Signature In Lung Adenocarcinoma Research Articles

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma

BackgroundRecently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD).MethodsSingle-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS).ResultsThis study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy.ConclusionsThis study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma.

Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis. Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression. Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR. Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.

Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in lung adenocarcinoma.

Cells associated with cancer (CAFs) contribute significantly to the stroma of a tumor microenvironment (TME), which is related to the occurrence, treatment, and prognosis of lung adenocarcinoma (LUAD). Therefore, this study investigated the function of CAF-associated genes in the microenvironment of LUAD. The Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from the TCGA Lung Adenocarcinoma cohort (TCGA-LUAD). The GSE68465 dataset, as the external validation set, was from the Gene Expression Omnibus (GEO) database. Besides, CAF-associated genes were sourced from the GeneCards and Molecular Signatures Database (MsigDB). For LUAD, differentially expressed CAF-related genes were selected from overlapping CAF and LUAD patient and control samples. Next, LASSO and Univariate Cox analyses were used to construct the risk model. Additionally, an analysis of Cox regression was used to construct a nomogram. Next, the immune infiltration in malignant tumour tissues was compared between high- and low-risk groups using Estimation of STromal and Immune cells in MAlignant Tumours (ESTIMATE) tissues and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT). The sensitivity differences of immunotherapy between the two risk groups were estimated by Tumor Immune Dysfunction and Exclusion (TIDE), and compared by rank-sum test. Finally, the model genes were detected by fluorescent real-time quantitative polymerase chain reaction (qRT-PCR). A total of 57 DE-CAFGs were acquired, and 9 of them (SHCBP1, CCNA2, AKAP12, CCNB1, GALNT3, SCGB1A1, CPS1, CDC6, and CXCL13) were selected as prognostic biomarkers. The Cox independent prognosis revealed the RiskScore and Stage were the two LUAD independent prognosis factors Moreover, 11 types of immune cells (memory B cells, resting natural killer cells (NK cells), Eosinophils, Macrophages M0, CD4 memory resting T cells, CD4 memory activated T cells, resting Mast cells, naive B cells, T cells regulatory (Tregs), neutrophils, and plasma cell), and 18 human leukocyte antigen (HLA) genes were different with the two risk groups. Lastly, the TIDE analysis showed differences between the two risk groups for TIDE, T cell dysfunction, and T cell exclusion, PD-L1 treatment scores. Lastly, Both LUAD and normal samples expressed the 9 model genes differently. A CAF-related prognostic model was constructed, which may have potential immunotherapy guiding significance for LUAD patients.

Identification and validation of a disulfidptosis-related genes prognostic signature in lung adenocarcinoma

Disulfidptosis, a newly revealed form of cell death, regulated by numerous genes that has been recently identified. The exact role of disulfidptosis in lung adenocarcinoma (LUAD) still uncertain. Objective of this study was to explore potential prognostic markers among disulfidptosis genes in LUAD. By combining transcriptomic information from Gene Expression Omnibus databases and The Cancer Genome Atlas, we identified differentially expressed and prognostic disulfidptosis genes. By conducting least absolute shrinkage and selection operator with multivariate Cox regression, four disulfidptosis genes were selected to create the prognostic signature. The implementation of the signature separated the training and validation cohorts into groups with high- and low-risk. Subsequently, the model was verified by conducting an independent analysis of receiver operating characteristic (ROC) curves. Further comparisons were made between the two risk-divided groups with regards the tumor microenvironment, immune cell infiltration, immunotherapy response, and drug sensitivity. The signature was constructed using four disulfidptosis-related genes: SLC7A11, SLC3A2, NCKAP1, and GYS1. According to ROC curves, the signature was effective for predicting LUAD prognosis. In addition, the prognostic signature correlated with sensitivity to chemotherapeutic agents and the efficacy of immunotherapy in LUAD. Finally, through external validation, we showed that NCKAP1 are correlated with tumor migration, proliferation, and invasion of LUAD cells. GYS1 affects immune cell, especially M2 macrophage infiltration in the tumor microenvironment. The disulfidptosis four-gene model can reliably predict the prognosis of patients diagnosed with LUAD, thereby providing valuable information for clinical applications and immunotherapy.

