The Cancer Genome Atlas Lung Adenocarcinoma Research Articles

Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions

BackgroundCancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored. MethodsIn this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models. ResultsWhen applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis, where the normal-derived model performed better. The COM-derived model demonstrated intermediate performance. Validation on three independent test datasets revealed that the COM-derived model showed the best performance, while the normal-derived model showed the worst. In overall survival (OS) analysis, the low-risk group defined by the COM-derived model consistently exhibited longer mean survival times. The tumor-derived model did not consistently show this trend, and the normal-derived model produced opposite results. In discrimination analysis, no significant differences were observed. The COM-derived model demonstrated good discrimination ability for short periods, while the tumor-derived model performed better for longer periods. In calibration analysis, both the COM and tumor-derived models had similar absolute prediction errors, which were better than those of the normal-derived model. However, the tumor-derived model tended to underestimate survival rates. The clinical feature analysis and validation in GSE229705 indicate that the risk score (RS) from the COM model is the most clinically significant. These results demonstrate that the COM model's RS aligns more closely with clinical data, maintaining stable performance and the strongest generalizability. ConclusionsOverall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.

A Prognosis and Immunological Study of Cellular Aging Related lncRNAs in Lung Adenocarcinoma

Background: Recently, cellular senescence has been recognized as a novel hallmark of cancer. However, the mechanisms that control cellular senescence remain poorly understood. This study seeks to discover long non-coding RNAs (lncRNAs) that are linked to senescence and can predict outcomes in lung adenocarcinoma (LUAD) patients. Methods: Using RNA sequencing data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a group of lncRNAs related to senescence was initially identified. Subsequently, through univariate and multivariate Cox regression analyses, a senescence-related lncRNA signature (SenLncSig) linked to the prognosis of LUAD was developed. LUAD patients were categorized into high-risk and low-risk groups based on the median risk score of SenLncSig. Kaplan-Meier analysis was employed to assess differences in overall survival (OS) between these subgroups. Additionally, disparities in Gene Set Enrichment Analysis (GSEA), immune infiltration, scores from the tumor immune dysfunction and exclusion (TIDE) module, and choices of chemotherapy and targeted therapy were also evaluated between the high-risk and low-risk groups. Results: Based on univariate and multivariate Cox regression analyses, we established a prognostic model composed of seven senescence-related lncRNAs, named SenLncSig, which includes AL031775.2, AC026355.1, AL365181.2, AC090559.1, AL606489.1, AL513550.1, and C20orf197. This model divides patients into high-risk and low-risk groups, with the high-risk group showing significantly shorter overall survival compared to the low-risk group, with a hazard ratio (HR) of 1.326. The predictive accuracy of SenLncSig was validated through ROC curves and principal component analysis (PCA). Additionally, we developed an intuitive survival prediction tool that combines SenLncSig with clinical pathological features to assess patients' overall survival (OS). Enrichment analysis (GSEA) revealed that SenLncSig is involved in multiple pathways related to tumor immune modulation. Risk model analysis indicated significant differences between the high and low-risk groups in terms of immune status and responses to immunotherapy, chemotherapy, and targeted therapy. Conclusion: In this study, a lncRNA signature named as SenLncSig was developed that not only identifies the senescence phenotype and predicts prognosis but also has the capability to forecast the response of LUAD to immunotherapy.

NcRNAs-mediated TIMELESS overexpression in lung adenocarcinoma correlates with reduced tumor immune cell infiltration and poor prognosis.

