Outcome Of Lung Adenocarcinoma Patients Research Articles

Effects of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of immune checkpoint inhibitors among patients with lung adenocarcinoma.

This study aimed to identify the associations between individual KRAS, STK11, KEAP1, or TP53 mutations, as well as the comutation status of these genes, and the tumor mutation burden (TMB) with clinical outcomes of lung adenocarcinoma patients treated with immune checkpoint inhibitors (ICIs). We collected data from patients with lung adenocarcinoma treated with ICIs from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database between June 2019 and August 2023. The main endpoints were the treatment response and overall survival (OS). Among 343 patients with lung adenocarcinoma, 61 (18%), 69 (20%), 41 (12%), and 222 (65%) patients had KRAS, STK11, KEAP1, and TP53 mutations, respectively. An overall objective response was observed in 94 of 338 patients (28%), including 2 (1%) who achieved a complete response and 92 (27%) who achieved a partial response. Patients with STK11, KEAP1, or TP53 mutations had a significantly greater TMB (P<0.001). According to the univariate analysis, the treatment response was significantly correlated with TP53 mutation in both the general (P = 0.041) and KRAS wild-type (P = 0.009) populations. KEAP1 and TP53 mutations were associated with worse OS among assessable patients (hazard ratio (HR) = 2.027, P = 0.002; HR = 1.673, P = 0.007, respectively) and among patients without KRAS mutations (HR = 1.897, P = 0.012; HR = 1.908, P = 0.004, respectively). According to the multivariate analysis, KEAP1 (HR = 1.890, P = 0.008) and TP53 (HR = 1.735, P = 0.011) mutations were found to be independent factors for OS. STK11, KEAP1, and TP53 mutations are significantly associated with a high TMB. TP53 mutation could affect the treatment response to some degree, and both KEAP1 and TP53 mutations resulted in inferior OS in the general patient population and in those with KRAS-wild-type lung adenocarcinoma, indicating that KEAP1 and TP53 mutations might act as prognostic factors for ICI treatment in lung adenocarcinoma patients.

Systematic screening of synthetic organochalcogen compounds with anticancer activity using human lung adenocarcinoma spheroids

Lung adenocarcinoma stands as a leading global cause of cancer-related fatalities, with current therapeutic approaches remaining unsatisfactory. Given the association between elevated oxidative markers and the aggressive nature of cancer cells (including multidrug resistance and metastatic potential) that can predict poor outcome of lung adenocarcinoma patients, any compounds that interfere with their aberrant redox biology should be rationally explored as innovative intervention strategies. This study was designed to screen potential anticancer activities within nine newly synthesized organochalcogen – compounds characterized by the presence of oxygen, sulfur, or selenium elements in their structure and exhibiting antioxidant activity – and systematically evaluated their performance against cisplatin, the cornerstone therapeutic agent for lung adenocarcinoma. Our methodology involved the establishment of optimal conditions for generating single tumor spheroids using A549 human lung adenocarcinoma cell line. The initiation interval for spheroid formation was determined to be four days in vitro (DIV), and these single spheroids demonstrated sustained growth over a period of 20 DIV. Toxic dose-response curves were subsequently performed for each compound after 24 and 48 h of incubation at the 12th DIV. Our findings reveal that at least two of the synthetic organochalcogen compounds exhibited noteworthy anticancer activity, surpassing cisplatin in key parameters such as lower LD (Lethal Dose) 50, larger drug activity area, and maximum amplitude of effect, and are promising drugs for futures studies in the treatment of lung adenocarcinomas. Physicochemical descriptors and prediction ADME (absorption, distribution, metabolism, and excretion) parameters of selected compounds were obtained using SwissADME computational tool; Molinspiration server was used to calculate a biological activity score, and possible molecule targets were evaluated by prediction with the SwissTargetPrediction server. This research not only sheds light on novel avenues for therapeutic exploration but also underscores the potential of synthetic organochalcogen compounds as agents with superior efficacy compared to established treatments.

