We aimed to characterize the expression patterns of glycolysis and hypoxia genes in colon cancers as well as their value in prognosis and immune microenvironment. Methods: The expression profiles were acquired from the Cancer Genome Atlas database. Enrichment of hypoxia and glycolysis gene sets in colon cancer was identified by gene set enrichment analysis. Then, a prognostic signature was built up after Cox regression analyses, and overall survival analysis validated the predictive ability. Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis and CIBERSORT algorithm. A nomogram model integrating clinical variables and the gene signature was established and assessed. Results: Altogether, 378 cancer and 39 control cases were enrolled. Three glycolysis gene sets and two hypoxia gene sets were enriched in colon cancer (P < 0.05). Five independent genes (ENO3, GPC1, P4HA1, SPAG4, and STC2) were significantly correlated with prognosis of colon cancer patients. Patients with higher risks had significantly better prognosis than those with lower risks (P = 0.002 and AUC = 0.750), which was also observed in the elderly, female and stage I-II subgroups (P < 0.05). In high-risk cases, proportion of NK cells resting increased (P < 0.05) while that of dendritic cells activated (P < 0.05), dendritic cells resting (P < 0.01) and monocytes (P < 0.01) decreased. Besides, expressions of 22 checkpoint genes were found abnormal in groups with different risks (P < 0.05). The predictive nomogram presented satisfactory performance with C-index of 0.771 (0.712-0.830). The area under ROC curve was 0.796 and 0.803 for 3-and 5-year survival prediction, respectively. Conclusion: A glycolysis and hypoxia combined gene signature was a promising method to evaluate the prognosis and immune infiltration of colon cancer patients, which may provide a new tool for cancer management.
BackgroundBreast cancer (BC) is the most common malignancy among women. Nicotinamide (NAM) metabolism regulates the development of multiple tumors. Herein, we sought to develop a NAM metabolism-related signature (NMRS) to make predictions of survival, tumor microenvironment (TME) and treatment efficacy in BC patients.MethodsTranscriptional profiles and clinical data from The Cancer Genome Atlas (TCGA) were analyzed. NAM metabolism-related genes (NMRGs) were retrieved from the Molecular Signatures Database. Consensus clustering was performed on the NMRGs and the differentially expressed genes between different clusters were identified. Univariate Cox, Lasso, and multivariate Cox regression analyses were sequentially conducted to develop the NAM metabolism-related signature (NMRS), which was then validated in the International Cancer Genome Consortium (ICGC) database and Gene Expression Omnibus (GEO) single-cell RNA-seq data. Further studies, such as gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, SubMap, and Immunophenoscore (IPS) algorithm, cancer-immunity cycle (CIC), tumor mutation burden (TMB), and drug sensitivity were performed to assess the TME and treatment response.ResultsWe identified a 6-gene NMRS that was significantly associated with BC prognosis as an independent indicator. We performed risk stratification according to the NMRS and the low-risk group showed preferable clinical outcomes (P < 0.001). A comprehensive nomogram was developed and showed excellent predictive value for prognosis. GSEA demonstrated that the low-risk group was predominantly enriched in immune-associated pathways, whereas the high-risk group was enriched in cancer-related pathways. The ESTIMATE and CIBERSORT algorithms revealed that the low-risk group had a higher abundance of anti-tumor immunocyte infiltration (P < 0.05). Results of Submap, IPS, CIC, TMB, and external immunotherapy cohort (iMvigor210) analyses showed that the low-risk group were indicative of better immunotherapy response (P < 0.05).ConclusionsThe novel signature offers a promising way to evaluate the prognosis and treatment efficacy in BC patients, which may facilitate clinical practice and management.
IntroductionIt is still unclear whether radiotherapy affects the long-term survival of breast cancer (BC) patients after immediate breast reconstruction (IBR). This study aims to evaluate the actual prognostic impact of radiotherapy on BC patients undergoing IBR, and to construct survival prediction models to predict the survival benefit of radiotherapy.MethodsData on eligible BC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk models were used to assess breast cause-specific death (BCSD) and non-breast cancer cause-specific death (NBCSD). Kaplan‐Meier curve, Cox risk regression model and forest map were used to evaluate and demonstrate overall survival (OS) and breast cancer-specific survival (BCSS). Survival prediction nomograms were used to predict OS and BCSS probabilities.ResultsA total of 22,218 patients were selected, 24.9% received radiotherapy and 75.1% were without radiotherapy. Competing risk models showed that whether BCSD or NBCSD, the cumulative long-term risk of death in the radiotherapy group was higher than that in the non-radiotherapy group. The Kaplan‐Meier curve showed that patients with different lymph node metastasis had different radiotherapy benefits. Multivariate stratified analysis showed that radiotherapy after autologous reconstruction was associated with poor BCSS in patients with stage N0, and radiotherapy after autologous reconstruction and combined reconstruction improved OS and BCSS in patients with stage N3. The C-indexes of nomogram (between 0.778 and 0.847) and calibration curves showed the good prediction ability of survival prediction model.ConclusionsRadiotherapy can improve OS and BCSS in N3 stage BC patients undergoing immediate autologous reconstruction after mastectomy. The practical nomograms can be used to predict OS and BCSS of patients with or without radiotherapy, which is helpful for individualized treatment.
This review introduces low-density lipoprotein (LDL) delivery systems, summarizes the research status and progress in targeted diagnosis and therapy, and looks forward to the clinical application of LDL as an effective delivery vehicle.
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