Lung adenocarcinoma (LUAD) has a poor prognosis. Circadian genes such as TIMELESS have been associated with several pathologies, including cancer. The expression of TIMELESS and the relationship between TIMELESS, infiltration of tumors and prognosis in LUAD requires further investigation. In this study, we investigated the expression of TIMELESS and its association with survival across several types of human cancer using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Program. Noncoding RNAs (ncRNAs) regulating overexpression of TIMELESS in lung adenocarcinoma (LUAD) were explored with expression, correlation, and survival analyses. Immune cell infiltration and biomarkers were analyzed between different TIMELESS expression levels. The relationship between TIMELESS expression and immunophenoscores, which were used to predict response to immunotherapy, was evaluated. TIMELESS was identified as a potential oncogene in LUAD. NcRNA analysis showed MIR4435-2HG/hsa-miR-1-3p may interact with TIMELESS in a competitive endogenous RNA network in LUAD tumor tissues. Most immune cells were significantly decreased in TCGA LUAD tumor tissues with high TIMELESS expression except for CD4+T cells and Th2 cells. TIMELESS expression in LUAD tumor tissues was significantly negatively correlated with neutrophil biomarkers, dendritic cell biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C) and an immunophenoscore that predicted outcomes associated with the use of immune checkpoint inhibitors. These findings imply that ncRNAs-mediated TIMELESS overexpression in LUAD tumor tissues correlated with poor prognosis, reduced immune cell infiltration in the tumor microenvironment, and poor response to immune checkpoint inhibitors.

Catalase Expression Is an Independent Prognostic Marker in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the deadliest cancer, and approximately 20% of stage I LUAD cases recur after surgical resection due to its high intratumor heterogeneity. Reactive oxygen species (ROS) have been detected in LUAD and are involved in carcinogenesis and tumor progression. Here, a comprehensive analysis was performed to evaluate the effects of antioxidants on the prognosis of LUAD. The Cancer Genome Atlas (TCGA) database was used to study the relationship of gene expression of different ROS-scavenging enzymes with the progression and prognosis of LUAD. Using TCGA LUAD datasets, we found that catalase (CAT) expression was significantly down-regulated in LUAD tissues compared to normal tissues, CAT down-regulation differed significantly between different grades of LUAD, low CAT expression was independently correlated with a worse prognosis in LUAD, and the expression of the CAT gene was associated with an inhibition of the "cell cycle". A panel of LUAD cells (CL1-0, CL1-1, CL1-3, and CL1-5), which harbored mutated p53 (R248W), with gradually increasing invasiveness showed a gradual decrease in CAT expression. Silencing of CAT upregulated cell growth in A549 cells, which harbor wild-type p53 and show high CAT expression and was associated with an increase in the expression of BUB1B, PLK1, and PKMYT1. Finally, over 38% (186/490) of LUAD cases with a p53 mutation exhibited significantly lower CAT expression than those with wild-type p53. CAT expression is a potent favorable prognostic marker for LUAD and may represent a drug target.

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.

NRIP1 regulates cell proliferation in lung adenocarcinoma cells.

Nuclear receptor interacting protein 1 (NRIP1) is a transcription cofactor that regulates the activity of nuclear receptors and transcription factors. Functional expression of NRIP1 has been identified in multiple cancers. However, the expression and function of NRIP1 in lung adenocarcinoma have remained unclear. Thus, we aimed to clarify the NRIP1 expression and its functions in lung adenocarcinoma cells. NRIP1 and Ki-67 were immunostained in the tissue microarray section consisting of 64 lung adenocarcinoma cases, and the association of NRIP1 immunoreactivity with clinical phenotypes was examined. Survival analysis was performed in lung adenocarcinoma data from The Cancer Genome Atlas (TCGA). Human A549 lung adenocarcinoma cell line with an NRIP1-silencing technique was used in vitro study. Forty-three of 64 cases were immunostained with NRIP1. Ki-67-positive cases were more frequent in NRIP1-positive cases as opposed to NRIP1-negative cases. Higher NRIP1 mRNA expression was associated with poor prognosis in the TCGA lung adenocarcinoma data. NRIP1 was mainly located in the nucleus of A549 cells. NRIP1 silencing significantly reduced the number of living cells, suppressed cell proliferation, and induced apoptosis. These results suggest that NRIP1 participates in the progression and development of lung adenocarcinoma. Targeting NRIP1 may be a possible therapeutic strategy against lung adenocarcinoma.

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.

Deep learning-based approach to predict multiple genetic mutations in colorectal and lung cancer tissues using hematoxylin and eosin-stained whole-slide images.

