Lung Adenocarcinoma Data Research Articles

CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled ‘radiogenomics’, a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research.

Low TP53 variant allele frequency as a biomarker for anti-programmed death (ligand) 1 monotherapy in lung adenocarcinoma.

TP53 mutation heterogeneity should be considered when using TP53 as a predictive biomarker for anti-programmed death (ligand) 1 (PD-(L)1) monotherapy in lung adenocarcinoma (LUAD). However, whether TP53 variant allele frequency (VAF) should also be considered remains unknown. Patients with LUAD from both published research and the local cohort were included to discover and validate the relationship between TP53 VAF and the efficacy of PD-(L)1inhibitors. The Cancer Genome Atlas (TCGA) LUAD data were included for genomic, transcriptomic, and tumor microenvironment analysis. Among 159 patients in the discovery cohort, low TP53 VAF patients (VAF ≤ 25%) experienced significantly longer progression-free survival (PFS) than both high TP53 VAF (5.4 vs. 3.3months; p=.021) and TP53-wild-type patients (5.4 vs. 2.5months; p=.011). Multivariate Cox regression revealed low TP53 VAF as an independent biomarker of better efficacy. Among 50 patients in the combined validation cohort, median PFS of low TP53 VAF patients was also significantly longer than that of high TP53 VAF patients (12.0 vs. 2.1months; p=.037). Analyzed with 469 TCGA LUAD samples, low TP53 VAF is associated with significantly higher PD-L1 expression, enrichment of gene sets related to T-cell activation, T cell-mediated immunity, and interferon-γ signaling pathways, and independently associated with more tumor-infiltrating CD8+ T cells compared with both high TP53 VAF and TP53-wild type. TP53 VAF should also be considered when using TP53 as a predictive biomarker. Only low TP53 VAF is independently associated with better efficacy of anti-PD-(L)1 monotherapy, which may result from higher PD-L1 expression and more tumor-infiltrating CD8+ T cells.

Identification of m7G-Related miRNA Signatures Associated with Prognosis, Oxidative Stress, and Immune Landscape in Lung Adenocarcinoma.

The role of N7-methylguanosine(m7G)-related miRNAs in lung adenocarcinoma (LUAD) remains unclear. We used LUAD data from The Cancer Genome Atlas (TCGA) to establish a risk model based on the m7G-related miRNAs, and divided patients into high-risk or low-risk subgroups. A nomogram for predicting overall survival (OS) was then constructed based on the independent risk factors. In addition, we performed a functional enrichment analysis and defined the oxidative stress-related genes, immune landscape as well as a drug response profile in the high-risk and low-risk subgroups. This study incorporated 28 m7G-related miRNAs into the risk model. The data showed a significant difference in the OS between the high-risk and low-risk subgroups. The receiver operating characteristic curve (ROC) predicted that the area under the curve (AUC) of one-year, three-year and five-year OS was 0.781, 0.804 and 0.853, respectively. The C-index of the prognostic nomogram for predicting OS was 0.739. We then analyzed the oxidative stress-related genes and immune landscape in the high-risk and low-risk subgroups. The data demonstrated significant differences in the expression of albumin (ALB), estimated score, immune score, stromal score, immune cell infiltration and functions between the high-risk and low-risk subgroups. In addition, the drug response analysis showed that low-risk subgroups may be more sensitive to tyrosine kinase inhibitor (TKI) and histone deacetylase (HDAC) inhibitors. We successfully developed a novel risk model based on m7G-related miRNAs in this study. The model can predict clinical prognosis and guide therapeutic regimens in patients with LUAD. Our data also provided new insights into the molecular mechanisms of m7G in LUAD.

Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression.

Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.

Validating a Macrophage Marker Gene Signature (MMGS) in Lung Adenocarcinoma Prognosis and Response to Immunotherapy.

