The promotion of non-small cell lung cancer progression by collagen and calcium binding EGF domain 1 is mediated through the regulation of ERK/JNK/P38 phosphorylation by reactive oxygen species.

Reactive oxygen species (ROS) are metabolic by-products of cells, and abnormal changes in their levels are often associated with tumor development. Our aim was to determine the role of collagen and calcium binding EGF domain 1 (CCBE1) in oxidative stress and tumorigenesis in non-small cell lung cancer cells (NSCLC). We investigated the tumorigenic potential of CCBE1 in NSCLC using in vitro and in vivo models of CCBE1 overexpression and knockdown. Immunohistochemical staining results showed that the expression of CCBE1 in cancer tissues was significantly higher than that in adjacent tissues. Cell counting Kit 8, clonal formation, wound healing, and transwell experiments showed that CCBE1 gene knockdown significantly inhibited the migration, invasion, and proliferation of NSCLC cell lines. In terms of mechanism, the silencing of CCBE1 can significantly promote the morphological abnormalities of mitochondria, significantly increase the intracellular ROS level, and promote cell apoptosis. This change of oxidative stress can affect cell proliferation, migration, and invasion by regulating the phosphorylation level of ERK/JNK/P38 MAPK. Specifically, the downregulation of CCBE1 inhibits the phosphorylation of ERK/P38 and promotes the phosphorylation of JNK in NSCLC, and this regulation can be reversed by the antioxidant NAC. In vivo experiments confirmed that downregulating CCBE1 gene could inhibit the growth of NSCLC in BALB/c nude mice. Taken together, our results confirm the tumorigenic role of CCBE1 in promoting tumor invasion and migration in NSCLC, and reveal the molecular mechanism by which CCBE1 regulates oxidative stress and the ERK/JNK/P38 MAPK pathway.

Circular RNA circATP9A promotes non-small cell lung cancer progression by interacting with HuR and by promoting extracellular vesicles-mediated macrophage M2 polarization.

BACKGROUND: CircRNA is recognized for its significant regulatory function across various cancers. However, its regulatory role in non-small cell lung cancer (NSCLC) is still largely uncharted. METHODS: Analysis based on public databases is completed using R software. circATP9A was identified by two circRNA datasets of NSCLC from the Gene Expression Omnibus database. To examine the impact of circATP9A on the phenotype of NSCLC, we conducted both in vitro and in vivo functional experiments. The mRNA and protein levels of specific molecules were determined through quantitative real-time PCR and western blot assays. RNA pulldown and RNA immunoprecipitation assays were performed to verify the interaction between RNA and protein. The functional role of extracellular vesicles (EVs)-circATP9A on tumor-associated macrophage (TAM) polarization was assessed using co-culture system and cell flow cytometry. RESULTS: Here, we elucidates the functional role of circATP9A in NSCLC. We demonstrated that circATP9A can foster the progression of NSCLC through in vivo and in vitro experiments. From a mechanistic standpoint, circATP9A can interact with the HuR protein to form an RNA-protein complex, subsequently amplifying the mRNA and protein levels of the target gene NUCKS1. Further, the PI3K/AKT/mTOR signaling was identified as the downstream pathways of circATP9A/HuR/NUCKS1 axis. More notably, hnRNPA2B1 can mediate the incorporation of circATP9A into EVs. Subsequently, these EVs containing circATP9A induce the M2 phenotype of TAMs, thereby facilitating NSCLC development. CONCLUSIONS: Our discoveries indicate that circATP9A could serve as a promising diagnostic indicator and a therapeutic target for NSCLC.

An individualized nomogram for predicting and validating spread through air space (STAS) in surgically resected lung adenocarcinoma: a single center retrospective analysis.

OBJECTIVE: A single-center study was conducted to explore the association between STAS and other clinical features in surgically resected adenocarcinoma to enhance our current understanding of STAS. METHODS: We retrospectively enrolled patients with lung adenocarcinoma (n = 241) who underwent curative surgeries. Patients undergoing surgery in 2019 were attributed to the training group (n = 188) and those undergoing surgery in January 2022 to June 2022 were attributed to the validation (n = 53) group. Univariate and multivariate logistic regression analyses were used to identify predictive factors for STAS, which were used to construct a simple nomogram. Furthermore, ROC and calibration curves were used to evaluate the performance of the nomogram. In addition, we conducted decision curve analysis (DCA) to assess the clinical utility of this nomogram. RESULTS: In our cohort, 52 patients were identified as STAS-positive (21.6%). In univariate analysis, STAS was significantly associated with age, surgical approach, CEA, CTR (Consolidation Tumor Ratio), TNM stage, tumor grade, gross tumor size, resection margin, vessel cancer embolus, pleural invasion, lymph node metastasis, high ki67 and positive PD-L1 staining (P < 0.05). Lower age, CTR > 0.75, vessel cancer embolus, high Ki67 and PD-L1 stain positive were significant predictors for STAS during multivariate logistics analysis. A simple nomogram was successfully constructed based on these five predictors. The AUC values of our nomogram for the probability of tumor STAS were 0.860 in the training group and 0.919 in the validation group. In addition, the calibration curve and DCA validated the good performance of this model. CONCLUSION: A nomogram was successfully constructed to identify the presence of STAS in surgically resected lung adenocarcinoma patients.