1549 Background: The presence of genetic mutations is a vital prognostic in many types of cancer. However, genomic testing is expensive and challenging to perform. In contrast, hematoxylin and eosin (H&E) staining is relatively inexpensive and straightforward. Thus, in this study, we propose a method of predicting the presence of genetic mutations using H&E-stained whole-slide images (WSIs). Methods: We divided each H&E–stained WSI into small pieces or “patches.” We used a deep learning model to classify each patch based on the presence of tumor-containing regions. We then extracted image features from each tumor-containing patch using a deep learning-based feature extractor. We created image features for the entire WSI by concatenating the features of the patches. We then trained genetic mutation classification models using the WSI features as the input and the presence or absence of genetic mutations as the output. Finally, we evaluated the performance of these models using the area under the receiver operating characteristic curve (AUC). Results: First, we evaluated our methods using The Cancer Genome Atlas (TCGA) colorectal cancer dataset. We used H&E–stained WSIs and data associated with Microsatellite Instability ( MSI) and BRAF gene mutations, which are directly relevant to therapeutic strategies, obtained from an independent clinical cohort of 566 patients with TCGA colon and rectum adenocarcinoma. We divided the data into training, validation, and test splits, comprising 367, 90, and 109 patients, respectively. We used the training and validation splits for model training and selection, and the test split for model evaluation. The AUC values of the classification models and associated 95% confidence intervals (CIs) were 0.721 (CI = 0.572–0.870) for MSI and 0.712 (CI = 0.547–0.877) for BRAF gene mutations. We also applied our approach to MUC16, KRAS, and ALK mutations using the TCGA lung cancer dataset. We divided 909 TCGA lung adenocarcinoma and lung squamous cell carcinoma patients into training, validation, and test splits, comprising 582, 146, and 181 patients, respectively. In contrast with those of the colorectal dataset, WSI image features were generated using all patches. The AUC values on the test splits were 0.897 (CI = 0.85–0.95) for MUC16, 0.845 (CI = 0.75–0.94) for KRAS, and 0.756 (CI = 0.57–0.94) for ALK mutations. Conclusions: We proposed an approach to predict the presence of genetic mutations using only H&E–stained WSIs and evaluated its performance using colorectal and lung cancer datasets. Our model has the potential to predict the presence of certain genetic mutations with superior performance. These predictions can be used to improve the accuracy of prognostic prediction using WSIs alone.

Integrated Bioinformatics Approach for Disclosing Autophagy Pathway as a Therapeutic Target in Advanced KRAS Mutated/Positive Lung Adenocarcinoma

Background: Lung cancer is the leading cause of cancer-related deaths, accounting for 1.8 million deaths (18%). Nearly 80%-85% of lung cancer cases are non-small cell lung cancers (NSCLC). One of the most frequent genetic mutations in NSCLC is the Kirsten Rat Sarcoma Oncogene Homolog (KRAS) gene mutation. In recent years, autophagy has drawn substantial attention as a potential pathway that can be targeted in cancer driven by KRAS gene mutation to efficiently improve the therapeutic profile of different treatments. Methods: In this study, we have investigated the potential of targeting the autophagy pathway as a treatment approach in advanced KRAS-mutated lung adenocarcinoma using gene expression data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project. Results: Compared to KRAS wild-type lung adenocarcinoma, there were found 11 differentially expressed autophagy-related genes (DEARGs), with 5 upregulated and 6 downregulated DEARGs (threshold of adjusted p-value <0.05). Conclusion: These DEARGs can be investigated as potential genes that can be targeted by different autophagy inhibitors.

PCK2 inhibits lung adenocarcinoma tumor cell immune escape through oxidative stress-induced senescence as a potential therapeutic target.

