Tumor-derived apoptotic extracellular vesicle-mediated intercellular communication promotes metastasis and stemness of lung adenocarcinoma.

Apoptosis has long been recognized as a significant mechanism for inhibiting tumor formation, and a plethora of stimuli can induce apoptosis during the progression and treatment of tumors. Moreover, tumor-derived apoptotic extracellular vesicles (apoEVs) are inevitably phagocytosed by live tumor cells, promoting tumor heterogeneity. Understanding the mechanism by which apoEVs regulate tumor cells is imperative for enhancing our knowledge of tumor metastasis and recurrence. Herein, we conducted a series of in vivo and in vitro experiments, and we report that tumor-derived apoEVs promoted lung adenocarcinoma (LUAD) metastasis, self-renewal and chemoresistance. Mechanistically, we demonstrated that apoEVs facilitated tumor metastasis and stemness by initiating the epithelial-mesenchymal transition program and upregulating the transcription of the stem cell factor SOX2. In addition, we found that ALDH1A1, which was transported by apoEVs, activated the NF-κB signaling pathway by increasing aldehyde dehydrogenase enzyme activity in recipient tumor cells. Furthermore, targeting apoEVs-ALDH1A1 significantly abrogated these effects. Collectively, our findings elucidate a novel mechanism of apoEV-dependent intercellular communication between apoptotic tumor cells and live tumor cells that promotes the formation of cancer stem cell-like populations, and these findings reveal that apoEVs-ALDH1A1 may be a potential therapeutic target and biomarker for LUAD metastasis and recurrence.

SUMOylation-triggered ALIX activation modulates extracellular vesicles circTLCD4-RWDD3 to promote lymphatic metastasis of non-small cell lung cancer.

Lymph node (LN) metastasis is one of the predominant metastatic routes of non-small cell lung cancer (NSCLC) and is considered as a leading cause for the unsatisfactory prognosis of patients. Although lymphangiogenesis is well-recognized as a crucial process in mediating LN metastasis, the regulatory mechanism involving lymphangiogenesis and LN metastasis in NSCLC remains unclear. In this study, we employed high-throughput sequencing to identify a novel circular RNA (circRNA), circTLCD4-RWDD3, which was significantly upregulated in extracellular vesicles (EVs) from LN metastatic NSCLC and was positively associated with deteriorated OS and DFS of patients with NSCLC from multicenter clinical cohort. Downregulating the expression of EV-packaged circTLCD4-RWDD3 inhibited lymphangiogenesis and LN metastasis of NSCLC both in vitro and in vivo. Mechanically, circTLCD4-RWDD3 physically interacted with hnRNPA2B1 and mediated the SUMO2 modification at K108 residue of hnRNPA2B1 by upregulating UBC9. Subsequently, circTLCD4-RWDD3-induced SUMOylated hnRNPA2B1 was recognized by the SUMO interaction motif (SIM) of ALIX and activated ALIX to recruit ESCRT-III, thereby facilitating the sorting of circTLCD4-RWDD3 into NSCLC cell-derived EVs. Moreover, EV-packaged circTLCD4-RWDD3 was internalized by lymphatic endothelial cells to activate the transcription of PROX1, resulting in the lymphangiogenesis and LN metastasis of NSCLC. Importantly, blocking EV-mediated transmission of circTLCD4-RWDD3 via mutating SIM in ALIX or K108 residue of hnRNPA2B1 inhibited the lymphangiogenesis and LN metastasis of NSCLC in vivo. Our findings reveal a precise mechanism underlying SUMOylated hnRNPA2B1-induced EV packaging of circTLCD4-RWDD3 in facilitating LN metastasis of NSCLC, suggesting that EV-packaged circTLCD4-RWDD3 could be a potential therapeutic target against LN metastatic NSCLC.

SERPINB5 is a prognostic biomarker and promotes proliferation, metastasis and epithelial-mesenchymal transition (EMT) in lung adenocarcinoma.