The HDAC2-MTA3 interaction induces nonsmall cell lung cancer cell migration and invasion by targeting c-Myc and cyclin D1.

Genome-wide association studies have identified numerous single-nucleotide polymorphisms (SNPs) associated with lung cancer; however, the functions of histone deacetylase 2 (HDAC2) rs13213007 and HDAC2 in nonsmall cell lung cancer (NSCLC) remain unclear. Here we identified HDAC2 rs13213007 as a risk SNP and showed that HDAC2 was upregulated in both peripheral blood mononuclear cells (PBMCs) and NSCLC tissues with the rs13213007 A/A genotype compared with those with the rs13213007 G/G or G/A genotype. Patient clinical data indicated strong associations between rs13213007 genotype and N classification. Immunohistochemical staining confirmed that higher expression of HDAC2 was associated with NSCLC progression. Furthermore, we generated 293T cells with the rs13213007 A/A genotype using CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 gene editing. Chromatin immunoprecipitation sequencing followed by motif analysis showed that HDAC2 can bind to c-Myc in rs13213007 A/A 293T cells. Cell Counting Kit-8, colony formation, wound-healing, and Transwell assays revealed that HDAC2 upregulates c-Myc and cyclin D1 expression and promotes NSCLC cell proliferation, migration, and invasion. Co-immunoprecipitation, quantitative reverse transcription-polymerase chain reaction, and western blot analysis assays showed that MTA3 interacts with HDAC2, decreases HDAC2 expression, and rescues the migration and invasion abilities of NSCLC cells. Taken together, these findings identify HDAC2 as a potential therapeutic biomarker in NSCLC.

Integrative analysis of tertiary lymphoid structures and immune microenvironment in patients with esophageal carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the most common upper gastrointestinal malignancies worldwide. Tertiary lymphoid structures (TLS) are tumor-infiltrating immune cells aggregates coupled with stromal cells which are similar to secondary lymphoid organs. The objective of this study is to explore the predictive effects of two common genes associated with TLS models on prognosis and immunotherapy effects in ESCC patients. METHODS: Clinical information for ESCC patients in the TCGA(The Cancer Genome Altas) cohort and GSE 53625 were collected. All of the samples were classified as either high score group or low score group based on two TLS signatures, and the association between TLS signatures and survival, clinical indicators, genomic burden, stemness indices analysis, tumor microenvironment and immunotherapy response were performed. Furthermore, the mature TLS was also assessed in ESCC tissue microarray. RESULTS: In our study, we quantified the score of TLS_9 and TLS_12, respectively, reflecting the different statuses of TLS (TLS_9 = B and T cells in TLSs; TLS_12 = neogenesis of TLSs). Subsequently, we explored the effect of TLS score on ESCC tumor microenvironment quantified by multiple algorithms. We found that a correlation analysis indicated that TLS_9 and TLS_12 were all positively correlated with CD8+ T cell, NK cells, CD4+ T cells, M1 macrophages and so on. Meanwhile, some cells present a different correlation pattern of TLS_9 and TLS_12, including activated CD4+ memory T cells and Tgd cells. Immune-related analysis revealed that the TLS_12 and TLS_9 scores were all positively correlated with immune dysfunction, yet negatively correlated with immune exclusion. Following this, the biological roles of TLS_9 and TLS_12 scores were investigated. Also, we noticed that the TLS score could significantly affect the CAFs infiltration and be associated with the genomic burden and tumor stemness. In addition, we explored the prognostic value of mature TLS through tissue microarray (TMA). Our result displayed ESCC patients with the presence of mature TLS had a better prognosis than ESCC patients without it. CONCLUSIONS: Our study indicated that ESCC patients with the presence of TLS had better outcomes and an inflamed immune microenvironment. In addition, both TLS-9 and TLS-12 gene signatures could be used as potential biomarkers for the immunotherapy of ESCC patients.

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer.