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. Tumor-associated macrophages play pivotal roles in the tumor microenvironment (TME) and prognosis of LUAD. We first used single-cell RNA sequencing data to identify macrophage marker genes in LUAD. Univariate, least absolute shrinkage and selection operator and stepwise multivariate Cox regression analyses were conducted to evaluate macrophage marker genes as prognostic factors and to construct the macrophage marker genes signature (MMGS). A novel 8-gene signature was constructed to predict prognosis based on 465 macrophage marker genes identified by an analysis of single-cell RNA sequencing data of LUAD, and was also verified in 4 independent GEO cohorts. The MMGS significantly classified patients into high-risk and low-risk groups in terms of OS. A prognostic nomogram based on independent risk factors was established to predict the 2-, 3- and 5-year survival, which indicated superior accuracy in predicting prognosis. The high-risk group was correlated to higher tumor mutational burden, number of neoantigens, T-cell receptor richness, and lower TIDE, which suggested that high-risk patients were more likely to benefit from immunotherapy. The prediction of the possibility of immunotherapy efficacy was also discussed. Analysis of an immunotherapy cohort further verified that patients with high-risk scores had better immunotherapy responses than low-risk patients. The MMGS is a promising signature for predicting prognosis and effectiveness of immunotherapy in patients with LUAD, and may be helpful for clinical decision-making.

B-Cell Infiltrate in the Tumor Microenvironment Is Associated With Improved Survival in Resected Lung Adenocarcinoma

IntroductionRelapse is common after resection of lung adenocarcinoma (LUAD). Features of the tumor microenvironment (TME) which influence postsurgical survival outcomes are poorly characterized. Here, we analyzed the TME of more than 1500 LUAD specimens to identify the relationship between B-cell infiltration and prognosis. MethodsWhole exome sequencing and bulk RNA sequencing were performed on LUADs and adjacent normal lung tissue. Relapse-free survival and overall survival (OS) were retrospectively correlated with characteristics of the tumor and TME in three data sets. ResultsHigh B-cell content (defined as >10% B cells) was associated with improved OS in both a The Cancer Genome Atlas–resected LUAD data set (p = 0.01) and a separate institutional stage II LUAD data set (p = 0.04, median not reached versus 89.5 mo). A validation cohort consisting of pooled microarray data representing more than 1400 resected stage I to III LUADs confirmed the association between greater B-cell abundance, specifically higher B-cell expression, and longer postsurgical survival (median OS 90 versus 71 mo, p < 0.01). Relapse-free survival was longer for patients with adenocarcinomas with high B-cell content across data sets, but it did not reach statistical significance. Subcategorization of B-cell subsets indicated that high naive B-cell content was most predictive of survival. There was no correlation between programmed death-ligand 1 expression, lymphoid aggregates, or overall immune infiltrate density and survival outcomes across the cohorts. ConclusionsThe growing adjuvant immunotherapy repertoire has increased the urgency for identifying prognostic and predictive biomarkers. Comprehensive profiling of more than 1500 LUADs suggests that high tumor-infiltrating B-cell content is a favorable prognostic marker.

Metabolic profiles of lung adenocarcinoma via peripheral blood and diagnostic model construction

The metabolic profile of cancerous cells is shifted to meet the cellular demand required for proliferation and growth. Here we show the features of cancer metabolic profiles using peripheral blood of healthy control subjects (n = 78) and lung adenocarcinoma (LUAD) patients (n = 64). Among 121 detected metabolites, diagnosis of LUAD is based on arginine, lysophosphatidylcholine-acyl (Lyso.PC.a) C16:0, and PC-diacyl (PC.aa) C38:3. Network analysis revealed that network heterogeneity, diameter, and shortest path were decreased in LUAD. On the contrary, these parameters were increased in advanced-stage compared to early-stage LUAD. Clustering coefficient, network density, and average degree were increased in LUAD compared to the healthy control, whereas these topologic parameters were decreased in advanced-stage compared to early-stage LUAD. Public LUAD data verified that the genes encoding enzymes for arginine (NOS, ARG, AZIN) and for Lyso.PC and PC (CHK, PCYT, LPCAT) were related with overall survival. Further studies are required to verify these results with larger samples and other histologic types of lung cancer.

Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

BackgroundThe TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.MethodsGenomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein–protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.ResultsTP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R2 < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.ConclusionsTP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Abstract 4288: Predicting DNA methylation in cervical cancer using somatic mutations in a classified mixed model prediction