Identification of metabolism-related gene signature in lung adenocarcinoma.

Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases. TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established. A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature. In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

Construction and validation of an efferocytosis-related prognostic signature in lung adenocarcinoma.

Accumulating evidence highlights the potential significance of efferocytosis in tumor progression. This study is directed towards the construction of a prognostic risk model for lung adenocarcinoma (LUAD), grounded on efferocytosis-related genes (ERGs). Employing LASSO-COX regression analysis, a risk-prognostic model was formulated, centered on seven ERGs. Concurrently, a nomogram was established that incorporated patient clinical features and risk scores. The predictive accuracy of the risk model and the nomogram was substantiated via external validation sets. The landscapes of immune infiltration and genetic mutation were evaluated for high- and low-risk groups, with the expression of seven key genes validated through RT-PCR. Our findings reveal that the high-risk group displayed considerably inferior survival outcomes in comparison to the low-risk group. A diminished abundance of immune cell infiltrates and a higher prevalence of gene mutations characterized the high-risk group. Genes with high expression were markedly enriched in pathways related to cell proliferation. The superior predictive performance of the risk model and nomogram was adequately substantiated by the external validation sets (GSE31210, GSE30219, and GSE50081). In addition, we discerned several potential therapeutic drugs demonstrating different sensitivities across patient risk groups. The differential expression of seven central genes was confirmed in A549, H1299, and BEAS-2B cell lines. The constructed risk model and nomogram display high accuracy in predicting the survival and immune landscape of LUAD patients, thus providing invaluable prognostic tools in clinical scenarios.

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.

Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD. We applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells. The scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS's prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis. Our research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.

Development of the prognostic value in lung adenocarcinoma based on anoikis-related genes and initial experimental validation.

Lung adenocarcinoma (LUAD) is a highly aggressive cancer in advanced stages and has the highest cancer-related death across the world. Anoikis has emerged as a specific form of apoptotic cell death that may play a vital role in the formation and development of tumors. Based on The Cancer Genome Atlas dataset, we developed a novel anoikis-related genes (ARGs) signature in LUAD and evaluated the differences between low and high-risk groups in clinical characteristics, expression patterns, immune cell infiltration, and drug sensitivity, etc. According to multivariate Cox regression analysis, the risk score was identified as a significant independent prognostic factor. The possible biological pathways of ARGs' were assessed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The immune infiltration landscape and risk score of ARGs were analyzed by ESTIMATE and CIBERSORT analysis. A nomogram grounded on six key ARGs and clinicopathological features was provided. Moreover, experiment validation of the expression patterns of six hub ARGs in lung cancer cell lines was conducted. We identified 53 survival-related LUAD anoikis-related differentially expressed genes and finally six hub anoikis genes (LDHA, SLC2A1, SERPINB5, ITGB4, BRCA2, and PIK3R1) were selected to construct an ARG model. The risk model could efficiently cluster the patients into low- and high-risk groups which could accurately predict clinical outcomes for LUAD patients. There is evidence that the prognostic risk score is a remarkable prognostic factor in determining overall survival. Different immune statuses and drug sensitivity between low- and high-risk groups were explored according to functional analysis. On the basis of risk scores and LUAD clinicopathological features, a novel nomogram was developed. Ultimately, all six key genes except for PIK3R1 were proved to be upregulated in LUAD tissues and cell lines by bioinformatics analysis and experimental validation. The result of the present study suggest that ARGs could be carcinogenic to LUAD and could be used as an effective stratification factor to customize therapies and forecast the survival rate in LUAD patients.

Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis

BackgroundLung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments.MethodsThe expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan–Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes.ResultsA total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis.ConclusionA novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

Development of a Novel Pyroptosis-Associated lncRNA Biomarker Signature in Lung Adenocarcinoma.

Pyroptosis is a novel type of cell death observed in various diseases. Our study aimed to investigate the relationship between pyroptosis-associated-long non-coding RNAs (lncRNAs), immune infiltration, and expression of immune checkpoints in the setting of lung adenocarcinoma and the prognostic value of pyroptosis-related lncRNAs. RNA-seq transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) were downloaded, and consensus clustering analysis was used to separate the samples into two groups. Least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk signature. The association between pyroptosis-associated lncRNAs, immune infiltration, and expression of immune checkpoints were analysed. The cBioPortal tool was used to discover genomic alterations. Gene set enrichment analysis (GSEA) was utilized to investigate downstream pathways of the two clusters. Drug sensitivity was also examined. A total of 43 DEGs and 3643 differentially expressed lncRNAs were identified between 497 lung adenocarcinoma tissues and 54 normal samples. A signature consisting of 11 pyroptosis-related lncRNAs was established as prognostic for overall survival. Patients in the low-risk group have a significant overall survival advantage over those in the high-risk group in the training group. Immune checkpoints were expressed differently between the two risk groups. Risk scores were validated to develop an independent prognostic model based on multivariate Cox regression analysis. The area under time-dependent receiver operating characteristic curve (AUC of the ROC) at 1-, 3-, and 5-years measured0.778, 0.757, and 0.735, respectively. The high-risk group was more sensitive to chemotherapeutic drugs than the low-risk group. This study demonstrates the association between pyroptosis-associated lncRNAs and prognosis in lung adenocarcinoma and enables a robust predictive signature of 11 lncRNAs to inform overall survival.

Prognostic roles of a novel basement membranes-related gene signature in lung adenocarcinoma.

Background: The basement membranes (BMs) are involved in tumor progression, while few comprehensive analyses to date are performed on the role of BM-related gene signatures in lung adenocarcinoma (LUAD). Thus, we aimed to develop a novel prognostic model in LUAD based on BMs-related gene profiling. Methods: The LUAD BMs-related gene profiling and corresponding clinicopathological data were obtained from the basement membrane BASE, The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) databases. The Cox regression and least absolute shrinkage and selection operator (LASSO) methods were used to construct a BMs-based risk signature. The concordance index (C-index), receiver operating characteristic (ROC), and calibration curves were generated to evaluate the nomogram. The GSE72094 dataset was used to validate prediction of the signature. The differences in functional enrichment, immune infiltration, and drug sensitivity analyses were compared based on risk score. Results: In TCGA training cohort, 10 BMs-related genes were found, (e.g., ACAN, ADAMTS15, ADAMTS8, BCAN, etc). The signal signature based on these 10 genes was categorized into high- and low-risk groups regarding survival differences (p < 0.001). Multivariable analysis showed that the signature of combined 10 BMs-related genes was an independent prognostic predictor. Such a prognostic value of BMs-based signature in validation cohort of the GSE72094 were further verified. The GEO verification, C-index, and ROC curve showed that the nomogram had accurate prediction performance. The functional analysis suggested that BMs were mainly enriched in extracellular matrix-receptor (ECM-receptor) interaction. Moreover, the BMs-based model was correlated with immune checkpoint. Conclusion: This study identified BMs-based risk signature genes and demonstrated their ability to predict prognosis and guide personalized treatment of patients with LUAD.

Identification of a cuproptosis-related lncRNA prognostic signature in lung adenocarcinoma.