Background: Complex regulation exists between tumor metabolism and M2 macrophages. Long noncoding RNAs (lncRNAs) are famous for their wide regulatory role. This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages. Methods: The Cancer Genome Atlas was responsible for the public data. R software was responsible for the analysis of public data. Results: Based on the input expression profile, we quantified the M2 macrophage infiltration using the CIBERSORT algorithm and found that M2 macrophages were a risk factor for lung cancer. Also, we found that M2 macrophages were correlated with multiple metabolism pathways. Then, 67 lncRNAs involved in both M2 macrophages and related metabolism pathways were identified. A prognosis signature based on AC027288.3, AP001189.3, FAM30A, GAPLINC, LINC00578, and LINC01936 was established, which had good prognosis prediction ability. The clinical parameters and risk score were combined into a nomogram plot for better prediction of the patient's prognosis. A high fit of actual survival and nomogram-predicted survival was found using the calibration plot. Moreover, in low-risk patients, immunotherapy was more effective, while cisplatin and docetaxel were more effective in high-risk patients. Biological enrichment analysis indicated pathways of notch signaling, TGF-ß signaling, interferon alpha response, and interferon-gamma response were activated in the high-risk group. Meanwhile, the risk score was associated with tumor metabolism and M2 macrophages. Also, we found that the promoting effect of CAPLINC on M2 macrophage polarization might act through multiple metabolism pathways. Conclusions: Our result can provide new insights into the interaction between M2 macrophages and tumor metabolism, as well as the involved lncRNAs, which can provide the direction for future studies.

Costimulatory molecule-related lncRNA model as a potential prognostic biomarker in non-small cell lung cancer.

OBJECTIVE: Costimulatory molecules have been demonstrated to exert essential roles in multiple cancers. However, their role in lung cancer remains elusive. Here, we sought to identify costimulatory molecule-related lncRNAs in non-small cell lung cancer (NSCLC) and establish a prognostic signature to predict the prognosis of patients with NSCLC. METHODS: A total of 535 lung adenocarcinoma (LUAD) and 502 lung squamous cell carcinoma (LUSC) patients from the cancer genome atlas (TCGA) database were recruited. A novel costimulatory molecule-based lncRNA prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival. The Homo_sapiens.GRCh38 data set was set as a reference file for probe annotation. RESULTS: A total of 593 costimulatory molecule-related lncRNAs were extracted. After analysis, six costimulatory molecule-related lncRNAs (AC084859.1, AC079949.2, HSPC324, LINC01150, LINC01150, and AC090617.5) were screened. A prognostic model based on the six lncRNAs was established using systematic bioinformatics analyses. The prognostic model had a prognostic value in NSCLC patients. Furthermore, a prognostic nomogram was established based on clinical parameters and a risk-score model. Patients with different risk scores had considerably different tumor-infiltrating immune cells, somatic mutational loading, clinical outcomes, signaling pathways, and immunotherapy efficacy. In addition, LINC01137 was associated with unfavorable disease outcomes and fueled tumor progression in NSCLC. CONCLUSION: Taken together, our study demonstrated that a costimulatory molecule-related lncRNA model could be a potential prognostic biomarker in NSCLC. Moreover, LINC01137 could facilitate the proliferation and invasion of lung cancer.

Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration.

Background: Increasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated. Method: TCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package. Results: We identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven. Conclusion: This study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.

Exploration of Novel Immunological Terms in Lung Cancer With Large Populations: Implications for Immunotherapy.

Background: Ideal biomarkers to predict the response to immunotherapy in lung cancer are still lacking. Therefore, there is a need to explore effective biomarkers in large populations. Objective: The objective of this study is to explore novel immunological classifications that are associated with immunotherapy response through the ssGSEA algorithm. Methods: Six independent lung cancer cohorts were collected for analysis including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the EMBL-EBI database. The ssGSEA algorithm was performed to extract immune terms. Then, TCGA samples were involved as a training group and other cohorts were used as a validation group. After LASSO and Cox regression, prognostic associated immune terms were extracted and an immune-related risk score (IRS) signature was constructed. Furthermore, the association between IRS signature and clinical data, genome features, stemness indices analysis, tumor immune microenvironment, immunotherapy efficiency, and targeted therapy response was also investigated. Results: A total of 1,997 samples were enrolled in this study including six large lung cancer cohorts. Fifty-four immune terms were calculated through the ssGSEA algorithm in combined cohorts. Then, a nine-immune-term risk score model named IRS signature was established to predict the prognosis in combined cohorts. We classified patients into high-risk and low-risk subgroups according to the cutoff point. Subsequently, analysis of clinical data and genome features indicated that the patients in the high-IRS group tend to have advanced clinical features (clinical stage and T classification), as well as a higher level of copy number variation burden, higher tumor burden mutation, and higher tumor stemness indices. Immune landscape analysis demonstrated that high-IRS groups exhibited lower immune cell infiltration and immune-suppressive state. More importantly, the predicted result of the Tumor Immune Dysfunction and Exclusion analysis showed that high-IRS groups might be more insensitive to immunotherapy. Meanwhile, we have also identified that high-IRS groups were associated with better efficiency of several targeted drugs. Conclusion: To summarize, we identified a novel IRS model based on nine immune terms, which was quantified by the ssGSEA algorithm. This model had good efficacy in predicting overall survival and immunotherapy response in non-small cell lung cancer patients, which might be an underlying biomarker.

