.001). Adding SPISE to the fully adjusted base model produced the largest improvements in reclassification and discrimination (NRI = 0.363; IDI = 0.093; both p < 0.001). A SPISE-based model incorporating liver enzymes, bilirubin, and blood pressure showed good discrimination at 12 and 24 months (AUC = 0.859 and 0.886, respectively) and favorable clinical net benefit. Conclusion: SPISE is independently and inversely associated with prevalent and incident MASLD and provides superior incremental predictive value versus common insulin resistance indices. External validation is warranted.

Background: As the core treatment for cervical cancer, chemoradiotherapy efficacy is often limited by tumor heterogeneity-driven resistance. During chemoradiotherapy, the tumor microenvironment undergoes dynamic remodeling, with residual malignant cells evolving adaptively. However, the key regulatory genes driving therapeutic resistance remain elusive at single-cell resolution, which hinders the development of novel targeted therapies. Methods: Single-cell RNA sequencing (scRNA-seq) was performed on paired clinical samples from three cervical cancer patients before and after chemoradiotherapy to identify the cellular changes and the molecular mechanisms induced by chemoradiotherapy. Through integrated multi-omics data analysis, non-heterogeneity-dependent genes with prognostic value were identified. Furthermore, the biological function of the candidate gene in chemoradiotherapy-resistant HeLa cells was verified through in vitro functional experiments, including the detection of cell proliferation, migration and invasion abilities. Results: Chemoradiotherapy significantly reprogrammed the cellular composition of the tumor microenvironment (TME) in cervical cancer. Residual malignant cells showed activation of angiogenesis, epithelial-mesenchymal transition, stress-response, and cell-cycle programs. Eight heterogeneity-filtered prognostic genes were identified, and phosphoglucomutase 2 (PGM2) was prioritized because it was upregulated in cervical cancer, enriched in malignant cells, associated with poor prognosis, and supported by independent transcriptomic, spatial, and immunohistochemical evidence. PGM2 knockdown reduced proliferation, migration, and invasion in resistant HeLa cells. Conclusion: PGM2 may contribute to the malignant phenotype of chemoradiotherapy-resistant cervical cancer cells and represents a candidate biomarker and therapeutic target. Further validation in larger clinical cohorts and laboratory work is needed before clinical translation.