Cuproptosis-related long non-coding RNA (lncRNA) diseases are associated with the occurrence and development of tumors. This study aimed to investigate whether cuproptosis-related lncRNA can predict the prognosis of patients with lung adenocarcinoma (LUAD). Cuproptosis-related lncRNA prognosis (CLPS) model was successfully constructed through cox regression and lasso regression analyses. Then, the prognostic value of CLPS model was tested through the survival analysis, the ROC curve and the nomogram. Finally, the correlation of CLPS model with tumor immunity and tumor mutation burden was analyzed, and the potential susceptibility of drugs for LUAD were predicted. CLPS model for LUAD (AC090948.1, CRIM1-DT, AC026356.2, AC004832.5, AL161431.1) was successfully constructed, which has an independent prognostic value. Furthermore, the risk score of CLPS model was correlated with tumor immune characteristics and immune escape, which can predict the sensitivity of drugs including Cisplatin, Etoposide, Gemcitabine, and Erlotinib. In conclusion, it was found that CLPS model was associated with tumor immunity and tumor mutation load, which also predicted four potentially sensitive drugs for LUAD patients at different risks.

Abstract A030: Deciphering the micro-RNA landscape of AKT isoforms and its clinical impact in patients with lung adenocarcinoma

Abstract Over the last 25 years, AKT kinase has been thoroughly investigated and characterized as a crucial regulator of cellular homeostasis, affecting a plethora of essential molecular functions. The three identified isoforms of this kinase, namely AKT1, AKT2 and AKT3, often display opposing and antagonizing cellular functions. One of these important cellular functions is the expression and activation of micro-RNAs (miRNAs), an important RNA process that epigenetically regulates the human transcriptome. In the context of human cancer and based on several reports, AKT kinase has been associated with aberrant expression of oncogenic miRNAs, that in turn hijack oncogenic pathways, leading to tumor progression, metastasis and resistance to therapy, especially in lung adenocarcinoma (LUAD). Nevertheless, a comprehensive analysis of the impact of each AKT isoform on the regulation and enrichment of a specific miRNA signature in lung adenocarcinoma has yet to be performed. Here, we were willing to elucidate the miRNA epigenetic landscape and downstream targets, driven by each AKT isoform, and its clinical importance in patients with lung adenocarcinoma. To this extent, we utilized RNA-Seq and Small RNA-Seq data from The Cancer Genome Atlas (TCGA-LUAD) to identify the miRNA signatures related with the expression of each AKT isoform in LUAD. Gene Set Enrichment Analysis from RNA-Seq and miRNA levels analysis from Small RNA-Seq, identified a unique miRNA signature for each AKT isoform, demonstrating their largely opposing effect in miRNA profiling. In parallel analysis, in silico target analysis of the top 50 regulated miRNAs, demonstrated another set of unique targets for each isoform-dependent miRNA signature, which is involved in metabolism, DNA damage, RNA processing, cell survival, immune response and metastasis. More importantly, clinical and survival analysis revealed that the top 50 Differentially regulated miRNAs, ranked by q-value, for each isoform correlated with poor progression free survival in patients with LUAD. Collectively, our atlas of AKT isoform-driven miRNA landscapes implicates AKT as a global effector of miRNA landscape in lung adenocarcinoma. Due to the lack of reliable isoform-specific AKT inhibitors, the identification of the oncogenic miRNA networks regulated by each isoform, acts as an important transnational resource for the design and testing of inhibitors that bypass AKT inhibition toxicity, sparing its impactful physiological functions. Citation Format: George Laliotis, Pauline Lin. Deciphering the micro-RNA landscape of AKT isoforms and its clinical impact in patients with lung adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr A030.

Development of a copper metabolism-related gene signature in lung adenocarcinoma.