BACKGROUND: Serine protease inhibitors clade B serpins (SERPINBs) are the largest subclass of protease inhibitors, once thought of as a tumor suppressor gene family. However, some SERPINBs exhibit functions unrelated to the inhibition of catalytic activity. METHODS: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Gene Set Cancer Analysis (GSCA), and cBioPortal databases were utilized to investigate SERPINBs expression, prognostic correlation, and genomic variation in 33 cancer types. We also conducted a comprehensive transcriptome analysis in multiple lung adenocarcinoma (LUAD) cohorts to reveal the molecular mechanism of SERPINB5 in LUAD. Then, qPCR and immunohistochemistry were used to verify the expression and prognostic value of SERPINB5 in LUAD patients. Furthermore, knockdown and overexpression of SERPINB5 in LUAD cell lines were performed to evaluate cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT). RESULTS: The expression of SERPINB5 was upregulated and demethylated in LUAD, and its abnormally high expression was significantly correlated with poor overall survival (OS). In addition, the expression of SERPINB5 was analyzed to determine its prognostic value in LUAD and confirmed that SERPINB5 was an independent predictor of LUAD in TCGA and GEO cohorts and qPCR validation with 106 clinical samples. At last, A knockdown of SERPINB5 in LUAD cells reduced proliferation, migration, and EMT. Proliferation, migration, and invasion are promoted by the overexpression of SERPINB5. CONCLUSION: Therefore, SERPINB5 has shown potential as a prognostic biomarker for LUAD, and it may become a potential therapeutic target for lung adenocarcinoma.

The independent prognostic effect of marital status on non-small cell lung cancer patients: a population-based study.

Background: Previous studies had demonstrated that marital status was an independent prognostic factor in multiple cancers. However, the impact of marital status on non-small cell lung cancer (NSCLC) patients was still highly controversial. Method: All NSCLC patients diagnosed between 2010-2016 were selected from the Surveillance, Epidemiology and End Results (SEER) database. To control the confounding effect of related clinicopathological characteristics, propensity score matching (PSM) was conducted between married and unmarried groups. In addition, independent prognostic clinicopathological factors were evaluated via Cox proportional hazard regression. Moreover, nomograms were established based on the clinicopathological characteristics, and the predictive accuracy was assessed by calibration curves. Furthermore, decision curve analysis (DCA) was used to determine the clinical benefits. Results: In total, 58,424 NSCLC patients were enrolled according to the selection criteria. After PSM, 20,148 patients were selected into each group for further analysis. The married group consistently demonstrated significantly better OS and CSS compared to unmarried group [OS median survival (95% CI): 25 (24-26) vs. 22 (21-23) months, p < 0.001; CSS median survival (95% CI): 31 (30-32) vs. 27 (26-28) months, p < 0.001]. Moreover, single patients were associated with the worst OS [median survival (95% CI): 20 (19-22) months] and CSS [median survival (95%CI): 24 (23-25) months] among unmarried subgroups. Besides, unmarried patients had a significantly worse prognosis compared to married patients in both univariate and multivariate Cox proportional hazard regressions. Furthermore, married group was associated with better survival in most subgroups. To predict the 1-, 3- and 5-year OS and CSS probabilities, nomograms were established based on age, race, sex, gender, marital status, histology, grade, TNM stage. The C-index for OS and CSS were 0.759 and 0.779. And the calibration curves showed significant agreement between predictive risk and the observed probability. DCA indicated nomograms had consistently better predict performance. Conclusion: This study demonstrated that unmarried NSCLC patients were associated with significantly worse OS and CSS compared to married NSCLC patients. Therefore, unmarried patients need not only closer surveillance, but also more social and family support, which may improve patients' adherence and compliance, and eventually improve the survival.

Intrinsic Immunogenic Tumor Cell Death Subtypes Delineate Prognosis and Responsiveness to Immunotherapy in Lung Adenocarcinoma.

