LncRNA LINC01833 is a Prognostic Biomarker and Correlates with Immune Infiltrates in Patients with Lung Adenocarcinoma by Integrated Bioinformatics Analysis

Non-coding RNAs and exosomal non-coding RNAs in lung cancer: insights into their functions

Lung cancer is the second most common form of cancer worldwide Research points to the pivotal role of non-coding RNAs (ncRNAs) in controlling and managing the pathology by controlling essential pathways. ncRNAs have all been identified as being either up- or downregulated among individuals suffering from lung cancer thus hinting that they may play a role in either promoting or suppressing the spread of the disease. Several ncRNAs could be effective non-invasive biomarkers to diagnose or even serve as effective treatment options for those with lung cancer, and several molecules have emerged as potential targets of interest. Given that ncRNAs are contained in exosomes and are implicated in the development and progression of the malady. Herein, we have summarized the role of ncRNAs in lung cancer. Moreover, we highlight the role of exosomal ncRNAs in lung cancer.

Deciphering disulfidptosis: Uncovering a lncRNA-based signature for prognostic assessment, personalized immunotherapy, and therapeutic agent selection in lung adenocarcinoma patients

BACKGROUND

Disulfidptosis, a recently identified type of regulated cell death, plays critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remains to be fully elucidated. We aimed to adopt this concept to develop 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. Disulfidptosis 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 DISULncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, nomogram predictor analysis, and functional assays in our validation process. We also compared DISULncSig with previous studies. We performed analyses to evaluate DISULncSig'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 DISULncSig lncRNAs, and the ability of DISULncSig in pan-cancer was also assessed.

RESULTS

DISULncSig containing twelve lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, DISULncSig had more prognostic ability advantages. DISULncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high DISULncSig score population, we found ten drug candidates, among which epothilone-b may have the most potential. The pan-cancer analysis found that DISULncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the DISULncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic DISULncSig 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 of an EMT-related lncRNA prognostic signature for lung adenocarcinoma and functional verification of its hub gene LINC01615

BACKGROUND

The epithelial-mesenchymal transition (EMT) plays a vital role in the progression of lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) participate in the EMT process as an important regulatory factor and have the potential to serve as prognostic biomarkers. We aimed to construct a novel lncRNA prognostic signature for LUAD based on EMT-related lncRNAs, identify EMT-related hub lncRNA, and investigate its biological functions.

METHODS

RNA-seq data, clinical and survival information were obtained from The Cancer Genome Atlas database. The EMT-related lncRNA prognostic signature (EMTscore) was constructed using the Least Absolute Shrinkage and Selection Operator Cox regression analysis. The efficiency of EMTscore in predicting the prognosis of LUAD was evaluated through the area under the time-dependent receiver operating characteristic (ROC) curves. The hub lncRNA of the prognostic signature was selected using a co-expression network map, and its effects on cell proliferation and metastasis were explored by in vitro experiments.

RESULTS

We constructed a prognostic signature (EMTscore) containing 8 tumor-high expressed lncRNAs. The EMTscore performed well in predicting overall survival rates with AUC values of 0.708 at 5 years in the training set. EMTscore could independently predict the survival of LUAD, with HR = 4.011 (95% CI 2.430-6.622) in the multivariate Cox regression. Importantly, we identified LINC01615 as the hub lncRNA in the EMTscore and revealed that LINC01615 enhanced the proliferation, migration, and EMT of lung cancer cells.

CONCLUSIONS

A new EMT-related lncRNA prognostic signature named EMTscore was developed, and LINC01615 was identified as the hub lncRNA of EMTscore. The hub lncRNA LINC01615 had an oncogenic biological function in LUAD.

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.