Abstract 5958: Exploring AKR1C3 as a therapeutic target to overcome erlotinib resistance in lung adenocarcinoma

Abstract Introduction: Erlotinib has demonstrated promising efficacy in lung adenocarcinoma (LUAD) patients with epidermal growth factor receptor mutation. However, the development of resistance to erlotinib poses a significant challenge in achieving long-lasting treatment responses. Aldo-keto reductase family 1 member C3 (AKR1C3) is associated with tumor progression, drug resistance, and unfavorable prognosis. This study aims to assess the potential of AKR1C3 as a therapeutic target for overcoming erlotinib resistance in LUAD. Methods: AKR1C3 expression was assessed in paired tumor tissues from 100 LUAD patients using reverse transcription quantitative polymerase chain reaction. Both in vitro and in vivo experiments were categorized into four treatment groups: a saline control (CTR), erlotinib (ER), an AKR1C3 inhibitor 3-(4-trifluoromethyl)phenylamino)benzoic acid (BA), and the combination of erlotinib with the AKR1C3 inhibitor (COM). An erlotinib-resistant cell line was established from the LUAD cell line HCC4006. The MTS assay was used to evaluate cell proliferation and viability. The combined effect of erlotinib and the AKR1C3 inhibitor was assessed using Synergyfinder and CompuSyn calculation software. Both cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) models of erlotinib resistance to LUAD were established in NSG mice. The mice received daily oral gavage administration of the drugs for 31 days, while tumor volume was monitored biweekly. Results: AKR1C3 expression was significantly upregulated by more than three-fold in LUAD tumor samples compared to normal counterparts (p &amp;lt; 0.0001). After 24 hours of treatment in erlotinib-resistant cells, the ER and BA groups exhibited inhibition rates of 7% and 9%, respectively, while the COM group demonstrated a substantially higher inhibition rate of 45%. Synergyfinder and CompuSyn analysis revealed a synergy score of 12 and a combination index of 0.5 for the COM group, indicating a robust synergistic effect between the two drugs. In the CDX model, the COM group exhibited a tumor volume reduction of 25% in erlotinib-resistant cells and 64% in parental cells compared to the CTR group. In the PDX model, the COM group displayed a significant reduction of 50.4% in tumor volume compared to the CTR group. There was no significant difference in the weight of body and internal organs among the four treatment groups in both xenograft models. Conclusions: AKR1C3 overexpression was observed in the tumor tissues of LUAD patients. The combination of erlotinib and AKR1C3 inhibitor showed promising efficacy in overcoming erlotinib resistance in both in vitro and in vivo experiments. These findings highlight AKR1C3 as a potential therapeutic target for managing erlotinib resistance in LUAD patients. Targeting AKR1C3 represents a novel approach to overcome resistance and enhance treatment outcomes in LUAD patients undergoing erlotinib therapy. Citation Format: William C. Cho, Kwan P. Li, Chi F. Wong, King Y. Fung, James C. Chow, Ka M. Cheung, Jeffrey C. Chan, Eunice Y. Lau. Exploring AKR1C3 as a therapeutic target to overcome erlotinib resistance in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5958.

Abstract 1712: Identifying a novel alternative promoter usage in HDAC9: Implications for LUAD prognosis and therapy