Recent studies have highlighted the combination of activation of host immunogenic cell death (ICD) and tumor-directed cytotoxic strategies. However, overall multiomic analysis of the intrinsic ICD property in lung adenocarcinoma (LUAD) has not been performed. Therefore, the aim of this study was to develop an ICD-based risk scoring system to predict overall survival (OS) and immunotherapeutic efficacy in patients. In our study, both weighted gene co-expression network analysis (WGCNA) and LASSO-Cox analysis were utilized to identify ICDrisk subtypes (ICDrisk). Moreover, we identify genomic alterations and differences in biological processes, analyze the immune microenvironment, and predict the response to immunotherapy in patients with pan-cancer. Importantly, immunogenicity subgroup typing was performed based on the immune score (IS) and microenvironmental tumor neoantigens (meTNAs). Our results demonstrate that ICDrisk subtypes were identified based on 16 genes. Furthermore, high ICDrisk was proved to be a poor prognostic factor in LUAD patients and indicated poor efficacy of immune checkpoint inhibitor (ICI) treatment in patients with pan-cancer. The two ICDrisk subtypes displayed distinct clinicopathologic features, tumor-infiltrating immune cell patterns, and biological processes. The ISlowmeTNAhigh subtype showed low intratumoral heterogeneity (ITH) and immune-activated phenotypes and correlated with better survival than the other subtypes within the high ICDrisk group. This study suggests effective biomarkers for the prediction of OS in LUAD patients and immunotherapeutic response across Pan-cancer and contributes to enhancing our understanding of intrinsic immunogenic tumor cell death.

Ten-year trends of the clinicopathological characteristics, surgical treatments and survival outcomes of operable lung cancer patients in monocenter: a retrospective cohort study.

Background: Lung cancer is one of the cancers with the highest morbidity and mortality. During the last decade, the trends of clinical characteristics, surgical treatments and survival of lung cancer patients in China have remained unclear. Methods: All lung cancer patients operated on from 2011 to 2020 were identified in a prospectively maintained database of Sun Yat-sen University Cancer Center. Results: A total of 7,800 lung cancer patients were included in this study. Within the past 10 years, the average age at diagnosis of the patients remained stable, the proportion of asymptomatic, female and nonsmoking patients increased, and the average tumor size decreased from 3.766 to 2.300 cm. In addition, the proportion of early stage and adenocarcinoma increased, while that of squamous cell carcinoma decreased. Among the patients, the proportion of patients having video-assisted thoracic surgery increased. More than 80% of the patients underwent lobectomy and systematic nodal dissection over the 10 years. Additionally, both the average postoperative length of stay and 1-, 3-, and 6-month postoperative mortality decreased. Moreover, the 1-, 3-, and 5-year overall survival (OS) rates of all the operable patients increased from 89.8, 73.9, and 63.8% to 99.6, 90.7, and 80.8%, respectively. The 5-year OS rates of the patients with stage I, II, and III lung cancer were 87.6, 79.9, and 59.9%, respectively, which were higher than those in other published data. Conclusion: There were significant changes in the clinicopathological characteristics, surgical treatments and survival outcomes of the patients with operable lung cancer from 2011 to 2020.

Ferroptosis-related lncRNAs signature to predict the survival and immune evasion for lung squamous cell carcinoma.

Introduction: the investigation on the interactions between ferroptosis and lncRNAs for lung squamous cell carcinoma (LUSC) has been scare, and its impact on tumor immune microenvironment remained unknown. We aim to not only identify a ferroptosis-related lncRNAs signature for LUSC prognosis, but also evaluate its correlation to tumor immune evasion. Methods: RNA sequencing data and survival information were obtained from The Cancer Genome Atlas database. A ferroptosis-related lncRNAs signature (FerRLSig) was developed and validated by univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression and multivariate Cox regression. The tumor immune microenvironment and immune evasion were subsequently evaluated based on the FerRLSig stratification. Results: the FerRLSig consisted of 10 ferroptosis-related lncRNAs and significantly associated with overall survival with satisfactory area under curve (HR = 2.240, 95% CI: 1.845-2.720, p < 0.001, 5-years AUC: 0.756). Based on the FerRLSig stratification, the high-risk group demonstrated not only significantly higher immune infiltration, but also more profound T cell dysfunction and immune evasion, which might ultimately lead to the resistance to current immune checkpoint inhibitors. Conclusion: a robust prognostic FerRLSig for LUSC has been developed and validated, demonstrating a close association not only with tumor immune cell infiltration, but also with T cell dysfunction and immune evasion. Further investigation is warranted to better improve the survival of LUSC patients based on the FerRLSig stratification.