The dysregulation of copper metabolism is closely related to the occurrence and progression of cancer. This study aims to investigate the prognostic value of copper metabolism-related genes (CMRGs) in lung adenocarcinoma (LUAD) and its characterization in the tumor microenvironment (TME). The differentially expressed CMRGs were identified in The Cancer Genome Atlas (TCGA) of LUAD. The least absolute shrinkage and selection operator regression (LASSO) and multivariate Cox regression analysis were used to establish the copper metabolism-related gene signature (CMRGs), which was also validated in Gene Expression Omnibus (GEO) database (GSE72094). The expression of key genes was verified by quantitative real-time PCR (qRT-PCR). Then, the CMRGS was used to develop a nomogram to predict the 1-year, 3-year, and 5-year overall survival (OS). In addition, differences in tumor mutation burden (TMB), biological characteristics and immune cell infiltration between high-risk and low-risk groups were systematically analyzed. Immunophenoscore (IPS) and an anti-PD-L1 immunotherapy cohort (IMvigor210) were used to verify whether CMRGS can predict the response to immunotherapy in LUAD. 34 differentially expressed CMRGs were identified in the TCGA dataset, 11 of which were associated with OS. The CMRGS composed of 3 key genes (LOXL2, SLC31A2 and SOD3) had showed good clinical value and stratification ability in the prognostic assessment of LUAD patients. The results of qRT-PCR confirmed the expression of key CMRGs in LUAD and normal tissues. Then, all LUAD patients were divided into low-risk and high-risk groups based on median risk score. Those in the low-risk group had a significantly longer OS than those in the high-risk group (P<0.0001). The area under curve (AUC) values of the nomogram at 1, 3, and 5 years were 0.734, 0.735, and 0.720, respectively. Calibration curves comparing predicted and actual OS were close to ideal model, indicating a good consistency between prediction and actual observation. Functional enrichment analysis showed that the low-risk group was enriched in a large number of immune pathways. The results of immune infiltration analysis also confirmed that there were a variety of immune cell infiltration in the low-risk group. In addition, multiple immune checkpoints were highly expressed in the low-risk group and may benefit better from immunotherapy. CMRGS is a promising biomarker to assess the prognosis of LUAD patients and may be serve as a guidance on immunotherapy.

Identification of a novel oxidative stress-related prognostic model in lung adenocarcinoma.

Background: Oxidative stress (OxS) participates in a variety of biological processes, and is considered to be related to the occurrence and progression of many tumors; however, the potential diagnostic value of OxS in lung cancer remains unclear. Methods: The clinicopathological and transcriptome data for lung adenocarcinoma (LUAD) were collected from TCGA and GEO database. LASSO regression was used to construct a prognostic risk model. The prognostic significance of the OxS-related genes was explored using a Kaplan-Meier plotter database. The prediction performance of the risk model was shown in both the TCGA and GSE68465 cohorts. The qRT-PCR was performed to explore the expression of genes. CCK-8, Edu and transwell assays were conducted to analyze the role of CAT on cell proliferation migration and invasion in lung cancer. Immune infiltration was evaluated by CIBERSORT and mutational landscape was displayed in the TCGA database. Moreover, the relationship between risk score with drug sensitivity was investigated by pRRophetic. Results: We identified a prognosis related risk model based on a four OxS gene signature in LUAD, including CYP2D6, FM O 3, CAT, and GAPDH. The survival analysis and ROC curve indicated good predictive power of the model in both the TCGA and GEO cohorts. LUAD patients in the high-risk group had a shorter OS compared to the low-risk group. QRT-PCR result showed that the expression of four genes was consistent with previous analysis in cell lines. Moreover, overexpression of CAT could decrease the proliferation, invasion and migration of lung cancer cells. The Cox regression analysis showed that the risk score could be used as an independent prognostic factor for OS. LUAD patients in the high-risk score group exhibited a higher tumor mutation burden and risk score were closely related to tumor associated immune cell infiltration, as well as the expression of immune checkpoint molecules. Both the high- and low-risk groups have significant differences in sensitivity to some common chemotherapy drugs, such as Paclitaxel, Docetaxel, and Vinblastine, which may contribute to clinical treatment decisions. Conclusion: We established a robust OxS-related prognostic model, which may contribute to individualized immunotherapeutic strategies in LUAD.

