Construction of an immune-related lncRNA pairs model to predict prognosis and immune landscape of lung adenocarcinoma patients

Prognostic models for immunotherapy in non-small cell lung cancer: A comprehensive review

The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.

Prognostic value and immune landscapes of anoikis-associated lncRNAs in lung adenocarcinoma

BACKGROUND

Methods for predicting the outcome of lung adenocarcinoma (LUAD) in the clinic are limited. Anoikis is an important route to programmed cell death in LUAD, and the prognostic value of a model constructed with anoikis-related lncRNAs (ARlncRNAs) in LUAD is unclear.

METHODS

Transcriptome and basic information for LUAD patients was obtained from the Cancer Genome Atlas. Coexpression and Cox regression analyses were utilized to identify prognostically significant ARlncRNAs and construct a prognostic signature. Furthermore, the signature was combined with clinical characteristics to create a nomogram. Finally, we performed principal component, enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) and drug sensitivity analyses to evaluate the basic research and clinical merit of the signature.

RESULTS

The prognostic signature developed with eleven ARlncRNAs can accurately predict that high-risk group patients have a worse prognosis, as proven by the receiver operating characteristic (ROC) curve (AUC: 0.718). Independent prognostic analyses indicated that the risk score is a significant independent prognostic element for LUAD (P<0.001). In the high-risk group, enrichment analysis demonstrated that glucose metabolism and DNA replication were the main enrichment pathways. TMB analysis indicated that the high-risk group had a high TMB (P<0.05). Drug sensitivity analyses can recognize drugs that are sensitive to different risk groups. Finally, 11 ARlncRNAs of this signature were verified by RT-qPCR analysis.

CONCLUSIONS

A novel prognostic signature developed with 11 ARlncRNAs can accurately predict the OS of LUAD patients and offer clinical guidance value for immunotherapy and chemotherapy treatment.

Comprehensive landscape and future perspective of long noncoding RNAs in non-small cell lung cancer: it takes a village

Long noncoding RNAs (lncRNAs) are a distinct subtype of RNA that lack protein-coding capacity but exert significant influence on various cellular processes. In non-small cell lung cancer (NSCLC), dysregulated lncRNAs act as either oncogenes or tumor suppressors, contributing to tumorigenesis and tumor progression. LncRNAs directly modulate gene expression, act as competitive endogenous RNAs by interacting with microRNAs or proteins, and associate with RNA binding proteins. Moreover, lncRNAs can reshape the tumor immune microenvironment and influence cellular metabolism, cancer cell stemness, and angiogenesis by engaging various signaling pathways. Notably, lncRNAs have shown great potential as diagnostic or prognostic biomarkers in liquid biopsies and therapeutic strategies for NSCLC. This comprehensive review elucidates the significant roles and diverse mechanisms of lncRNAs in NSCLC. Furthermore, we provide insights into the clinical relevance, current research progress, limitations, innovative research approaches, and future perspectives for targeting lncRNAs in NSCLC. By summarizing the existing knowledge and advancements, we aim to enhance the understanding of the pivotal roles played by lncRNAs in NSCLC and stimulate further research in this field. Ultimately, unraveling the complex network of lncRNA-mediated regulatory mechanisms in NSCLC could potentially lead to the development of novel diagnostic tools and therapeutic strategies.

LMNTD2-AS1 regulates immune cell infiltration and promotes prostate cancer progression by targeting FUS to regulate NRF2 signal pathway

Numerous studies have demonstrated that long non-coding RNAs (lncRNAs) play crucial roles in tumor progression. This study aimed to identify lncRNAs associated with overall survival (OS) and progression-free interval (PFI) in prostate cancer (PCa) patients and to elucidate the driving mechanisms and functions of these lncRNAs. We utilized the TCGA database to screen for lncRNAs linked with OS and PFI. KM survival analysis, ROC curve analysis, and Cox survival analysis were employed to assess the prognostic significance of lncRNAs in PCa patients. We conducted a loss-of-function assay to explore the role of lncRNAs in PCa. Correlation analysis was performed to study the relationship between lncRNAs and immune cell infiltration. Lasso regression analysis was performed to screen proteins which might interact with lncRNAs, while rescue experiments verified the integrity of the signaling pathway. LMNTD2-AS1 was found to be the only lncRNA in PCa patients associated with both OS and PFI with significantly elevated levels in PCa. Elevated LMNTD2-AS1 expression was significantly linked to advanced stage, grade, primary treatment outcomes, residual tumors, and Gleason scores in PCa patients. Moreover, multivariate Cox regression analysis revealed that high LMNTD2-AS1 expression independently predicted PFI in PCa patients. The AUC of LMNTD2-AS1 for predicting 3-year OS and 5-year OS in PCa patients was 0.877 and 0.807, respectively, while for 3-year PFI and 5-year PFI it was 0.751 and 0.727, respectively. Overexpression of LMNTD2-AS1 correlated with infiltration of neutrophils, macrophages, pDC, NK CD56bright cells, and other immune cells. Furthermore, FUS and NRF2 are both potential binding proteins and related signaling pathways downstream of LMNTD2-AS1. Functional experiments demonstrated that LMNTD2-AS1 knockdown significantly inhibited migration, invasion, and proliferation of PCa cells while overexpression of FUS was found to rescue the functional inhibition caused by LMNTD2-AS1 knockdown. LMNTD2-AS1 functions as an oncogene in PCa, influencing patient prognosis and the immune microenvironment; it may regulate immune cell infiltration and promote PCa progression by interacting with the NRF2 signaling pathway via FUS binding.

A risk model based on autophagy-related lncRNAs for predicting prognosis and efficacy of immunotherapy and chemotherapy in gastric cancer patients

Long non-coding RNAs (lncRNAs) are a class of non-protein-coding RNAs essential to the occurrence and development of gastric cancer (GC). We aimed to identify critical lncRNA pairs to construct a prognostic model and assess its performances in prognosis and efficacy prediction in GC patients receiving immunotherapy and chemotherapy. We searched transcriptome and clinical data of GC patients from The Cancer Genome Atlas (TCGA) database. Autophagy-related lncRNAs were identified using co-expression network analysis, and lncRNA pairs with prognostic value were selected using pairwise transcriptome analysis. The gene pairs were subjected to LASSO algorithm for identification of optimal gene pairs for risk model construction. Patients were classified into the low-risk and high-risk groups with the RiskScore as a cutoff. Finally, 9 optimal gene pairs were identified in the LASSO algorithm model for construction of a lncRNA prognostic risk model. For predictive performances, it successfully predicted a shorter survival of high-risk patients than that obtained in low-risk individuals (P < 0.001). It showed moderate AUC (area under the curve) values for 1-, 2-, and 3-year overall survival prediction of 0.713 and could serve as an independent predictor for GC prognosis. Compared to the low-risk group, high-risk patients had higher expressions of marker genes for immune checkpoint inhibitors (ICIs) and showed higher sensitivity to the chemotherapy agents, rapamycin, bexarotene, and bicalutamide. Our findings demonstrate a robust prognostic model based on nine autophagy-related lncRNA pairs for GC. It acts as an independent predictor for survival and efficacy prediction of immunotherapy and chemotherapy in GC patients.