Our research aimed to better understand how phosphoenolpyruvate carboxykinase 2 (PCK2) is linked to survival outcomes in lung cancer patients. We confirmed PCK2 expression and its association with the outcome of lung cancer patients using The Cancer Genome Atlas (TCGA) database. PCK2 and immune cell connections were investigated using data from the Tumor IMmune Estimation Resource (TIMER) and TCGA repositories. We used the CancerSEA database to examine the links between PCK2 expression and the efficiency of lung adenocarcinomas, and a T-distributed Stochastic Neighbor Embedding (T-SNE) map was constructed to show the expression profile of PCK2 in single cells in TCGA lung adenocarcinoma samples. The potential mechanism of action was finally investigated using Gene Set Enrichment Analysis (GSEA) enrichment analysis, Gene Ontology (GO) pathway enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The expression of PCK was lower in lung adenocarcinoma tumor tissues than in paracancerous tissues. Patients with lung adenocarcinoma who expressed PCK2 at high levels fared better in overall survival (OS), disease-specific survival (DSS), and progression free interval (PFI). PCK2 was positively correlated with programmed cell death 1 (PDCD1) expression, and its mutation rate in lung adenocarcinoma was 0.53%. CancerSEA research revealed that in lung adenocarcinoma, PCK2 was negatively correlated with epithelial-mesenchymal transition (EMT) and hypoxia. Gene ontology and KEGG enrichment analysis revealed PCK2-coexpressed genes influenced the onset and progression of lung adenocarcinoma by modulating the activity of DNA-binding transcriptional activators, the specificity of RNA polymerase II, the interaction between neuroactive ligands and their receptors, and the cAMP signaling pathway. The prognosis for lung adenocarcinoma was shown to vary according to whether PCK2 was involved in the response to oxidative stress-induced senescence, gene silencing, cell cycle, and other biological processes. An increased expression of PCK2 may be employed as a novel prognostic biomarker in patients with lung adenocarcinoma and has been shown to increase OS, DSS, and PFI. Improving the prognosis of lung adenocarcinoma by interference with PCK2 may be possible since it induces senescence through the oxidative stress response and blocks the immune escape of tumor cells. These results point to a probable target anticancer treatment development in lung adenocarcinoma.

Abstract 6556: A gene signature of DNA damage response pathway is predictive of patient clinical outcomes and is associated with tumor immune microenvironment of lung adenocarcinoma

Abstract DNA damage response (DDR) is crucial to prevent the accumulation of DNA lesions that can lead to genomic instability and promote tumorigenesis. Dysregulation of DDR pathways also provides therapeutic opportunities for new cancer therapy. Given that lung cancer displays a high level of genetic mutation and genomic instability, a comprehensive understanding of the dysregulation of DDR genes in lung cancer, especially non-small cell lung cancer (NSCLC) which accounts for 85% of all lung cancer cases, will be critical for identifying new genes contributing to tumorigenesis and novel targets for lung cancer therapy. The goal of this study is to identify novel targets for lung cancer treatment through a comprehensive analysis of DDR genes and its association with patient survival, genomic mutation/instability, tumor immune microenvironment (TIME), and tumor proliferation ability. To this end, we have comprehensively characterized the expression of 276 DDR genes using the Cancer Genome Atlas (TCGA) and other publicly available data of lung adenocarcinoma (LUAD), the most common NSCLC. We found that 36 out of 276 DDR genes are dysregulated with majority being upregulated in tumors and are associated with patient overall survival (OS) and progression free survival (PFS) based on the TCGA LUAD cohort. A gene expression signature, i.e., DDR gene expression dysregulation score (DDRDS), was constructed based on the geometric mean of the differentially expressed genes that were also associated with patient OS. Patients with a higher DDRDS showed significantly worse OS and PFS, more advanced tumor stage, a higher tumor mutational burden (TMB), neoantigen load, and homologous recombination deficiency (HRD) score. In addition, tumors with higher DDRDS displayed an increase in CD4+ activated memory T-cells, CD8+ T- cells, M0 and M1 macrophage, and NK cells, but a decrease in B cells, CD4+ resting memory T-cells, monocytes, and dendritic cells in TIME. Further, these tumors also showed a higher expression of immune check point molecules PD1 and PD-L1. Lastly, gene set enrichment analysis demonstrated that these tumors had significantly increased nuclear division, DNA replication, chromosome segregation, and cell cycles, reflecting the aggressiveness of the disease. Hence, despite the previous studies demonstrating that mutation and deletion defects in DDR genes are frequent in tumors, we found that the dysregulated DDR genes are mainly upregulated, and DDRDS based on the dysregulated expression of DDR genes is associated with the alteration in TMB/HRD/TIME, tumor aggressiveness, and ultimately patient outcomes. These results suggested that patients with a higher tumor DDRDS are associated with poor prognosis but might benefit from immune checkpoint blockade-associated immunotherapy. Citation Format: Xiaoli Zhang, Junran Zhang. A gene signature of DNA damage response pathway is predictive of patient clinical outcomes and is associated with tumor immune microenvironment of lung adenocarcinoma [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 6556.

