Programmed cell death (PCD) plays an important role in the onset and progression of various cancers. The molecular events surrounding the occurrence of abnormally expressed long noncoding RNAs (lncRNAs) leading to colon cancer (CC) have become a focus. We comprehensively evaluated the roles of PCD-related lncRNAs in the clinical management of CC and their immune responses. Therefore, we screened 41 prognostic PCD-related lncRNAs in The Cancer Genome Atlas database using co-expression analysis and assigned patients to groups according to the results of cluster analysis. The immune response and functions of cluster 2 were substantially suppressed, which might explain the poor prognosis in this group. A prognostic model comprising eight PCD-related lncRNAs was developed, and its effectiveness was verified using an external database. High-and low-risk groups had different epigenetic modifications and changes in immune cell infiltration. Patients in the high-risk group were resistant to immunotherapy and various chemotherapeutic drugs. Studies in vitro and in vivo further confirmed a carcinogenic role of the lncRNA U62317.4. Our findings of the prognostic value of PCD-related lncRNAs revealed their important roles in immune response disorders, thus providing valuable insights into the clinical management and molecular mechanisms of CC.

Background: Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide, with death rates increasing by approximately 2–3% per year. The high mortality and poor prognosis of HCC are primarily due to inaccurate early diagnosis and lack of monitoring when liver transplantation is not feasible. Fatty acid (FA) metabolism is a critical metabolic pathway that provides energy and signaling factors in cancer, particularly in HCC, and promotes malignancy. Therefore, it is essential to explore specific FA metabolism-related diagnostic and prognostic signatures that can enable the effective early diagnosis and monitoring of HCC. Methods: In this study, we used genes associated with FA metabolism pathway and weighted gene co-expression network analysis (WGCNA) to establish a gene co-expression network and identify hub genes related to HCC (disease WGCNA) and FA clusters (cluster WGCNA). A diagnostic model was constructed using data downloaded from the Gene Expression Omnibus database (GSE25097), and a prognostic model was established using The Cancer Genome Atlas cohort, in which Univariate Cox regression analysis, multivariate Cox risk model, and LASSO Cox regression analysis were applied. The immune infiltration of HCC cells was evaluated using CIBERSORT. The function of the key SLC22A1 gene was experimentally verified in vitro and in vivo. Results: Twelve overlapping genes (CPEB3, ASPDH, DEPDC7, ETFDH, UGT2B7, GYS2, F11, ANXA10, CYP2C8, GLYATL1, C6, and SLC22A1) from disease and cluster WGCNA were identified as key genes and used in the construction of the diagnostic and prognostic models. The RF model had the highest area under the ROC curve (AUC) of 0.994 was identified as the most effective for distinguishing patients with HCC with different features. The top five important genes (C6, UGT2B7, SLC22A1, F11, and CYP2C8) from the RF model were selected as diagnostic genes for further analysis (ROC curves: AUC value = 0.986, 95% confidence interval [95% CI] = 0.967–0.999). Moreover, a risk score formula consisting of four genes (GYS2, F11, ANXA10 and SLC22A1) was established and its independent prognostic ability was further demonstrated (univariate Cox regression analysis: hazard ratio [HR] = 3.664%, 95% CI = 2.033–6.605, P < 0.001; multivariate Cox regression analysis: HR = 2.801%, 95% CI = 1.553–5.049, P < 0.001). Additionally, in vitro and in vivo experiments demonstrated that SLC22A1 inhibits HCC tumor development, suggesting it may be a potential therapeutic target for HCC. Conclusions: These findings indicate a considerable value of specific FA metabolism-related genes in the diagnostic and prognostic evaluation of HCC, which provide novel insights into the disease’s management, as well as has potential implications for personalized treatment strategies. However, further investigation of the effects of these model genes on HCC is required.

Wound healing is plagued by an inherent reactive oxygen species (ROS) paradox: elevated ROS are critical for antibacterial defense, yet excessive ROS levels impede inflammation resolution and tissue repair. Conventional single-component nanomaterials lack the capacity for bidirectional ROS regulation, while multi-drug combination strategies suffer from complex regulatory modalities, poor stability, and antimicrobial resistance issues-all of which limit clinical translation. To address these challenges, we develop a vanadium carbide (V₄C₃)-iodinene (Ine)@polyvinyl alcohol (PVA) smart microneedle system. Notably, V₄C₃ within the system exhibits intrinsic bifunctionality: it not only generates ROS under light irradiation to enable photodynamic antibacterial therapy but also scavenges excess ROS after irradiation. Further enhanced by Ine and PVA microneedles, which boost functional adaptability and biocompatibility, this system forms a therapeutic platform with excellent mechanical strength, adaptive wound conformity, and laser-controlled switchable ROS regulation. In vitro and in vivo experiments demonstrate its potent antibacterial activity, its ability to induce M2 macrophage polarization, and its ability to reduce pro-inflammatory cytokine expression, thereby alleviating inflammation and accelerating wound healing. By leveraging V₄C₃'s bifunctionality and combining it with microneedle delivery and laser regulation, the simplified yet intelligent wound-healing platform resolves the ROS paradox, avoids issues such as drug resistance, and holds great potential for clinical translation.