A tumor Microenvironment-Derived Prognostic Model Guides IL-27 as a Therapeutic Strategy to Restore T Cell Immunity in Lung Adenocarcinoma.

BackgroundPrevious studies found that lung adenocarcinomas (LUAD) with EGFR-positive and ALK-positive were less responsive to immunotherapy, which may be associated with a suppressive tumor immune microenvironment (TIME). Given the discordance...

This study aimed to investigate the key genes and immune microenvironment involved in different TNM stages of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The gene expression and clinical characteristics data were downloaded from the genomic data commons (GDC) database. After initial data processing, the characteristics of the immune microenvironment were analyzed. The differentially expressed genes (DEGs) in tumor vs. normal, and in early vs. advanced stages were screened, followed by Spearman correlation test for tumor infiltrating immune cells (TIICs) to identify immune-related genes. Finally, functional enrichment, protein-protein interaction, and survival analyses were performed. In LUAD, early stage was with higher immune scores, greater number of memory B cells and M0 macrophages compared to advanced stage. M0 and M2 macrophages, and resting memory CD4+ T cells accounted for a large proportion of TIICs in LUAD. The abundance of M0 macrophage infiltration was significantly correlated with the TNM stage and survival. In LUSC, early stage was with higher cytolytic activity and neoantigen burden compared to advanced stage. M0 and M2 macrophages, and plasma cells accounted for a large proportion of TIICs in LUSC. The abundance of resting and activated mast cells was significantly correlated with TNM stage, while resting dendritic cells, eosinophils, activated memory CD4 T cells, and mast cells were significantly correlated with prognosis. Tumor mutation burden analysis revealed that the median of variants per sample decreased from stage I to IV in LUAD, while it increased in LUSC. Further, 83 and 9 immune-related DEGs were identified in LUAD and LUSC, respectively, of which 23 genes in LUAD and 2 genes in LUSC correlated with survival. In conclusion, we identified the key genes, and characterized the tumor immune microenvironment in LUAD and LUSC which may provide therapeutic targets for the treatment of NSCLC.

BackgroundReplication factor C subunit 4 (RFC4) is crucial for initiating DNA replication via DNA polymerase δ and ϵ and is overexpressed in various cancers. However, its relationship with the tumor immune microenvironment (TIME), and immunotherapy response in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine whether overexpressed RFC4 impacts survival in patients with LUAD and to explore potential mechanisms of RFC4 in regulating the TIME using integrated bioinformatics.MethodsLUAD gene expression data were downloaded from the Cancer Genome Atlas (TCGA) database and used for exploratory analysis. Differential expression of RFC4 was validated using gene expression data from the Gene Expression Omnibus (GEO). Clinical data with survival information from TCGA and GEO were use to explore and validate the prognostic value of RFC4. The relationship between RFC4 and TIME was studied by Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE). Tumor Immune Dysfunction and Exclusion (TIDE) was used to predict the therapeutic response of RFC4 to immune checkpoint inhibitors. We validated the differential expression of RFC4 in LUAD and adjacent tissues using immunohistochemical staining in a real-world cohort from the Second Affiliated Hospital of Fujian Medical University.ResultsRFC4 was significantly over-expressed in LUAD at both the RNA and protein levels. High RFC4 expression levels were associated with poor prognosis in LUAD, both in TCGA and GEO. High RFC4 levels were significantly associated with immunostimulators and immune cells infiltration in LUAD tissues. Correlation analysis revealed a significant relationship between the RFC4 and ESTIMATE scores. A high RFC4 expression level was associated with a lower TIDE score, indicating a stronger therapeutic response to immunotherapy. Functional prediction of RFC4 suggested that RFC4 mainly participated in DNA replication and repair, and reshaped the TIME.ConclusionsRFC4 proved to be a promising biomarker for tumorigenesis and could effectively predict immunotherapy response in LUAD. RCF4 altered tumor prognosis by reshaping the TIME, and targeted inhibition of RCF4 may be a promising new strategy for treating LUAD.

Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

Lung adenocarcinoma (LUAD) is a leading cause of cancer‐related mortality, with the tumor microenvironment (TME) playing a critical role in its progression. Metabolic reprogramming, particularly lactate accumulation, drives immune suppression within the TME. Utilizing single‐cell RNA sequencing (scRNA‐seq) of 30 LUAD samples, genome‐wide association studies (GWAS) involving 29,863 patients and 55,586 controls, and clinical data from 220 LUAD patients, we identified N‐Myc downstream‐regulated gene 1 (NDRG1) as a key pathogenic gene in LUAD, strongly associated with tumor progression and poor prognosis. Mechanistic studies revealed that NDRG1 stabilizes lactate dehydrogenase A (LDHA) by inhibiting its ubiquitination, thereby enhancing glycolysis and promoting lactate accumulation. This process fosters immune suppression by inducing M2 macrophage polarization, impairing CD8+ T cell function, and upregulating immunosuppressive genes. Furthermore, histone H3K18 lactylation in macrophages exacerbates this immunosuppressive state. Clinically, elevated NDRG1 expression correlates with increased PD‐L1 levels, a higher abundance of immunosuppressive macrophages, and reduced CD8+ T cell infiltration, contributing to immunotherapy resistance. Conversely, low NDRG1 expression is associated with enhanced CD8+ T cell infiltration and improved therapeutic outcomes. Preclinical studies demonstrated targeting NDRG1 suppresses tumor growth, alleviates immune suppression, and boosts anti‐PD‐L1 efficacy. These findings establish NDRG1 as a critical LUAD regulator and a promising immunotherapy target.

Cyclin-dependent kinases (CDKs) play a key role in cell proliferation in lung adenocarcinoma (LUAD). Comprehensive analysis of CDKs to elucidate their clinical significance and interactions with the tumor immune microenvironment is needed. RNA expression, somatic mutation, copy number variation, and single-cell RNA sequencing data were downloaded from public datasets. First, we comprehensively evaluated the expression profile and prognostic characteristics of 26 CDKs in LUAD, and CDK1 was selected as a candidate for further analysis. Then, a systematic analysis was performed to explore the relationships of CDK1 with clinical characteristics and tumor immune microenvironment factors in LUAD. CDK1 was markedly upregulated at both the mRNA and protein level in LUAD. Moreover, overexpression of CDK1 was related to poor clinical outcomes. CDK1 coexpressed genes were mainly involved in the cell cycle, the DNA repair process, and the p53 signaling pathway. In addition, CDK1 expression was found to be correlated with the expression of multiple immunomodulators and chemokines, which participate in activating and suppressing the immune microenvironment. CDK1 expression was also correlated with increased infiltration of numerous immune cells, including CD4+ T cells and M1 macrophages. Patients with high CDK1 expression tended to have a poor response to immunotherapy but were sensitive to multiple chemotherapies and targeted drugs. The MDK-NCL and SPP1-CD44 ligand-receptor pairs were markedly activated in the intercellular communication network. CDK1 was an independent prognostic factor for LUAD and improved the ability to predict overall survival when combined with tumor stage. CDK1 plays an essential role in reshaping the tumor immune microenvironment and might be a prognostic and treatment biomarker in LUAD.

The extracellular matrix (ECM) is a complex and dynamic network of cross-linked proteins and a fundamental building block in multicellular organisms. Our study investigates the impact of genes related to the ECM receptor interaction pathway on immune-targeted therapy and lung adenocarcinoma (LUAD) prognosis. This study obtained LUAD chip data (GSE68465, GSE31210, and GSE116959) from NCBI GEO. Moreover, the gene data associated with the ECM receptor interaction pathway was downloaded from the Molecular Signature Database. Differentially expressed genes were identified using GEO2R, followed by analyzing their correlation with immune cell infiltration. Univariate Cox regression analysis screened out ECM-related genes significantly related to the survival prognosis of LUAD patients. Additionally, Lasso regression and multivariate Cox regression analysis helped construct a prognostic model. Patients were stratified by risk score and survival analyses. The prognostic models were evaluated using receiver operating characteristic curves, and risk scores and prognosis associations were analyzed using univariate and multivariate Cox regression analyses. A core gene was selected for gene set enrichment analysis and CIBERSORT analysis to determine its function and tumor-infiltrating immune cell proportion, respectively. The results revealed that the most abundant pathways among differentially expressed genes in LUAD primarily involved the cell cycle, ECM receptor interaction, protein digestion and absorption, p53 signaling pathway, complement and coagulation cascade, and tyrosine metabolism. Two ECM-associated subtypes were identified by consensus clustering. Besides, an ECM-related prognostic model was validated to predict LUAD survival, and it was associated with the tumor immune microenvironment. Additional cross-analysis screened laminin subunit beta 1 (LAMB1) for further research. The survival time of LUAD patients with elevated LAMB1 expression was longer than those with low LAMB1 expression. Gene set enrichment analysis and CIBERSORT analyses revealed that LAMB1 expression correlated with tumor immune microenvironment. In conclusion, a prognostic model of LUAD patients depending on the ECM receptor interaction pathway was constructed. Screening out LAMB1 can become a prognostic risk factor for LUAD patients or a potential target during LUAD treatment.

