Cuproptosis-related gene signatures for predicting prognosis of lung adenocarcinoma

Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes

BACKGROUND

Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals.

METHODS

This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified.

RESULTS

The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan.

CONCLUSION

Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

Resveratrol improves meat quality traits by activating the lncRNAs-KEAP1-NRF2 axis in pigs

This research aims at uncovering the effects and investigating the molecular mechanisms of dietary resveratrol (RES) supplementation on antioxidant capacity and meat quality of pigs. In this study, 20 μM RES could activate the KEAP1-NRF2 antioxidant defense pathway in response to oxidative stress in porcine skeletal muscle satellite cells was firstly found. Then, twenty-four healthy crossbred castrated boars were allocated to 4 treatments that were fed with a basal diet (control) and a basal diet supplemented with 200 mg, 400 mg or 600 mg RES per Kilogram (kg) of feed for 41 days, respectively. 400 and 600 mg/kg RES-supplemented diet can effectively improve the meat quality traits and activities of antioxidizing enzymes via the KEAP1-NRF2 signaling pathway of pigs. The molecular dynamic simulation further revealed that RES could directly binding to KEAP1 to reduce the tightness of KEAP1-NRF2 protein-protein interaction. More importantly, dietary supplementation of RES also improves antioxidant capacity through a series of KEAP1-NRF2 pathway-related lncRNAs were found by RNA sequencing (RNA-seq). Altogether, this study demonstrated that RES improves meat quality traits by effectively increasing antioxidant levels via the lncRNA-KEAP1-NRF2 axis in vivo and/or in vitro. These results provide new insights into the molecular mechanisms by which RES, as a nutritional agent, regulates antioxidant capacity and improves meat quality in pigs.

Crosstalk between copper homeostasis and cuproptosis reveals a lncRNA signature to prognosis prediction, immunotherapy personalization, and agent selection for patients with lung adenocarcinoma

BACKGROUND

Copper homeostasis and cuproptosis play critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remain to be fully elucidated. We aimed to adopt these concepts to create and validate a lncRNA signature for LUAD prognostic prediction.

METHODS

For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Copper homeostasis and cuproptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature CoCuLncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, and nomogram predictor analysis in our validation process. We also compared CoCuLncSig with previous studies. We performed analyses using R software to evaluate CoCuLncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of CoCuLncSig lncRNAs, and the ability of CoCuLncSig in pan-cancer was also assessed.

RESULTS

CoCuLncSig containing eight lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, CoCuLncSig had more prognostic ability advantages. CoCuLncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high CoCuLncSig score population, we found 16 drug candidates, among which epothilone-b and gemcitabine may have the most potential. The pan-cancer analysis found that CoCuLncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the CoCuLncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic CoCuLncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.

Construction and validation of a cuproptosis-related lncRNA signature for the prediction of the prognosis of LUAD and LUSC

Lung cancer is one of the most prevalent malignant tumors worldwide, with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) accounting for the majority of cases. Cuproptosis, tumor immune microenvironment (TIME) and long non-coding RNA (lncRNA) have been demonstrated to be associated with tumorigenesis. The objective of the present study was to develop a novel cuproptosis-related lncRNA signature to assess the association between cuproptosis and TIME in patients with LUAD or LUSC and to predict prognosis. Based on the outputs of the least absolute shrinkage and selection operator regression model, a cuproptosis-related lncRNA signature was developed. Kaplan-Meier survival curves were generated to confirm the predictive ability of the signature. Univariate and multivariate analysis was also performed to determine the association between overall survival and this signature and other clinical characteristics, and a nomogram was created. Additionally, the relationship between the signature, TIME, tumor mutation burden and m6A methylation was established. The results of the present study revealed that 8 cuproptosis-related lncRNAs were associated with the prognosis of patients with LUAD and LUSC. This novel cuproptosis-related lncRNA signature is associated with TIME and m6A methylation in LUAD and LUSC and can predict prognosis with accuracy.