Abstract Promoters are key elements in gene regulation. It has been estimated that more than half of protein-coding genes in the human genome possess multiple promoters. Aberrant expression and dysregulated activity of alternative promoters have been correlated with numerous diseases, especially cancer. In this study, we utilized RNA-seq data from 608 lung adenocarcinoma (LUAD) patients and systematically analyzed promoter activities using the proActiv algorithm. Of the expressed genes in LUAD, approximately 57% have at least two distinct promoters. In addition, we identified 1852 tumor-specific alternative promoters that showed a significant shift (2-fold changes) in promoter activity without any notable changes in the expression of corresponding 1603 genes between tumor and adjacent non-tumor tissues. Functional enrichment analysis showed that these genes were associated with key cellular functions such as signaling pathways, cell cycle, and cell junctions. Unsupervised clustering of patients based on altered promoters revealed a significant prognostic value. Among them, we observed a major promoter switch in HDAC9, which mainly utilizes three promoters: prmt3280, prmt3283, and prmt3287 in our LUAD dataset. Notably, prmt3287 was exclusively active in tumor samples and functioned as the dominant promoter, while prmt3283 served as the main promoter in adjacent non-tumor samples. Intriguingly, even though the overall expression of HDAC9 mRNA lacked prognostic relevance both in our dataset and in the TCGA dataset, higher expression of the alternative promoter prmt3287 (using ≥50% activity level as a cutoff) was associated with adverse outcomes in LUAD patients. We further compared the DNA methylation level around the HDAC9 domain between paired tumor and non-tumor samples and found that positions with the most significant differences (p&amp;lt;0.001) overlapped with the prmt3287 region. Moreover, DNA methylation levels around prmt3287 significantly decreased in prmt3287 high-activity patient groups. Next, we investigated the ChIP-seq levels over the HDAC9 region, and ChIPseeker successfully annotated the alternative promoters of HDAC9. In addition, the alternative promoter prmt3287 exhibited the highest active enhancer signals. Furthermore, we performed PCR to determine the expression of six distinct transcript isoforms from alternative promoters in four different LUAD cell lines, and we observed high expression of isoform 10, which is possibly generated by prmt3287 in all cell lines. The impact of prmt3287 on cell proliferation, cell cycle, and cell migration will be further addressed. Our results indicate that alternative promoter usage can capture insights that might be missed at the overall gene expression level, and alternative promoter usage in HDAC9 could be a novel biomarker and a promising therapy target. Citation Format: Amina Bolatkan, Ken Asada, Syuzo Kaneko, Masaaki Komatsu, Ryuji Hamamoto. Identifying a novel alternative promoter usage in HDAC9: Implications for LUAD prognosis and therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1712.

NME6 as a potential biomarker and therapeutic target involved in immune infiltration for lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a prevalent form of lung cancer, is characterized by its high global mortality rate. Previous studies have demonstrated the significance of Nucleoside diphosphate kinase (NME) in various cancers; however, the specific role of NME6 in LUAD remains inadequately understood. This research aims to enhance our understanding of LUAD by investigating the expression level, epigenetic mechanism, signaling activities, and immune infiltrating characteristic immune cells of NME6 in patients. The NME6 expression was explored between LUAD and normal tissue samples using GEPIA, UALCAN and HPA databases. The survival analysis was performed by Kaplan-Meier plotter. The Shiny Methylation Analysis Resource Tool was employed to examine the methylation characteristics of NME6. The Tumor Immune Single-cell Hub (TISCH) and CIBERSORT algorithm were utilized to analyze immune infiltrating characteristic immune cells between NME6 high- and low-expression group in LUAD. According to GEPIA, UALCAN, and HPA databases, NME6 is highly expressed in LUAD compared to normal tissues. At the same time, elevated levels of NME6 were found to be significantly correlated with inferior overall survival outcomes in LUAD patients. Subsequently, the top 10 genes interacted with NME6 were mainly involved in seven pathways, such as p53 signaling pathway, glutathione metabolism, thiamine metabolism, metabolic pathways, and drug metabolism. Notably, NME6 methylation in LUAD samples was lower than in normal samples. The methylation of cg04625862 has a significant impact on the regulation of NME6 expression in LUAD. Furthermore, high NME6 expression in LUAD was associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as Macrophage M2, activated mast cell, and neutrophil. Moreover, NME6 regulated the expression of m6A modification of genes related to LUAD, including METTL3, WTAP, RBM15B, METTL14, RBMX, VIRMA, YTHDC1, RBM15, ZC3H13, YTHDF1, YTHDC2, IGF2BP2, YTHDF3, HNRNPA2B1, YTHDF2, HNRNPC, FTO, and ALKBH5. The analysis showed that NME6 is a crucial prognostic factor for LUAD patients. NME6 regulates genes related to m6A modification and immune cells infiltration. Furthermore, NME6 could sever as a potential therapeutic target for LUAD.