Abstract In cervical cancer, the promoter CpG islands of several tumor suppressor genes have been found with abnormal DNA methylation (DNAm) levels, which results in transcriptional silencing. Genetic databases from tumor samples are available without the corresponding DNAm profile. To fulfill this gap and contribute to deciphering the link between genetic variants and DNAm, we performed this study to find a good statistical model for predicting DNAm using genetic data. To address this, we downloaded from the TCGA dataset the genes with significant somatic mutations, as reported by the BROAD Institute, and the DNAm beta values, in known CpG sites, from 307 Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) samples. Clinical data were also downloaded from patients, including race, age, gender, and cancer stage. We tested five mixed models containing both fixed and random effects to include the unmeasured source of variations in DNAm, for which we used the clustering of samples based on DNAm patterns. Models tested were: Classified mixed model prediction (CMMP), Linear regression model (LM), High dimensional shrinkage estimator based on the elastic net (ENET), a combination of CMMP - ENET, and Random Forest prediction (RF). Besides the significantly mutated genes, the clinical variables were also included as covariates to predict DNAm. We used shared random effects to borrow strength across racial groups, which improves predictive accuracy. Models were enhanced by combining other types of cancers to increase data heterogeneity and the number of samples. Lung Adenocarcinoma (LUAD) and Liver Hepatocellular Carcinoma (LIHC) were selected using Gini and Gap statistics for optimal clustering. Model fitting was evaluated using the average mean squared prediction error (MSPE). Results revealed that the CMMP model with CESC LIHC and LUAD data combined showed the best prediction results (lowest MSPE). The plot of the averaged estimated ENET coefficients indicated that the most important-significantly mutated genes to predict DNAm were CTNNB1, DMD, XIRP2, and PIK3CA. SNPs in these genes linked to cervical cancer include rs121913396, rs121913403 (CTNNB1), rs867262025, and rs121913279 (PIK3CA).In summary, we developed a mixed-effects model for accurately predicting DNAm levels in cervical cancer, using genes with significant somatic mutations (SNVs and INDELs). These results also align with growing evidence suggesting that genetic variation plays a role in DNAm. Citation Format: Jairo D. Ramos, J. Sunil Rao. Predicting DNA methylation in cervical cancer using somatic mutations in a classified mixed model prediction. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4288.

Construction of a prognostic model for lung adenocarcinoma based on membrane-tension-related genes.

Lung adenocarcinoma (LUAD), which is the most common type of non-small cell lung cancer (NSCLC), is one of the most aggressive and fatal tumors. Therefore, the identification of key biomarkers affecting prognosis is important to improving the prognosis of patients with LUAD. Cell membranes have long been understood; however, few studies have focused on the role of membrane tension in LUAD. The present study aimed to construct a prognostic model associated with membrane-tension-related genes (MRGs) and explore its prognostic value in LUAD patients. RNA sequencing data and the corresponding clinical characteristics data of LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Five membrane-tension prognosis-related genes (5-MRG) were screened by univariate and multifactorial COX regression and least absolute shrinkage and selection operator (LASSO) regression analyses. The data were then divided into testing, training, and all groups to build a prognostic model, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), copy number variations (CNV), tumor mutation burden (TMB), and tumor microenvironment (TME) analyses were performed to explore the potential mechanisms of MRGs. Finally, single-cell data from the GSE200972 dataset in the Gene Expression Omnibus (GEO) database were obtained to determine the distribution of prognostic MRGs. Construction and validation of the prognostic risk models were conducted using 5-MRG in the trial, test, and all data sets. Patients in the low-risk group had a better prognosis than those in the high-risk group, and the Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) confirmed that the model had a better predictive value for LUAD patients. GO and KEGG analyses of differential genes in the high- and low-risk groups were significantly enriched in immune-related pathways. Immune checkpoint (ICP) differential genes differed significantly in the high- and low-risk groups. By analyzing the single-cell sequencing data, the cells were divided into nine subpopulations and cell subpopulation localization through 5-MRG. The results of this study suggest that a prognostic model based on prognosis-associated MRGs can be used to predict the prognosis of LUAD patients. Therefore, prognosis-related MRGs could be potential prognostic biomarkers and therapeutic targets.

Integrative learning in developing an immunologic lncRNA signature as a consensus risk-stratification tool for lung adenocarcinoma.

