Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis.

The early-stage lung adenocarcinoma (LUAD) incidence has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure. LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response. First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results. This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

Zhu M ，Zeng Q ，Fan T ，Lei Y ，Wang F ，Zheng S ，Wang X ，Zeng H ，Tan F ，Sun N ，Xue Q ，He J ... -  《Frontiers in Immunology》

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

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). First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified 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 involved in the signature, which has not been reported in LUAD, for further experimental validation. 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 ( < 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 experiments further confirmed that could promote the proliferation and migration of LUAD cells. 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.

Wang Y ，Xu J ，Fang Y ，Gu J ，Zhao F ，Tang Y ，Xu R ，Zhang B ，Wu J ，Fang Z ，Li Y ... -  《Frontiers in Immunology》

An In Silico Analysis Reveals an EMT-Associated Gene Signature for Predicting Recurrence of Early-Stage Lung Adenocarcinoma.

The potential micrometastasis tends to cause recurrence of lung adenocarcinoma (LUAD) after surgical resection and consequently leads to an increase in the mortality risk. Compelling evidence has suggested the underlying mechanisms of tumor metastasis could involve the activation of an epithelial-mesenchymal transition (EMT) program. Hence, the objective of this study was to develop an EMT-associated gene signature for predicting the recurrence of early-stage LUAD. The mRNA expression data of patients with early-stage LUAD were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) available databases. Gene Set Variation Analysis (GSVA) was first performed to provide an assessment of EMT phenotype, whereas Weighted Gene Co-expression Network Analysis (WGCNA) was constructed to determine EMT-associated key modules and genes. Based on the genes, a novel EMT-associated signature for predicting the recurrence of early-stage LUAD was identified using a least absolute shrinkage and selection operator (LASSO) algorithm and a stepwise Cox proportional hazards regression model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves and Cox regression analyses were used to estimate the performance of the identified gene signature. GSVA revealed diverse EMT states in the early-stage LUAD. Further correlation analyses showed that the EMT states presented high correlations with several hallmarks of cancers, tumor purity, tumor microenvironment cells, and immune checkpoint genes. More importantly, Kaplan-Meier survival analyses indicated that patients with high EMT scores had worse recurrence-free survival (RFS) and overall survival (OS) than those with low EMT scores. A novel 5-gene signature (AGL, ECM1, ENPP1, SNX7, and TSPAN12) was established based on the EMT-associated genes from WGCNA and this signature successfully predicted that the high-risk patients had a higher recurrence rate compared with the low-risk patients. In further analyses, the signature represented robust prognostic values in 2 independent validation cohorts (GEO and TCGA datasets) and a combined GEO cohort as evaluated by Kaplan-Meier survival (P-value < .0001) and ROC analysis (AUC = 0.781). Moreover, the signature was corroborated to be independent of clinical factors by univariate and multivariate Cox regression analyses. Interestingly, the combination of the signature-based recurrence risk and tumor-node-metastasis (TNM) stage showed a superior predictive ability on the recurrence of patients with early-stage LUAD. Our study suggests that patients with early-stage LUAD exhibit diverse EMT states that play a vital role in tumor recurrence. The novel and promising EMT-associated 5-gene signature identified and validated in this study may be applied to predict the recurrence of early-stage LUAD, facilitating risk stratification, recurrence monitoring, and individualized management for the patients after surgical resection.

Han Y ，Wong FC ，Wang D ，Kahlert C ... -  《-》

Exploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature.

Lipid metabolic reprogramming is gaining attention as a hallmark of cancers. Recent mounting evidence indicates that the malignant behavior of breast cancer (BC) is closely related to lipid metabolism. Here, we focus on the estrogen receptor-positive (ER+) subtype, the most common subgroup of BC, to explore immunometabolism landscapes and prognostic significance according to lipid metabolism-related genes (LMRGs). Samples from The Cancer Genome Atlas (TCGA) database were used as training cohort, and samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Gene Expression Omnibus (GEO) datasets and our cohort were applied for external validation. The survival-related LMRG molecular pattern and signature were constructed by unsupervised consensus clustering and least absolute shrinkage and selection operator (LASSO) analysis. A lipid metabolism-related clinicopathologic nomogram was established. Gene enrichment and pathway analysis were performed to explore the underlying mechanism. Immune landscapes, immunotherapy and chemotherapy response were further explored. Moreover, the relationship between gene expression and clinicopathological features was assessed by immunohistochemistry. Two LMRG molecular patterns were identified and associated with distinct prognoses and immune cell infiltration. Next, a prognostic signature based on nine survival-related LMRGs was established and validated. The signature was confirmed to be an independent prognostic factor and an optimal nomogram incorporating age and T stage (AUC of 5-year overall survival: 0.778). Pathway enrichment analysis revealed differences in immune activities, lipid biosynthesis and drug metabolism by comparing groups with low- and high-risk scores. Further exploration verified different immune microenvironment profiles, immune checkpoint expression, and sensitivity to immunotherapy and chemotherapy between the two groups. Finally, arachidonate 15-lipoxygenase (ALOX15) was selected as the most prominent differentially expressed gene between the two groups. Its expression was positively related to larger tumor size, more advanced tumor stage and vascular invasion in our cohort (n = 149). This is the first lipid metabolism-based signature with value for prognosis prediction and immunotherapy or chemotherapy guidance for ER+ BC.

Shen L ，Huang H ，Li J ，Chen W ，Yao Y ，Hu J ，Zhou J ，Huang F ，Ni C ... -  《Frontiers in Immunology》

Lipid metabolism-related gene prognostic index (LMRGPI) reveals distinct prognosis and treatment patterns for patients with early-stage pulmonary adenocarcinoma.

Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.

Jiang A ，Chen X ，Zheng H ，Liu N ，Ding Q ，Li Y ，Fan C ，Fu X ，Liang X ，Tian T ，Ruan Z ，Yao Y ... -  《International Journal of Medical Sciences》