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.

DCAF13 inhibits the p53 signaling pathway by promoting p53 ubiquitination modification in lung adenocarcinoma

BackgroundLung cancer is a malignant tumor with the highest mortality worldwide. Abnormalities in the ubiquitin proteasome system are considered to be contributed to lung cancer progression with deleterious effects. DDB1 and CUL4 associated factor 13 (DCAF13) is a substrate receptor of the E3 ubiquitin ligase CRL4, but its role in lung cancer remains unknown. In this study, we aimed to investigate the regulatory mechanisms of DCAF13 in lung adenocarcinoma (LUAD).MethodsSo as to investigate the effect of DCAF13 on lung adenocarcinoma cell function using in vivo and in vitro. Mechanistically, we have identified the downstream targets of DCAF13 by using RNA-sequencing, as well as ubiquitination assays, co-immunoprecipitation, immunofluorescence, immunohistochemistry and chromatin immunoprecipitation - qPCR experiments.ResultsOur findings reveal that DCAF13 is a carcinogenic factor in LUAD, as it is highly expressed and negatively correlated with clinical outcomes in LUAD patients. Through RNA-sequencing, it has been shown that DCAF13 negatively regulates the p53 signaling pathway and inhibits p53 downstream targets including p21, BAX, FAS, and PIDD1. We also demonstrate that DCAF13 can bind to p53 protein, leading to K48-linked ubiquitination and degradation of p53. Functionally, we have shown that DCAF13 knockdown inhibits cell proliferation and migration. Our results highlight the significant role of DCAF13 in promoting LUAD progression by inhibiting p53 protein stabilization and the p53 signaling pathway. Furthermore, our findings suggest that high DCAF13 expression is a poor prognostic indicator in LUAD, and DCAF13 may be a potential therapeutic target for treating with this aggressive cancer.ConclusionsThe DCAF13 as a novel negative regulator of p53 to promote LUAD progression via facilitating p53 ubiquitination and degradation, suggesting that DCAF13 might be a novel biomarker and therapeutical target for LUAD.

The mechanism of action of myricetin against lung adenocarcinoma based on bioinformatics, in silico and in vitro experiments.

Myricetin is a natural flavonoid with anti-cancer and anti-inflammatory effects, but its mechanism for treating lung adenocarcinoma (LUAD) remains unclearly. Therefore, bioinformatics, in silico and in vitro experiments were employed to elucidate this issue in this study. The core targets of myricetin against LUAD were screened by PharmaMapper (v2017), Assistant for Clinical Bioinformatics, STRING (v11.5) and Cytoscape (v3.8.1). Using Kaplan-Meier Plotter (v2022.04.20), UALCAN (v2021.12.13) and GEPIA (v2.0) databases, the correlation between core genes and the prognosis of LUAD patients were analyzed, and the expression levels of core genes were verified. In silico studies were used to analyze the binding energies and sites of myricetin with core genes. The effects of myricetin on H1975 cells were explored through thiazolyl blue (MTT), cell migration, colony formation and western blot assays. A total of 72 potential targets of myricetin against LUAD were identified through bioinformatics. Among the four core targets obtained by multiple networks and in silico assays, the up-regulated MMP9 (HR = 1.14 (1-1.29), logrank P = 0.046) and down-regulated PIK3R1 (HR = 0.58 (0.51-0.66), logrank P < 1E-16) were positively correlated with poor survival outcomes in LUAD patients. In vitro experiments demonstrated that myricetin inhibited the proliferation and migration of H1975 cells, promoting their apoptosis. Myricetin inhibits the proliferation of H1975 cells and induces cell apoptosis through its influence on the expression levels of MMP1, MMP3, MMP9, and PIK3R1 and regulating the multiple pathways these genes participate in. Both MMP9 and PIK3R1 are potential biomarkers for LUAD.