In the tumor immune microenvironment, the contribution of innate and adaptive immune cells to tumor progression has been consistently demonstrated. However, reliable prognostic biomarkers for lung adenocarcinoma (LUAD) have not yet been identified. We thus developed and validated an immunologic long noncoding RNA (lncRNA) signature (ILLS) to facilitate the classification of patients with high and low risk and provide potential "made-to-measure" treatment choices. The LUAD data sets were obtained and processed from public databases of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The abundance of immune infiltration and its related pathways were calculated through consensus clustering, weighted gene coexpression network analysis (WGCNA), and an integrated ImmLnc to identify immune-related lncRNAs and extract immune-related prognostic lncRNAs. Based on the integrative procedure, the best algorithm composition was least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression in both directions to develop the ILLS in the TCGA-LUAD data set and validate the predictive power of 4 independent data sets, GSE31210, GSE37745, GSE30219, and GSE50081 through survival analysis, receiver operating characteristic (ROC) analysis, and multivariate Cox regression. The concordance index (C-index) analysis was transversely compared with 49 published signatures in the above 5 data sets to further confirm its stability and superiority. Finally, drug sensitivity analysis was conducted to explore potential therapeutic agents. Patients from the high-risk groups consistently had worse overall survival (OS) compared to the low-risk groups. ILLS proved to be an independent prognostic factor with favorable sensitivity and specificity. Among the 4 GEO data sets, compared to those reported in the other literature, ILLS maintained stable prediction ability and was more suitable as a consensus risk-stratification tool. However, The Cancer Immunome Atlas and IMvigor210 data sets demonstrated practical utility in recognizing target populations with effective immunotherapy, while the high-risk group exhibited potential targets for certain chemotherapy drugs, such as carmustine, etoposide, arsenic trioxide, and alectinib. ILLS demonstrated superior and stable prognostic prediction ability and thus has potential as a tool for assisting in risk classification and clinical decision-making in patients with LUAD.

Prognosis Prediction of Lung Adenocarcinoma Patients Based on Molecular Subgroups of DNA Methylation.

Lung adenocarcinoma (LUAD) is a malignant tumor with high mortality. At present, the clinicopathologic feature is the main breakthrough to assess the prognosis of LUAD patients. However, in most cases, the results are less than satisfactory. Cox regression analysis was conducted in this study to obtain methylation sites with significant prognostic relevance based on mRNA expression, DNA methylation data, and clinical data of LUAD from The Cancer Genome Atlas Program database. LUAD patients were grouped into 4 subtypes according to different methylation levels using K-means consensus cluster analysis. By survival analysis, patients were grouped into high-methylation and low-methylation groups. Later, 895 differentially expressed genes (DEGs) were obtained. Eight optimal methylation signature genes associated with prognosis were screened by Cox regression analysis, and a risk assessment model was constructed based on these genes. Samples were then classified into high-risk and low-risk groups depending on the risk assessment model, and prognostic, predictive ability was assessed using survival and receiver operating characteristic (ROC) curves. The results showed that this risk model had a great efficacy in predicting the prognosis of patients, and it was, therefore, able to be an independent prognostic factor. At last, the enrichment analysis demonstrated that the signaling pathways, including cell cycle, homologous recombination, P53 signaling pathway, DNA replication, pentose phosphate pathway, and glycolysis gluconeogenesis were remarkably activated in the high-risk group. In general, we construct an 8-gene model based on DNA methylation molecular subtypes by a series of bioinformatics methods, which can provide new insights for predicting the prognosis of patients with LUAD.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing

BackgroundT cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. We aimed to explore the expression profile of T cell marker genes, constructed a prognostic risk model based on these genes in Lung adenocarcinoma (LUAD), and investigated the link between this risk model and the immunotherapy response. MethodsWe obtained the single-cell sequencing data of LUAD from the literature, and screened out 6 tissue biopsy samples, including 32,108 cells from patients with non-small cell lung cancer, to identify T cell marker genes in LUAD. Combined with TCGA database, a prognostic risk model based on T-cell marker gene was constructed, and the data from GEO database was used for verification. We also investigated the association between this risk model and immunotherapy response. ResultsBased on scRNA-seq data 1839 T-cell marker genes were identified, after which a risk model consisting of 9 gene signatures for prognosis was constructed in combination with the TCGA dataset. This risk model divided patients into high-risk and low-risk groups based on overall survival. The multivariate analysis demonstrated that the risk model was an independent prognostic factor. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. Risk scores of the model were closely correlated with Linoleic acid metabolism, intestinal immune network for IgA production and drug metabolism cytochrome P450. ConclusionOur study proposed a novel prognostic risk model based on T cell marker genes for LUAD patients. The survival of LUAD patients as well as treatment outcomes may be accurately predicted by the prognostic risk model, and make the high-risk population present different immune cell infiltration and immunosuppression state.