A tumor microenvironment gene set-Based prognostic signature for non-small-cell lung cancer.

Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS). Methods: Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes. Results: Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response. Conclusion: The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities.

Mutation-Derived Long Noncoding RNA Signature Predicts Survival in Lung Adenocarcinoma.

Background: Genomic instability is one of the representative features of cancer evolution. Recent research has revealed that long noncoding RNAs (lncRNAs) play a critical role in maintaining genomic instability. Our work proposed a gene signature (GILncSig) based on genomic instability-derived lncRNAs to probe the possibility of lncRNA signatures as an index of genomic instability, providing a potential new approach to identify genomic instability-related cancer biomarkers. Methods: Lung adenocarcinoma (LUAD) gene expression data from an RNA-seq FPKM dataset, somatic mutation information and relevant clinical materials were downloaded from The Cancer Genome Atlas (TCGA). A prognostic model consisting of genomic instability-related lncRNAs was constructed, termed GILncSig, to calculate the risk score. We validated GILncSig using data from the Gene Expression Omnibus (GEO) database. In this study, we used R software for data analysis. Results: Through univariate and multivariate Cox regression analyses, five genomic instability-associated lncRNAs (LINC01671, LINC01116, LINC01214, lncRNA PTCSC3, and LINC02555) were identified. We constructed a lncRNA signature (GILncSig) related to genomic instability. LUAD patients were classified into two risk groups by GILncSig. The results showed that the survival rate of LUAD patients in the low-risk group was higher than that of those in the high-risk group. Then, we verified GILncSig in the GEO database. GILncSig was associated with the genomic mutation rate of LUAD. We also used GILncSig to divide TP53 mutant-type patients and TP53 wild-type patients into two groups and performed prognostic analysis. The results suggested that compared with TP53 mutation status, GILncSig may have better prognostic significance. Conclusions: By combining the lncRNA expression profiles associated with somatic mutations and the corresponding clinical characteristics of LUAD, a lncRNA signature (GILncSig) related to genomic instability was established.

Analysis of the tumor immune environment identifies an immune gene set-based prognostic signature in non-small cell lung cancer.

BACKGROUND: The tumor immune environment plays a critical role in lung cancer initiation and prognosis. Therefore, understanding how the tumor immune environment impacts the overall survival (OS) of patients with advanced lung cancer post immunotherapy is of great importance. In this article, we aimed to identify the immune components of lung cancer and develop an immune prognostic signature to predict OS. METHODS: Differentially expressed immune-related genes were calculated between tumor and normal tissues using expression data from The Cancer Genome Atlas (TCGA) database. Then univariate Cox regression analysis was conducted to select prognosis-related genes and the prognostic risk model was constructed by multivariate Cox regression analysis. Patient risk scores were calculated, and a clinical correlation analysis was performed within the risk model. In addition, immune cell infiltration patterns were identified to find the immune cell subtypes related to prognosis. RESULTS: A gene model consisting of 12 immune-related genes was used as our signature. The model showed that the high-risk group experienced a shorter survival time, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.733. High-risk immune genes, such as S100 calcium binding protein A16 (S100A16) and angiopoietin-like 4 (ANGPTL4), were associated with more malignant clinical manifestations. Further, we discovered that extensive infiltration of B cells, dendritic cells, and mast cells indicated a favorable prognosis. CONCLUSIONS: The signature developed in this paper could be an effective model for estimating OS in lung cancer patients, and the immune cell infiltration analysis of the tumor immune microenvironment could shed light on more effective treatment in clinical practice.