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma.

This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD). Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD. A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients. The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.

Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma

Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients’ individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.

POLRMT over-expression is linked to WNT/beta-catenin signaling, immune infiltration, and unfavorable outcomes in lung adenocarcinoma patients.

Mitochondrial RNA polymerase (POLRMT) is essential for the expression of mitochondrial genes. In recent studies, POLRMT expression promoted non-small cell cancer cell proliferation in cell lines and xenografts. The present study investigated the impact of POLRMT expression and function on lung adenocarcinoma (LUAD) patients. Multi-omics data (genomics, transcriptomics, and proteomics) from publicly available databases were used to assess the role of POLRMT expression and function in LUAD. These findings were further verified using cancer tissues from clinical samples. POLRMT was over-expressed in LUADs, with mutation frequencies ranging from 1.30% to 5.71%. Over-expression of POLRMT was associated with an abnormal clinicopathological condition resulting in a decreased lifespan. Furthermore, gene sets enrich analysis revealed that POLRMT expression was linked to WNT/beta-catenin signaling; the expression of downstream target genes was positively correlated with POLRMT expression. Also, POLRMT expression was positively correlated with immunosuppressive genes, thereby affecting immune infiltration. POLRMT is over-expressed in LUAD, thereby impacting patient survival. It is also involved in WNT/beta-catenin signaling and may affect tumor infiltration.

Revealing distinct immune landscape in EGFR mutant and EGFR wild type lung adenocarcinoma by using single-cell RNA sequencing.

e21008 Background: Non-small cell lung cancer (NSCLC) with EGFR mutations displays heterogeneity in the extent of intratumoral immunocytes infiltration compared to those with EGFR wild types, which may explain their variable responsiveness to PD-1 blockade. Here, we performed single-cell RNA sequencing (scRNA-seq) to unveil the immunescape and heterogeneity of lung adenocarcinoma (LUAD) between EGFR mutant (EGFR-mut) patients and wild type (EGFR-wt) patients. Methods: Eleven EGFR-mut and 7 EGFR-wt NSCLC tumor samples were analyzed. Immune cells (CD45+) were enriched through fluorescence-activated cell sorting (FACS) and subjected to single-cell RNA sequencing (scRNA-seq) using the 10X Genomics platform. Sequencing reads were normalized and analyzed using the R/Seurat package. Cellular components of each sample were determined based on known marker genes. Wilcox test was used to compare promotion by subtype composition and immune cell infiltrates. Results: Different immune cell compositions were observed between EGFR-mut and EGFR-wt LUAD tumor samples. We found EGFR-wt tumors are marked by an enrichment of CCL15+ tumor-associated macrophages(TAMs), which display pro-inflammatory properties and high expressions of TNF-a-signaling genes. In contrast, EGFR-mut tumors exhibit enrichment of SPARCL1+TAMs, which are involved in epithelial-mesenchymal transition (EMT) and angiogenesis. EGFR-wt tumors exhibited enrichment of CD8-Teff-MX1 and Gamma delta T cells, both of which displayed higher cytotoxic and naive features. Moreover, the high expression of Gamma delta T corresponds with a better clinical prognosis. In contrast, CD8-Teff-XCL1 and NKT-FGFBP2 were found enriched in EGFR-mut tumors, both of which contributed to higher exhaustion and decreased cytotoxic function. Additionally, NK-IL32 was found enriched in EGFR-mut tumors, showing a downregulated T cell receptor signaling and stronger exhausted features. The high expression of this subcluster correlated with poor outcomes in LUAD patients harboring EGFR mutation. Conclusions: Our study provided a deeper understanding of the immune landscape of EGFR-mut and EGFR-wt LUAD and gained better insights into the immunological mechanisms involved in the response to ICIs. The results of our study could be instructive for the development of future immunotherapy strategies for NSCLC patients harboring EGFR mutations.