ANK2 as a novel predictive biomarker for immune checkpoint inhibitors and its correlation with antitumor immunity in lung adenocarcinoma

BackgroundImmune checkpoint inhibitors (ICIs) have been shown to significantly improve the survival of patients with advanced lung adenocarcinoma (LUAD). However, only limited proportion of patients could benefit from ICIs. Novel biomarkers with strong predictability are needed for clinicians to maximize the efficacy of ICIs. Our study aimed to identify potential biomarkers predicting ICIs efficacy in LUAD.MethodsThe Cancer Genome Atlas (TCGA) PanCancer Atlas studies in cBioportal were used to evaluate the mutation frequency of ANK2 across multiple cancers. Clinical and mutational data for LUAD from ICIs-treated cohorts (Hellmann et al. and Rizvi et al.) were collected to explore the correlation between ANK2 mutation and clinical outcomes. In addition, the relationship between ANK2 expression and clinical outcomes was analyzed using LUAD data from TCGA and Gene Expression Omnibus. Furthermore, the impact of ANK2 mutation and expression on the tumor immune microenvironment of LUAD was analyzed using TCGA and TISIDB databases.ResultsPatients with ANK2 mutation benefited more from ICIs. In ICIs-treated cohort, prolonged progression-free survival (PFS) (median PFS: NR (not reached) vs. 5.42 months, HR (hazard ratio) 0.31, 95% CI 0.18–0.54; P = 0.0037), improved complete response rate (17.65% vs. 1.85%, P = 0.0402), and improved objective response rate (64.71% vs. 24.07%, P = 0.0033) were observed in LUAD patients with ANK2 mutation compared to their wild-type counterparts. Regarding ANK2 expression, it was observed that ANK2 expression was decreased in LUAD (P < 0.05) and a higher level of ANK2 expression was associated with longer overall survival (HR 0.69, 95% CI 0.52–0.92; P = 0.012) in TCGA LUAD cohort. Moreover, ANK2 mutation or higher ANK2 expression correlated with enhanced antitumor immunity and “hot” tumor microenvironment in LUAD, which could be potential mechanisms that ANK2 mutation facilitated ICIs therapy and patients with higher ANK2 expression survived longer.ConclusionOur findings suggest that ANK2 mutation or increased ANK2 expression may serve as a favorable biomarker for the efficacy of ICIs in patients with LUAD.

Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs.

Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD. The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the "ConsensusClusterPlus" package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy. Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group. Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis.

High SERPINH1 expression predicts poor prognosis in lung adenocarcinoma.

Serpine Protease Inhibitorclade H1 (SERPINH1) is abnormally expressed in a variety of tumor tissues and is linked to the biological processes of tumorigenesis, migration, invasion, and metastasis. SERPINH1 expression and prognosis in malignant tumors, such as gastric, colorectal, and breast cancers, have previously been studied, but the gene has not yet been investigated in lung adenocarcinoma (LUAD) in terms of prognosis and the potential mechanisms of action. SERPINH1 was identified as an independent prognostic factor for LUAD in The Cancer Genome Atlas (TCGA) cohort and Affiliated Hospital of Nantong University (NTU) cohort (the LUAD data set) by univariate and multivariate Cox regression analyses. Additionally, we performed immunohistochemical staining to analyze the expression of SERPINH1 in LUAD and normal lung tissue. Based on the TCGA database, we analyzed the correlation of this gene with the tumor mutation burden (TMB), tumor microenvironment, immune infiltration, immune checkpoints, and anti-tumor drugs using the R language-related R package. SERPINH1 was highly expressed in LUAD tissue. Kaplan-Meier survival curves in both the TCGA cohort and the NTU cohort showed that the SERPINH1 low-expression group had a higher survival rate than the high-expression group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the SERPINH1 co-expressed genes revealed that the gene was associated with the extracellular matrix and cell proliferation and migration. The analysis of SERPINH1 and the TMB revealed a superior survival advantage for patients with high TMB and high SERPINH1 expression, and worse survival for those with low TMB and high SERPINH1 expression. The analysis of the tumor microenvironment (TME) and immune infiltration revealed that the high and low expression of SERPINH1 was associated with different immune infiltration characteristics. The analysis of the immune checkpoints and anti-tumor drugs showed that immunotherapy and anti-neoplastic treatment were more efficacious in the high SERPINH1 expression group than the low SERPINH1 expression group. Using LUAD tissues and clinical samples, we showed that SERPINH1 can be used as a prognostic biomarker for LUAD. Our findings provide a new approach and strategy for the clinical treatment of LUAD patients.

Identification and Application of a Novel Immune-Related lncRNA Signature on the Prognosis and Immunotherapy for Lung Adenocarcinoma.