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes.

DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

Prognostic Significance of Iron Metabolism Related Genes in Human Lung Adenocarcinoma.

Iron metabolism related genes participate in cell proliferation, cell growth, and redox cycling in multiple cancers. Limited studies have revealed the roles and clinical significance of iron metabolism in the pathogenesis and prognosis of lung cancer. A total of 119 iron metabolism related genes were extracted from MSigDB database and their prognostic values were determined in The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset and the Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) database. Immunohistochemistry technique and correlations with immune cell infiltration, gene mutation and drug resistance were used to identify the potential and underlying mechanisms of STEAP1 and STEAP2 as prognostic biomarkers of LUAD. The expression of STEAP1 and STEAP2 are negatively associated with the prognosis of LUAD patients both at the mRNA and protein level. The expression of STEAP1 and STEAP2 was not only negatively correlated with the trafficking degree of CD4+ T immune cells and positively related to most immune cells' trafficking degree, but also significantly associated with gene mutation status, particularly with mutations on TP53 and STK11. Four types of drug resistance showed significant correlation with the expression level of STEAP1 while 13 types of drug resistance were associated with the expression level of STEAP2. Multiple iron metabolism related genes including STEAP1 and STEAP2 are significantly associated with the prognosis of LUAD patients. STEAP1 and STEAP2 might affect the prognosis of LUAD patients partially through immune cell infiltration, gene mutation and drug resistance, which indicated they were independent prognostic factors for LUAD patients.

A Signature Constructed Based on the Integrin Family Predicts Prognosis and Correlates with the Tumor Microenvironment of Patients with Lung Adenocarcinoma.

As an important element in regulating the tumor microenvironment (TME), integrin plays a key role in tumor progression. This study aimed to establish prognostic signatures to predict the overall survival and identify the immune landscape of patients with lung adenocarcinoma based on integrins. The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus datasets were used to obtain information on mRNA levels and clinical factors (GSE72094). The least absolute shrinkage and selection operator (LASSO) model was used to create a prediction model that included six integrin genes. The nomogram, risk score, and time-dependent receiver operating characteristic analysis all revealed that the signatures had a good prognostic value. The gene signatures may be linked to carcinogenesis and TME, according to a gene set enrichment analysis. The immunological and stromal scores were computed using the ESTIMATE algorithm, and the data revealed, the low-risk group had a higher score. We discovered that the B lymphocytes, plasma, CD4+ T, dendritic, and mast cells were much higher in the group with low-risk using the CiberSort. Inflammatory processes and several HLA family genes were upregulated in the low-risk group. The low-risk group with a better prognosis is more sensitive to immune checkpoint inhibitor medication, according to immunophenoscore (IPS) research. We found that the patients in the high-risk group were more susceptible to chemotherapy than other group patients, according to the prophetic algorithm. The gene signatures could accurately predict the prognosis, identify the immune status of patients with lung adenocarcinoma, and provide guidance for therapy.

Cancer/testis antigen HEMGN correlated with immune infiltration serves as a prognostic biomarker in lung adenocarcinoma