Systematic integration of machine learning algorithms to develop immune escape-related signatures to improve clinical outcomes in lung adenocarcinoma patients.

Immune escape has recently emerged as one of the barriers to the efficacy of immunotherapy in lung adenocarcinoma (LUAD). However, the clinical significance and function of immune escape markers in LUAD have largely not been clarified. In this study, we constructed a stable and accurate immune escape score (IERS) by systematically integrating 10 machine learning algorithms. We further investigated the clinical significance, functional status, TME interactions, and genomic alterations of different IERS subtypes to explore potential mechanisms. In addition, we validated the most important variable in the model through cellular experiments. The IERS is an independent risk factor for overall survival, superior to traditional clinical variables and published molecular signatures. IERS-based risk stratification can be well applied to LUAD patients. In addition, high IERS is associated with stronger tumor proliferation and immunosuppression. Low IERS exhibited abundant lymphocyte infiltration and active immune activity. Finally, high IERS is more sensitive to first-line chemotherapy for LUAD, while low IERS is more sensitive to immunotherapy. In conclusion, IERS may serve as a promising clinical tool to improve risk stratification and clinical management of individual LUAD patients and may enhance the understanding of immune escape.

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma.

Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment. Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature. Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p < 0.001, p = 0.026, and p < 0.01). Using receiver operating characteristic (ROC) curve, we tested the sensitivity combined with the specificity of the model and found that the risk model had a satisfactory level of 1-year, 3-year, and 5-year concordance index (C-index) (C = 0.661 in the training group, C = 0.679 in validation group 1, C = 0.692 in validation group 2). We also explored the immune microenvironment and immune checkpoint correlation of the risk model and found some significant results. Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.

Human Papillomavirus Oncoproteins Confer Sensitivity to Cisplatin by Interfering with Epidermal Growth Factor Receptor Nuclear Trafficking Related to More Favorable Clinical Survival Outcomes in Non-Small Cell Lung Cancer.

High-risk human papillomavirus (HPV) infections and epidermal growth factor receptor (EGFR) expression have been reported to be associated with more favorable survival outcomes in lung adenocarcinoma patients. In this study, we utilized transfected HPV 16E5/16E6/16E7 H292 cells to investigate the mechanism of HPV oncoproteins interfering with EGFR nuclear trafficking related to a better response to cisplatin. Furthermore, we correlated HPV 16E6/18E6 expression and differentially localized EGFR expression with the clinical association and survival impact in lung adenocarcinoma patients. Our results found significantly higher phosphorylated nuclear EGFR expression upon epidermal growth factor stimulus and better responses to cisplatin in transfected HPV 16E5/16E6/16E7 NCI-H292 cells and xenograft animal models. Our data were compatible with clinical results of a high correlation of HPV 16E6/18E6 and EGFR expression in non-small cell lung cancer tissues and the synergistic effects of both with the best survival prognosis in a lung adenocarcinoma cohort, especially in patients with older age, no brain metastasis, smoking history, and wild-type EGFR status. Cumulatively, our study supports HPV 16E5/16E6/16E7 oncoproteins interfering with EGFR nuclear trafficking, resulting in increased sensitivity to cisplatin. HPV 16E6/18E6 and EGFR expression serve as good prognostic factors in lung adenocarcinoma patients.

