A novel algorithm for lung adenocarcinoma based on N6 methyladenosine-related immune long noncoding RNAs as a reliable biomarker for predicting survival outcomes and selecting sensitive anti-tumor therapies

The role of m6A methylation in targeted therapy resistance in lung cancer

Targeted therapies have greatly improved clinical outcomes for patients with lung cancer (LC), but acquired drug resistance and disease relapse inevitably occur. Increasingly, the role of epigenetic mechanisms in driving acquired drug resistance is appreciated. In particular, N6-methyladenosine (m6A), one of the most prevalent RNA modifications, has several roles regulating RNA stability, splicing, transcription, translation, and destruction. Numerous studies have demonstrated that m6A RNA methylation can modulate the growth and invasion of cancer cells as well as contribute to targeted therapy resistance in LC. In this study, we outline what is known regarding the function of m6A in the acquisition of targeted therapy resistance in LC.

RNA epigenetics in pulmonary diseases: Insights into methylation modification of lncRNAs in lung cancer

Long non-coding RNAs (lncRNAs) are pivotal controllers of gene expression through epigenetic mechanisms, Methylation, a prominent area of study in epigenetics, significantly impacts cellular processes. Various RNA base methylations, including m6A, m5C, m1A, and 2'-O-methylation, profoundly influence lncRNA folding, interactions, and stability, thereby shaping their functionality. LncRNAs and methylation significantly contribute to tumor development, especially in lung cancer. Their roles encompass cell differentiation, proliferation, the generation of cancer stem cells, and modulation of immune responses. Recent studies have suggested that dysregulation of lncRNA methylation can contribute to lung cancer development. Furthermore, methylation modifications of lncRNAs hold potential for clinical application in lung cancer. Dysregulated lncRNA methylation can promote lung cancer progression and may offer insights into potential biomarker or therapeutic target. This review summarizes the current knowledge of lncRNA methylation in lung cancer and its implications for RNA epigenetics and pulmonary diseases.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer

Lung cancer is the common malignant tumor with the highest mortality rate. Lung cancer patients have achieved benefits from immunotherapy, including immune checkpoint inhibitors (ICIs) therapy. Unfortunately, cancer patients acquire adaptive immune resistance, leading to poor prognosis. Tumor microenvironment (TME) has been demonstrated to play a critical role in participating in acquired adaptive immune resistance. TME is associated with molecular heterogeneity of immunotherapy efficacy in lung cancer. In this article, we discuss how immune cell types of TME are correlated with immunotherapy in lung cancer. Moreover, we describe the efficacy of immunotherapy in driven gene mutations in lung cancer, including KRAS, TP53, EGFR, ALK, ROS1, KEAP1, ZFHX3, PTCH1, PAK7, UBE3A, TNF-α, NOTCH, LRP1B, FBXW7, and STK11. We also emphasize that modulation of immune cell types of TME could be a promising strategy for improving adaptive immune resistance in lung cancer.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.