HEMGN belongs to the Cancer/testis antigens (CTAs), which are expressed in various types of human cancers and have received particular attention in cancer immunotherapy. However, the potential function of HEMGN involved in lung cancer and the immune response is not yet elucidated. HEMGN expression in lung adenocarcinoma (LUAD) was estimated via the Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN), and Human Protein Atlas databases. The prognostic role of HEMGN was investigated by Gene Expression Profiling Interactive Analysis (GEPIA), PrognoScan, and Kaplan-Meier plotter databases. The associations between HEMGN and clinicopathological parameters were analyzed with UALCAN database. Then, immunohistochemical and Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) analysis were performed to further verify the associations in tissue or serum samples. Serum from patients were detected for HEMGN antibody by enzyme-linked immunosorbent assay (ELISA). Flow cytometry was used to detect immune cell infiltration in peripheral blood of patients with LUAD. In addition, Gene Set Enrichment Analysis (GSEA) was conducted to investigate the functional role of HEMGN. Furthermore, we obtained the somatic mutation data from the TCGA LUAD dataset and analyzed the mutation profiles with “maftools” package. Finally, we evaluated the associations between HEMGN and immune infiltration level and the characteristic markers of immune cells in TIMER, GEPIA, and CIBERSORT. The mRNA and protein expressions of HEMGN were significantly decreased in LUAD patients. High HEMGN expression was remarkably associated with better prognosis in LUAD patients. The concentration levels of anti-HEMGN antibody in LUAD were significantly higher than that in healthy individuals and were closely correlated with clinical stage. In addition, HEMGN was involved in distinct typical genomic alterations in LUAD. GSEA demonstrated that HEMGN was significantly connected with immunity and substance metabolism. Notably, HEMGN was significantly related to immune infiltrates, including B cells, CD8 + T cells, CD4 + T cells, neutrophils, macrophages, dendritic cells (DCs), and various kinds of functional T cells. Furthermore, HEMGN had a significant association with diverse immune gene markers. HEMGN can be considered as a prognostic biomarker of LUAD and is associated with immune infiltration.

Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients.

The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 0.757-0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients.

DNA repair/recombination protein 54L promotes the progression of lung adenocarcinoma by activating mTORC1 pathway.

Lung adenocarcinoma (LUAD) is the most prevalent form of lung cancer and has a poor prognosis. RAD54L is a DNA repair protein upregulated in several cancer types, but its role in LUAD progression remains unclear. The objective of this study was to characterise the molecular pathways that oncogenic RAD54L modulates to drive LUAD progression. The Cancer Genome Atlas (TCGA)‒LUAD dataset was analysed to compare the RAD54L mRNA expression in LUAD tumours to that in normal lung tissue. RAD54L and E2F7 mRNA expression was confirmed in human cancer cell lines using RT-qPCR. Bioinformatics tools were used to predict the target genes and downstream signalling pathways of RAD54L. Proteins related to RAD54L, apoptosis, migration, and the mTORC1 pathway were assessed by Western blotting. Using the TCGA‒LUAD dataset, we found that RAD54L was higher in LUAD tumours compared to that in non-cancerous lung tissue, and RAD54L levels were significantly correlated with pathological TNM stage and unfavourable prognosis in patients with LUAD. RAD54L was ubiquitously upregulated in LUAD cells (NCI-H1975, H1299, H23 and A549). Furthermore, RAD54L silencing decreased cell proliferation, invasion, and migration, and induced cell apoptosis and G1 cell cycle phase arrest in H1299 and H23 human lung cancer cell lines. E2F7 was predicted as a target gene of RAD54L. E2F7 overexpression restored malignant cell behaviour in si-RAD54L-treated H1299 cells. Bioinformatic analysis suggested that the mTORC1 signalling pathway is downstream of RAD54L. Rapamycin treatment impaired RAD54L-mediated malignant cell behaviour in H1299 cells. Additionally, RAD54L promoted the progression of xenograft tumours and metastasis in vivo. In conclusion, the E2F7-RAD54L axis promotes the progression of LUAD through the mTORC1 signalling pathway.

Mesh-free technique for enhancement of the lung CT image

Lung cancer is abnormal cell disease and a leading cause of mortality for both men and women according to Global cancer statistics 2020 (GLOBACON). Pulmonary nodules appear to be one of the first signs of lung cancer which needs to be recognized for early diagnosis. CT images are distorted due to various noises and poor resolution. Image pre-processing plays an important role to get rid of noises and improve image quality. Enhancement of lung CT image facilitates to determine the sign of early stage of lung cancer in terms of tumor size, malignancy, rounded hematoma. A partial differential equation based heat diffusion equation is used in this work for enhancement of lung CT-scan images. Mesh-less based radial basis function technique is used to solve the heat equation. Proposed work employed on randomly selected sample images of two lung dataset named as The Cancer Genome Atlas Lung Adenocarcinoma(TCGA-LUAD) and QIN. The performance of the proposed lung enhancement technique is quantified by various parameters namely, Structure Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Contract Improvement Index (CII). The observational investigations demonstrated that the proposed technique is computationally more efficient as compared to FDM, Median, and CLAHE.

