Immune Landscape and Classification in Lung Adenocarcinoma Based on a Novel Cell Cycle Checkpoints Related Signature for Predicting Prognosis and Therapeutic Response

Identification of endoplasmic reticulum stress-related signature characterizes the tumor microenvironment and predicts prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) remains one of the most lethal malignancies worldwide, with a high mortality rate and unfavorable prognosis. Endoplasmic reticulum (ER) stress is a key regulator of tumour growth, metastasis, and the response to chemotherapy, targeted therapies and immune response. It acts via responding to misfolded proteins and triggering abnormal activation of ER stress sensors and downstream signalling pathways. Notably, the expression patterns of ER-stress-related-genes (ERSRGs) are indicative of survival outcomes, especially in the context of immune infiltration. Through consensus clustering of prognosis-associated ERSRGs, we delineated two distinct LUAD subtypes: Cluster 1 and Cluster 2. Comprehensive analyses revealed significant disparities between these subtypes in terms of prognosis, immune cell infiltration, and tumor progression. Leveraging the robustness of LASSO regression and Multivariate stepwise regression, we constructed and validated an ER Stress-associated risk signature for LUAD. This signature underwent assessments for its prognostic value, correlation with clinical attributes, and interaction within the tumour immune microenvironment. By integrating this signature with multivariate cox analysis of distinct pathological stages, we devised an enhanced nomogram, validated through various statistical metrics, with an area under the curve for overall survival at 1, 3, and 5 years post-diagnosis being 0.79, 0.80, and 0.81, respectively. In conclusion, our findings introduce a composite signature of 11 pivotal ERSRGs, holding promise as a potent prognostic tool for LUAD, and offering insights for immunotherapeutic and targeted intervention strategies.

Serum cytokine levels and other associated factors as possible immunotherapeutic targets and prognostic indicators for lung cancer

Lung cancer is one of the most prevalent cancer types and the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) accounts for 80-85% of all cancer incidences. Lung cancer therapy and prognosis largely depend on the disease's degree at the diagnosis time. Cytokines are soluble polypeptides that contribute to cell-to-cell communication, acting paracrine or autocrine on neighboring or distant cells. Cytokines are essential for developing neoplastic growth, but they are also known to operate as biological inducers following cancer therapy. Early indications are that inflammatory cytokines such as IL-6 and IL-8 play a predictive role in lung cancer. Nevertheless, the biological significance of cytokine levels in lung cancer has not yet been investigated. This review aimed to assess the existing literature on serum cytokine levels and additional factors as potential immunotherapeutic targets and lung cancer prognostic indicators. Changes in serum cytokine levels have been identified as immunological biomarkers for lung cancer and predict the effectiveness of targeted immunotherapy.

Application of an angiogenesis-related genes risk model in lung adenocarcinoma prognosis and immunotherapy

Lung adenocarcinoma (LUAD) is an essential pathological subtype of non-small cell lung cancer and offers a severe problem for worldwide public health. There is mounting proof that angiogenesis is a crucial player in LUAD progression. Consequently, the purpose of this research was to construct a novel LUAD risk assessment model based on genetic markers related to angiogenesis. We accessed The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases for LUAD mRNA sequencing data and clinical information. Based on machine algorithms and bioinformatics, angiogenic gene-related risk scores (RS) were calculated. Patients in the high-risk category had a worse prognosis (p < 0.001) in the discovery TCGA cohort, and the results were confirmed by these three cohorts (validation TCGA cohort, total TCGA cohort, and GSE68465 cohort). Moreover, risk scores for genes involved in angiogenesis were independent risk factors for lung cancer in all four cohorts. The low-risk group was associated with better immune status and lower tumor mutational load. In addition, the somatic mutation study revealed that the low-risk group had a lower mutation frequency than the high-risk group. According to an analysis of tumor stem cell infiltration, HLA expression, and TIDE scores, the low-risk group had higher TIDE scores and HLA expression levels than the high-risk group, and the amount of tumor stem cell infiltration correlated with the risk score. In addition, high-risk groups may benefit from immune checkpoint inhibitors and targeted therapies. In conclusion, we developed an angiogenesis-related gene risk model to predict the prognosis of LUAD patients, which may aid in the classification of patients with LUAD and select medications for LUAD patients.

Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma

Background

As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD).

Methods

First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation.

Results

LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells.

Conclusions

We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.

A m6A methyltransferase-mediated immune signature determines prognosis, immune landscape and immunotherapy efficacy in patients with lung adenocarcinoma

BACKGROUND

As the most abundant modification in mRNA, the N⁶-methyladenosine (m⁶A) RNA modification is involved in the occurrence and development of various tumors. However, the underlying functions of this alteration in the immune microenvironment of lung adenocarcinoma (LUAD) remain unknown.

METHODS

We identified m⁶A-mediated immune genes by performing a correlation analysis. Next, a m⁶A-mediated immune model was constructed using multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The potential of this model to predict the immune landscapes, drug sensitivities, and immunotherapy responses of different LUAD risk groups was studied.

RESULTS

A m⁶A-mediated immune model containing 13 m⁶A-mediated immune genes was established and found to be an independent predictor of survival time. The prognosis of low-risk patients was significantly better than that of high-risk patients. These two risk groups displayed different immune environments, genomic backgrounds, chemotherapy responses and immunotherapy response tendencies. The low- and high-risk groups strongly corresponded to the immune-hot and immune-cold phenotypes, respectively. The low-risk group was more enriched in immune-related biological processes, and the high-risk group was more enriched in proliferation-related biological processes. Furthermore, low-risk patients responded better to immunotherapy based on the results obtained from the tumor immune dysfunction and exclusion (TIDE) algorithm and subclass mapping algorithm using five external independent immunotherapy cohorts.

CONCLUSIONS

Our results suggest that the m⁶A modification participates in regulating the tumor microenvironment. The m⁶A-mediated immune model may be useful to predict the immunotherapy responses and outcomes of patients with LUAD.