Genetic Variants in Histone Modification Regions Predict Clinical Outcomes of Pemetrexed Chemotherapy in Lung Adenocarcinoma

Objective: This study was conducted to investigate the association between genetic variants in histone modification regions and clinical outcomes of PEM chemotherapy in patients with lung adenocarcinoma. Methods: Potentially functional SNPs were selected using integrated analysis of ChIP-seq and RNA-seq. The associations of 279 SNPs with chemotherapy response and overall survival (OS) were analyzed in 314 lung adenocarcinoma patients who underwent PEM chemotherapy. Results: Among the SNPs investigated, 18 were significantly associated with response to chemotherapy, while 28 with OS. Of these SNPs, rs549794A>G in an enhancer which is expected to regulate the expression of ribosomal protein S3 (RPS3) gene was significantly associated with both worse response to chemotherapy and worse OS (adjusted odds ratio = 0.59, 95% CI = 0.36–0.97, p = 0.04; adjusted hazard ratio = 1.44, 95% CI = 1.09–1.91, p = 0.01, respectively). Previous studies suggested that RPS3, a multi-functional protein with various extraribosomal activities, may play a role in chemotherapy resistance. Therefore, it is postulated that rs549794-induced change in the expression level of RPS3 may affect the response to PEM chemotherapy and consequently the survival outcomes in lung adenocarcinoma patients. Conclusion: This study suggests that genetic variants in the histone modification regions may be useful for the prediction of clinical outcomes of PEM chemotherapy in advanced lung adenocarcinoma.

Comprehensive analysis reveals the potential value of inflammatory response genes in the prognosis, immunity, and drug sensitivity of lung adenocarcinoma

BackgroundAlthough the relationship between inflammatory response and tumor has been gradually recognized, the potential implications of of inflammatory response genes in lung adenocarcinoma (LUAD) remains poorly investigated.MethodsRNA sequencing and clinical data were obtained from multiple independent datasets (GSE29013, GSE30219, GSE31210, GSE37745, GSE42127, GSE50081, GSE68465, GSE72094, TCGA and GTEx). Unsupervised clustering analysis was used to identify different tumor subtypes, and LASSO and Cox regression analysis were applied to construct a novel scoring tool. We employed multiple algorithms (ssGSEA, CIBERSORT, MCP counter, and ESTIMATE) to better characterize the LUAD tumor microenvironment (TME) and immune landscapes. GSVA and Metascape analysis were performed to investigate the biological processes and pathway activity. Furthermore, ‘pRRophetic’ R package was used to evaluate the half inhibitory concentration (IC50) of each sample to infer drug sensitivity.ResultsWe identified three distinct tumor subtypes, which were related to different clinical outcomes, biological pathways, and immune characteristics. A scoring tool called inflammatory response gene score (IRGS) was established and well validated in multiple independent cohorts, which could well divide patients into two subgroups with significantly different prognosis. High IRGS patients, characterized by increased genomic variants and mutation burden, presented a worse prognosis, and might show a more favorable response to immunotherapy and chemotherapy. Additionally, based on the cross-talk between TNM stage, IRGS and patients clinical outcomes, we redefined the LUAD stage, which was called ‘IRGS-Stage’. The novel staging system could distinguish patients with different prognosis, with better predictive ability than the conventional TNM staging.ConclusionsInflammatory response genes present important potential value in the prognosis, immunity and drug sensitivity of LUAD. The proposed IRGS and IRGS-Stage may be promising biomarkers for estimating clinical outcomes in LUAD patients.

Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.

This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients. A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis. The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7-88.7), followed by M3OS (AUC 0.84, CI 82.9-84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4-77.9, CI 74.6-78, respectively). Predictions of M4OS (hazard ratio (HR) -2.4, CI -2.47 to -1.64, p < 0.05) and M3OS (HR -2.36, CI -2.79 to -1.93, p < 0.05) were independently associated with OS. ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy. • Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma. • Highly accurate survival predictions are achieved in lung adenocarcinoma patients despite heterogenous histologies and treatment regimens. • Radiomic machine learning models are able to predict lung adenocarcinoma tumor grade and histologic growth pattern risk with satisfactory accuracy.