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

BackgroundRecent studies have emphasized the close relationship between macrophages and tumor immunity, and the prognosis of lung adenocarcinoma (LUAD) patients is intimately linked to this. Nonetheless, the prognostic signature and classification of different immune patterns in LUAD patients based on the macrophages is largely unexplored. MethodsTwo sc-RNAseq datasets of LUAD patients were collected and reprocessed. The differentially expressed genes (DEGs) related to macrophages between LUAD tissues and normal lung tissues were then identified. Based upon the above genes, three distinct immune patterns in the TCGA-LUAD cohort were identified. The ssGSEA and CIBERSORT were applied for immune profiling and characterization of different subtypes. A four-gene prognostic signature for LUAD patients was established based on the DEGs between the subtypes using stepwise multi-Cox regression. TCGA-LUAD cohort was used as training set. Five GEO-LUAD datasets and an independent cohort containing 112 LUAD samples were used for validation. TIDE (tumor immune dysfunction and exclusion) and drug sensitivity analyses were also performed. ResultsMacrophage-related differentially expressed genes were found out using the publicly available scRNA-seq data of LUAD. Three different immune patterns which were proved to have distinct immune infiltration characteristics in the TCGA-LUAD cohort were recognized based on the above macrophage-related genes. Thereafter, 174 DEGs among the above three different immune patterns were figured out; on the basis of this, a four-gene prognostic signature was constructed. This signature distinguished the prognosis of LUAD patients well in various GSE datasets as well as our independent cohort. Further analyses revealed that patients which had a higher risk score also accompanied with a lower immune infiltration level and a worse response to several immunotherapy biomarkers. ConclusionThis study highlighted that macrophage were significantly associated with TME diversity and complexity. The four-gene prognostic signature could be used for predicting outcomes and immune landscapes for patients with LUAD.

Comprehensive Analysis of a Novel Immune-Related Gene Signature in Lung Adenocarcinoma

Lung cancer is the major cause of cancer-related deaths around the world. Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, contributed to the majority of mortalities and showed different clinical outcomes in prognosis. Tumor-infiltrated immune cells at the tumor site are associated with better survival and immunotherapy response. Thus, it is essential to further investigate the molecular mechanisms and new prognostic biomarkers of lung adenocarcinoma development and progression. In this study, a six-gene signature (CR2, FGF5, INSL4, RAET1L, AGER, and TNFRSF13C) was established to predict the prognosis of LUAD patients, as well as predictive value. The prognostic risk model was also significantly associated with the infiltration of immune cells in LUAD microenvironments. To sum up, a novel immune-related six-gene signature (CR2, FGF5, INSL4, RAET1L, AGER, and TNFRSF13C) was identified that could predict LUAD survival and is highly related to B cells and dendritic cells, which may provide a theoretical basis of personalized treatment for targeted immunotherapy.

Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma.

Background Lung cancer is emerging as one of most deadly diseases, and the mortality rate was still high with 5-year overall survival rate less than 20%. Aging is referred as protumorigenic state, and it plays a significant role in cancer development. Methods Molecular subtype of lung cancer was identified by consensus cluster analysis. Prognostic signature was constructed using LASSO cox regression analysis. CeRNA network was constructed to explore lncRNA-miRNA-mRNA regulatory axis. Results A total of 27 differentially expressed aging-related genes (ARGs) were obtained in LUAD. Three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy were identified. We also developed an aging-related prognostic signature that had a better performance in predicting the1-year, 3-year, and 5-year overall survival of LUAD. Further analysis suggested a significant correlation between prognostic signature gene expression and clinical stage, immune infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also identified the lncRNA UCA1/miR-143-3p/CDK1 regulatory axis in LUAD. Conclusion Our study identified three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy. We also developed an aging-related prognostic signature that had a good performance in the prognosis of LUAD.