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear.Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample.Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105–1.240, p < 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 (GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy.Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.

Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD). We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments. Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells. Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

SLC16A3 is considered to affect the malignant progression of lung adenocarcinoma (LUAD), but its mechanism remains elusive. Lactate secretion can facilitate the M2 polarization of macrophages, which are essential components of the tumor immune microenvironment (TIME). Based on the Cancer Genome Atlas (TCGA) database, differential expression analysis of SLC16A3 in LUAD was undertaken and the Pearson correlation analysis was on SLC16A3 and targets of M2 macrophages. Pathway enrichment analysis on SLC16A3 was achieved by utilizing the gene set enrichment analysis (GSEA). The expression of SLC16A3 in cells was examined by qPCR and Western blot (WB). The levels of glycolysis marker proteins in cells were tested by WB. The Glucose test kit, lactate test kit, Seahorse energy metabolism analyzer, and pHrodo™ Green AM intracellular indicator reagent kit were applied in assessing cellular glycolysis levels. CCK-8, scratch assay, Transwell assay, and flow cytometry were conducted to evaluate the malignant phenotype and apoptosis level of cancer cells. Flow cytometry and Enzyme-linked immunosorbent assay (ELISA) were utilized to assess the polarization of macrophages. Finally, a mouse model of allograft tumors was created, and the effects of SLC16A3 on glycolysis and M2 polarization of macrophages in vivo were evaluated by tracking tumor growth and detecting related protein distribution through Immunohistochemistry. SLC16A3 was greatly upregulated in LUAD. Knocking down SLC16A3 remarkably repressed the malignant phenotype of LUAD cells and reinforced apoptosis. The results derived from GSEA manifested that SLC16A3 had a higher enrichment in the glycolysis pathway. SLC16A3 positively modulated the extracellular and intracellular levels of lactate and glycolysis. Pearson correlation analysis uncovered a positive linkage between SLC16A3 and M2 macrophage markers. According to the rescue experiment, glycolysis inhibitors were observed to greatly reduce the enhancement in M2 polarization of macrophages caused by overexpression of SLC16A3. The final mouse experiment demonstrated that SLC16A3 boosted tumor growth in vivo and enhanced tumor glycolysis level and M2 macrophage infiltration in the TIME. SLC16A3 in LUAD modulates the glycolysis pathway to facilitate M2 polarization of macrophages.

To identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

Background: Lung cancer remains a major global health burden. RNA-binding proteins (RBPs) play crucial roles in post-transcriptional gene regulation, and their dysregulation is frequently implicated in tumorigenesis. The present study aimed to elucidate the molecular network governed by the highly expressed RBP TIMELESS in lung adenocarcinoma (LUAD) and determine its mechanistic role in LUAD progression. Methods: The Cancer Genome Atlas-LUAD, Gene Expression Omnibus, and single-cell RNA sequencing datasets were analyzed to identify aberrantly expressed RBP genes. The RBP gene TIMELESS exhibited the most significant effect on LUAD cell death and was selected for further study. Photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation sequencing and RNA sequencing were employed to identify ferroptosis-related targets directly bound by TIMELESS. Molecular mechanisms underlying the TIMELESS-mediated regulation of ferroptosis in LUAD were investigated via immunoprecipitation–mass spectrometry, glutathione S-transferase pull-down, immunofluorescence–fluorescence in situ hybridization, RNA immunoprecipitation, poly(A)-tail, and RNA stability assays. In an orthotopic lung cancer mouse model treated with erastin (a ferroptosis inducer) and programmed cell death protein 1 (PD-1) blockade, the role of TIMELESS in therapeutic response was assessed via flow cytometry and multiplex immunofluorescence (mIF). Infiltrating immune cells in LUAD were analyzed by tissue microarrays (TMAs) via mIF. Results: TIMELESS significantly affected LUAD cell proliferation and death, and TIMELESS knockdown significantly enriched RNA-binding and ferroptosis pathways. Transferrin (TF) was identified as a direct TIMELESS target governing ferroptosis. TIMELESS was revealed to bind Ccr4-Not transcription complex subunit 3 (CNOT3) to promote TF mRNA degradation. TIMELESS depletion combined with erastin and PD-1 blockade enhances efficacy, prolongs survival, increases T cell and M1 macrophage infiltration, and reduces M2 macrophage infiltration. Further, high TIMELESS expression was inversely correlated with ferroptosis marker 4-hydroxynonenal but positively correlated with programmed cell death ligand 1 (PD-L1), reduced T cell and M1 macrophage infiltration, and increased M2 macrophage infiltration. Conclusions: TIMELESS recruits CNOT3 to accelerate TF mRNA degradation, thereby suppressing ferroptosis and promoting LUAD growth. These findings suggest that the TIMELESS/TF regulatory axis may be a promising therapeutic target for LUAD.

Background: Recent studies have revealed that SUMOylation modifications are involved in various biological processes, including cancer development and progression. However, the precise role of SUMOylation in lung adenocarcinoma (LUAD), especially in the tumor immune microenvironment, is not yet clear.Methods: We identified SUMOylation patterns by unsupervised consensus clustering based on the expression of SUMOylation regulatory genes. The tumor microenvironment in lung adenocarcinoma was analyzed using algorithms such as GSVA and ssGSEA. Key genes of SUMOylation patterns were screened for developing a SUMOylation scoring model to assess immunotherapy and chemotherapy responses in lung adenocarcinoma patients. Experiments were conducted to validate the differential expression of model genes in lung adenocarcinoma. Finally, we constructed a nomogram based on the SUMOylation score to assess the prognosis of individual lung adenocarcinoma patients.Results: Two patterns of SUMOylation were identified, namely, SUMO-C1, which showed anti-tumor immune phenotype, and SUMO-C2, which showed immunosuppressive phenotype. Different genomic subtypes were also identified; subtype gene-T1 exhibited a reciprocal restriction between the immune microenvironment and stromal microenvironment. High SUMOylation scores were indicative of poor lung adenocarcinoma prognosis. SUMOylation score was remarkably negatively correlated with the infiltration of anti-tumor immune cells, and significantly positively correlated with immune cells promoting immune escape and immune suppression. In addition, patients with low scores responded better to immunotherapy. Therefore, the developed nomogram has a high prognostic predictive value.Conclusion: The SUMOylation patterns can well discriminate the tumor microenvironment features of lung adenocarcinoma, especially the immune cell infiltration status. The SUMOylation score can further assess the relationship between SUMOylation and immune cell crosstalk and has significant prognostic value and can be used to predict immunotherapy and chemotherapy response in patients with lung adenocarcinoma.