Long non-coding RNA (lncRNA) participates in the immune regulation of lung cancer. However, limited studies showed the potential roles of immune-related lncRNAs (IRLs) in predicting survival and immunotherapy response of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) and ImmLnc databases, IRLs were identified through weighted gene coexpression network analysis (WGCNA), Cox regression, and Lasso regression analyses. The predictive ability was validated by Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves in the internal dataset, external dataset, and clinical study. The immunophenoscore (IPS)-PD1/PD-L1 blocker and IPS-CTLA4 blocker data of LUAD were obtained in TCIA to predict the response to immune checkpoint inhibitors (ICIs). The expression levels of immune checkpoint molecules and markers for hyperprogressive disease were analyzed. A six-IRL signature was identified, and patients were stratified into high- and low-risk groups. The low-risk had improved survival outcome (p = 0.006 in the training dataset, p = 0.010 in the testing dataset, p &lt; 0.001 in the entire dataset), a stronger response to ICI (p &lt; 0.001 in response to anti-PD-1/PD-L1, p &lt; 0.001 in response to anti-CTLA4), and higher expression levels of immune checkpoint molecules (p &lt; 0.001 in PD-1, p &lt; 0.001 in PD-L1, p &lt; 0.001 in CTLA4) but expressed more biomarkers of hyperprogression in immunotherapy (p = 0.002 in MDM2, p &lt; 0.001 in MDM4). The six-IRL signature exhibits a promising prediction value of clinical prognosis and ICI efficacy in LUAD. Patients with low risk might gain benefits from ICI, although some have a risk of hyperprogressive disease.

High expression levels of FANCI correlate with worse prognosis and promote tumor growth of lung adenocarcinoma partly via suppression of M1 macrophages

FANCI, a member of the Fanconi anemia (FA) complementation group, normally associates with FANCD2 to play an important role in ribosome biogenesis and DNA repair. However, the correlation of FANCI with prognostic value and the molecular mechanism in patients with lung adenocarcinoma (LUAD) remains unclear. In the present study, bioinformatics analysis was performed on LUAD data from TCGA and GEO databases, and further confirmed by in vitro experiments. We found that a high level of FANCI was significantly correlated with a worse survival probability in patients with LUAD. Moreover, the results from in vitro experiments revealed high levels of FANCI in LUAD specimens and cell lines. Knockdown of FANCI expression in A549 and H460 cells significantly inhibited cell viability and clone formation of LUAD cells in vitro and in vivo. Furthermore, high FANCI levels were negatively correlated with a variety of tumor-infiltrating immune cells. Importantly, the overexpression of FANCI significantly inhibited the activation of M1 macrophages. All the data demonstrated that FANCI was a useful prognostic biomarker in patients with LUAD, and knockdown of FANCI inhibited tumor growth of LUAD cells in vitro and in vivo, partly by suppressing the activation of M1 macrophages.

A pyroptosis-related gene model and its correlation with the microenvironment of lung adenocarcinoma: A bioinformatics analysis and experimental verification.

Background: Non-small cell lung cancer, comprising lung adenocarcinoma (LUAD) and lung squamous cell carcinoma, is one of the leading causes of cancer-related mortality. Pyroptosis is a new form of programmed cell death involved in cancer development. The relationship between LUAD and pyroptosis is unclear. This research aims to investigate this relationship and develop a stratified clinical model based on pyroptosis-related genes (PRGs). Methods: We analyzed the data of LUAD from The Cancer Genome Atlas (TCGA) and evaluated the expression of 48 PRGs to identify the differentially expressed genes. Then, constructing the risk model using the least absolute shrinkage and selection operator and the Cox regression method to find the gene signatures. The functional enrichment, immune cell infiltration, tumor mutational burden (TMB), and expression of immune checkpoints were compared to investigate the potential mechanism. The IC50 of common drugs was evaluated and compared. The inflammasome activation assay and lactate dehydrogenase (LDH) assay of NLR-family CARD-containing protein 4 (NLRC4) were also performed to confirm the role of pyroptosis in LUAD. Results: The pyroptosis-related model accurately predicted the prognosis of patients with LUAD, with the low-risk group exhibiting a higher survival probability. The risk score was an independent prognostic factor for survival. The stratified patients exhibited distinct tumor microenvironments, TMB, and drug sensitivity. The validation experiments of NLRC4 confirmed its role in inducing pyroptosis via promoting IL-1 maturation. Conclusion: PRGs regulated the tumor microenvironment and influenced the outcome of LUAD. NLRC4 may function as a hub gene in the process of LUAD.