Identification of metastasis-related long non-coding RNAs in lung cancer through a novel tumor mesenchymal score

Long non-coding RNAs (lncRNAs) have been proven to play critical roles in epithelial-mesenchymal transition (EMT) and metastasis of lung cancer. However, the biological functions and related mechanisms of lncRNAs are unclear. In addition, the EMT-based prognosis prediction in lung cancer still lacks investigation. Here, we established the methodology of identifying critical metastasis-related lncRNAs using comprehensive datasets of cancer transcriptome, genome and epigenome, and also provided tools for prognosis prediction in lung cancer. Initially, important mesenchymal marker genes were identified to compose the tumor mesenchymal score, which predicted patient prognosis in lung cancer, especially lung adenocarcinoma (LUAD). The score was also correlated with several crucial biological and physiological processes, such as tumor immune and hypoxia. Based on the score, lung cancer patients was classified into epithelial and mesenchymal subtypes, and lncRNAs which exhibited expressional dysregulation, promotor methylation alteration and copy number variation between the two subtypes in LUAD were identified and underwent further prognostic analyses. Finally, we identified 14 lncRNAs as EMT-related and significant biomarkers in prognosis prediction of LUAD. As validation, lncRNA RBPMS-AS1 was proven to be co-expressed with epithelial biomarkers, suppressive for A549 cell migration, invasion and EMT, and also significantly associated with better outcomes of LUAD patients, suggesting the potential of RBPMS-AS1 to serve as a lncRNA epithelial biomarker in metastasis of LUAD. Based on the identified lncRNAs, an EMT-linked lncRNA prognostic signature was further established. Taken together, our study provides robust predictive tools, potential lncRNA targets and feasible screening strategies for future study of lung cancer metastasis.

Characterization of the Oncogenic Potential of Eukaryotic Initiation Factor 4A1 in Lung Adenocarcinoma via Cell Cycle Regulation and Immune Microenvironment Reprogramming.

Simple SummaryLung cancer is a common cancer throughout the world. Despite advanced treatment strategies, the outcome is still poor. Based on the results of the present study, EIF4A1 interacting with EIF4H manipulates cell cycle regulation and immune microenvironment reprogramming in lung adenocarcinoma. The results specify EIF4A1 in lung adenocarcinoma tumorigenesis. Treatment derived from EIF4A1 would be worthy of further investigation.Lung adenocarcinoma (LUAD) is a common type of lung cancer. Although the diagnosis and treatment of LUAD have significantly improved in recent decades, the survival for advanced LUAD is still poor. It is necessary to identify more targets for developing potential agents against LUAD. This study explored the dysregulation of translation initiation factors, specifically eukaryotic initiation factors 4A1 (EIF4A1) and EIF4A2, in developing LUAD, as well as their underlying mechanisms. We found that the expression of EIF4A1, but not EIF4A2, was higher in tumor tissue and associated with poor clinical outcomes in LUAD patients. Elevated expression of EIF4H with poor prognosis may potentiate the oncogenic role of EIF4A1. Functional enrichment analysis revealed that upregulation of EIF4A1 was related to cell cycle regulation and DNA repair. The oncogenic effect of EIF4A1 was further elucidated by Gene Set Variation Analysis (GSVA). The GSVA score of the gene set positively correlated with EIF4A1 was higher in tumors and significantly associated with worse survival. In the meantime, gene set enrichment analysis (GSEA) also indicated that elevated EIF4A1 expression in LUAD patients was associated with a decreased infiltration score for immune cells by reducing anticancer immune cell types and recruiting immunosuppressive cells. Consistent with the results, the GSVA score of genes whose expression was negatively correlated with EIF4A1 was lower in the tumor tissue of LUAD cases with worse clinical outcomes and was strongly associated with the disequilibrium of anti-cancer immunity by recruiting anticancer immune cells. Based on the results from the present study, we hypothesize that the dysregulation of EIF4A1 might be involved in the pathophysiology of LUAD development by promoting cancer growth and changing the tumor immune microenvironment. This can be used to develop potential diagnostic biomarkers or therapeutic targets for LUAD.