BACKGROUND. Outcome of breast cancer (BC) in African American females (AA) remains worse than Caucasian females (CA) after accounting for socioeconomic factors and tumor characteristics. Heterogeneity of tumor immune microenvironment (TIME) composition demonstrated varying roles of infiltrating lymphocytes (TILs) in tumor progression and clinical outcome. We hypothesize that different racial TILs composition impacts downstream tumor signaling and worsens AA BC outcome. METHODS. The Cancer Genome Atlas (TCGA) harmonized RNA-Seq HTSeq and 450K Methylation data were accessed from GDC. Clinical and CIBERSORT immune composition data downloaded from final PANCAN publications. Outcome data included overall survival (OS), disease specific survival (DSS) and disease free interval (DFI). Differential expression and gene set enrichment analysis (GSEA) were performed using R packages limma and ClusterProfiler. FDR corrected p values &amp;lt; 0.05 were considered significant. RESULTS. Of 182 AA and 755 CA, TNBC and Basal subtypes were higher in AA (33% and 34% vs 14% and 13% respectively). OS, DSS, DFI and PFI were worse in AA but did not reach significance. Within TNBC, OS of AA was worse than CA (HR 2.18[1.04-4.56]). Compared to CA, AA had higher TILs, T-Regulatory (Tregs) and T-Follicular Helper cells but lower M2 Macrophages (all p&amp;lt;0.01). Within all IHC subtypes, as well as Basal and LumA PAM50 subtypes, Tregs and Treg:TILs ratios were higher in AA (all p&amp;lt;0.05). TILs predicted favorable OS in CA only (HR 0.11[0.27-0.46] versus 1.26[0.87-18]). Treg, Treg:TILs and Treg:CD8+ ratio predicted worse DSS (HR 1525[1-5e7], 51[1.8-1.4e3], 1.05[1-1.1]) and worse DFI (HR 3241[1.14-9e6], 49[1.78-1.3e3], 1.04[1-1.09]) respectively. On GSEA of differentially expressed genes, multiple pathways were enriched in AA including KEGG (IL-17 signaling), MySigDB Hallmarks [H] (IFN-α and IFN-γ), MySigDB Gene Ontology [C5-BP] (CCR Binding), MySigDB Immunological Signatures [C7] (induced Treg signaling) and REACTOME (IL-10 signaling, NF-kB). Notable CA enriched pathways included KEGG (PI3K-Akt, TGF-β, cGMP-PKG) and MySigDB Hallmarks [H] (Hedgehog, early and late Estrogen responses). T-cell exhaustion markers were higher in AA transcriptome, with higher PDCD1, CTLA4, IDO1 and LAG3 but lower TIM3 expression. After correcting for false discovery, AA had lower mean CpG β-value methylation signature of CTLA4, IDO1 and PDCD1. CONCLUSION. AA had overall more immune infiltrates and different composition of BC TIME compared to CA, with higher immunosuppressive fractions. Tregs were persistently higher across all IHC subtypes, predicted worse outcome and generated higher T-cell exhaustion signature, as well as downstream Treg-specific enriched chemotaxis and activation gene sets in AA compared to CA. These data support the hypothesis of adverse TIME in AA and opens venues of personalized TIME modulation. Citation Format: Ahmed Elkhanany, Eriko Katsuta, Kazuaki Takabe. Detrimental impact of T-regulatory cells on outcome of breast cancer in African American population [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-10-03.

Angiogenesis is one of the hallmarks of cancer. However, the role of molecular subtypes of angiogenesis-associated genes (AAGs) in the tumor immune microenvironment (TIME) of lung adenocarcinoma (LUAD) remains unclear. The expression of AAGs in patients with LUAD were studied. Consensus clustering was performed to identify new AAG-associated molecular subgroups. The TIME and immune status of the subgroups were analyzed. Functional enrichment analysis was performed on the differentially expression genes among the clustered subgroups to analyze their relationship with AAGs. Furthermore, a prognostic risk model and clinical nomogram associated with survival time were constructed. Risk scores of drug sensitivity, immune checkpoint molecules, tumor mutational burden, and tumor cell stemness were analyzed. Finally, a series of in vitro experiments were performed to investigate the role of dickkopf WNT signaling pathway inhibitor 1 (DKK1) in LUAD. Two molecular subgroups with significantly different survival rates and TIME were identified. Immune checkpoint scores were higher in the subgroup with a worse prognosis. Moreover, differentially expressed genes were enriched in cell-cycle regulation, protein metabolism, and the immune microenvironment. The risk model and clinical nomogram constructed based on AAGs accurately predicted the prognosis of patients with LUAD. Patients with high-risk scores were less sensitive to chemotherapy but more sensitive to immunotherapy. DKK1 was highly expressed in basal cells and luminal cells. In addition, the knockdown of DKK1 reduced LUAD cell proliferation, invasion, and migration. Models based on AAGs can play an important role in predicting LUAD prognosis and immunotherapy effects. We further characterized the angiogenesis of TIME and studied the AAG DKK1. Our findings provide a theoretical basis for antitumor strategies targeting angiogenesis.