The impact of tertiary lymphoid structures on clinicopathological, genetic and gene expression characteristics in lung adenocarcinoma

IntroductionTertiary lymphoid structures (TLS) are observed in several cancers and are associated with favorable prognosis. This study aimed to examine the clinicopathological, genetic, and gene expression profiles of lung adenocarcinoma patients with TLS. MethodsA total of 112 patients with pathological stage IB lung adenocarcinoma who underwent complete resection between 2011 and 2015 were enrolled in this study. We investigated whether TLS correlated with prognosis and programmed death-ligand 1 (PD-L1) expression. Furthermore, the correlation of TLS with tumor mutation burden (TMB) and genetic mutations was evaluated in patients for whom whole-exon sequencing data were available. In addition, using the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset, gene expression analysis according to the TLS status was performed. ResultsAmong the 112 patients, 49 were TLS-positive (TLS+). TLS+ correlated with longer recurrence-free survival (RFS) than TLS-negative cases (TLS−) (hazard ratio [HR], 0.47; 95 % confidence interval [CI]: 0.23–0.88, p = 0.02). In the multivariate analysis, TLS was a better independent prognostic factor for RFS (HR 0.37, 95 %CI 0.18–0.72, p < 0.01). PD-L1 expression was not significantly different between TLS+ and TLS− patients (p = 0.54). TMB in TLS+ was similar to that in TLS− patients (p = 0.39); however, it tended to be lower than that in TLS− especially among smokers (p = 0.07). In gene expression analysis, RNA expression of chemokines related to lymph node formation, such as CXCL13, CCL19 and CCL21, was significantly higher, and biological processes such as positive regulation of humoral immune response and regulation of antigen receptor-mediated signaling pathway were enhanced in TLS+. ConclusionsTLS was a favorable prognostic factor and was not associated with PD-L1 expression in patients with lung adenocarcainoma. Moreover, gene expression analysis indicated that TLS is a site for the generation and regulation of antitumor immune responses.

A novel senescence-related lncRNA signature that predicts prognosis and the tumor microenvironment in patients with lung adenocarcinoma.

Background: Cellular senescence has recently been considered a new cancer hallmark. However, the factors regulating cellular senescence have not been well characterized. The aim of this study is to identify long non-coding RNAs (lncRNAs) associated with senescence and prognosis in patients with lung adenocarcinoma (LUAD). Methods: Using RNA sequence data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a subset of senescence-related lncRNAs was first identified. Then, using univariate and multivariate Cox regression analyses, a senescence lncRNA signature (LUADSenLncSig) associated with LUAD prognosis was developed. Based on the median LUADSenLncSig risk score, LUAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) in the high- and low-risk score subgroups. Differences in Gene Set Enrichment Analysis (GSEA), immune infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) module score, chemotherapy, and targeted therapy selection were also compared between the high-risk and low-risk groups. Results: A prognostic risk model was obtained consisting of the following nine senescence-related lncRNAs: LINC01116, AC005838.2, SH3PXD2A-AS1, VIMS-AS1, SH3BP5-AS1, AC092279.1, AC026355.1, AC027020.2, and LINC00996. The LUADSenLncSig high-risk group was associated with poor OS (hazard ratio = 1.17, 95% confidence interval = 1.102-1.242; p < 0.001). The accuracy of the model was further supported based on receiver operating characteristic (ROC), principal component analysis (PCA), and internal validation cohorts. In addition, a nomogram was developed consisting of LUADSenLncSig for LUAD prognosis, which is consistent with the actual probability of OS. Furthermore, immune infiltration analysis showed the low-risk group had a stronger anti-tumor immune response in the tumor microenvironment. Notably, the levels of immune checkpoint genes such as CTLA-4, PDCD-1, and CD274, and the TIDE scores were significantly higher in the low-risk subgroups than in high-risk subgroups (p < 0.001). This finding indicates the LUADSenLncSig can potentially predict immunotherapy efficacy. Conclusion: In this study, a lncRNA signature, LUADSenLncSig, that has dual functions of senescence phenotype identification and prognostic prediction as well as the potential to predict the LUAD response to immunotherapy was developed.