Analysis of the prognostic role and biological characteristics of circulating tumor cell-associated white blood cell clusters in non-small cell lung cancer.

Background: Recently, circulating tumor-cell-associated white blood cell (CTC-WBC) clusters have been reported to have prognostic value in some cancers. The prognostic role of CTC-WBC clusters in lung cancer has not yet been elucidated. Very little information is available about the biological characteristics of CTC-WBC clusters. Methods: A total of 82 patients with non-small cell lung cancer (NSCLC) were included in this study, and 61 patients with advanced-stage disease were closely followed-up. All patients had blood drawn prior to treatment. Subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH) platform was used to isolate and identify CTCs and CTC-WBC clusters. Kaplan-Meier survival analysis and Cox regression analysis were applied to assess patient progression-free survival (PFS). Further, qualitative and quantitative analyses the size and ploidy characteristics of CTC-WBC clusters. Results: Firstly, CTC-WBC clusters appeared more in the advanced (stage III and IV) stage (P=0.043) than in the early stage. Furthermore, the multivariable analysis (Cox proportional hazards model) revealed that the high-CTC (≥7/6 mL) group and CTC-WBC clusters (≥1/6 mL) positive group both had significantly worse PFS, with a hazard ratio (HR) of 2.89 [95% confidence interval (CI): 1.36-6.17, P=0.006] and 2.18 (95% CI: 1.07-4.43, P=0.031), respectively. In the conjoint analysis, compared to patients with <7 CTCs/6 mL without CTC-WBC clusters, patients with ≥7 CTCs/6 mL with CTC-WBC clusters had the highest risk of progression (HR =7.13, 95% CI: 2.51-20.23, P<0.001). In addition, the presence of ≥3-cell CTC-WBC clusters in patients may indicate a shorter PFS (P<0.05) and a higher risk of progression (HR =2.90, 95% CI: 1.06-7.89, P=0.037). Furthermore, compared with the characteristics of the total CTCs, almost all of the CTCs that could recruit WBCs were large cells (≥5 µm) and exhibited polyploidy (≥ tetraploid) (both P<0.01). Conclusions: The presence of CTC-WBC clusters was an independent prognostic factor for advanced NSCLC. The joint analysis of CTCs and CTC-WBC clusters could provide additional prognostic value to the enumeration of CTCs alone. Besides, most of the CTCs in CTC-WBC clusters were large polyploid cells.

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and NSCLC Progression:Evidence From Bioinformatics Analysis.

Background: Lung cancer is the most common comorbidity of idiopathic pulmonary fibrosis. Thus there is an urgent need for the research of IPF and carcinogenesis Objective: The objective of this study was to explore hub genes which are common in pulmonary fibrosis and lung cancer progression through bioinformatic analysis. Methods: All the analysis was performed in R software. Differentially expressed genes (DEGs) were explored by comparing gene expression profiles between IPF tissues and healthy lung tissues from GSE24206, GSE53845, GSE101286 and GSE110147 datasets. Venn Diagram analysis was used to identify the overlapping genes, while GO and KEGG pathway enrichment analysis were used to explore the biological functions of the DEGs using clusterprofiler package. Hub genes were identified by analyzing protein-protein interaction networks using Cytoscape software. Nomogram was constructed using the rms package. Tumor immune dysfunction and exclusion (TIDE) and Genomics of Drug Sensitivity in Cancer (GDSC) analysis was used to quantify the immunotherapy and chemotherapy sensitivity of non-small cell lung cancer (NSCLC) patients. Results: COL1A1, COL3A1, MMP1, POSTN1 and TIMP3 were identified as the top five hub genes. The five hub genes were used to construct a diagnostic nomogram that was validated in another IPF dataset. Since the hub genes were also associated with lung cancer progression, we found that the nomogram also had diagnostic value in NSCLC patients. These five genes achieved a statistically difference of overall survival in NSCLC patients (p < 0.05). The expression of the five hub genes was mostly enriched in fibroblasts. Fibroblasts and the hub genes also showed significant ability to predict the susceptibility of NSCLC patients to chemotherapy and immunotherapy. Conclusion: We identified five hub genes as potential biomarkers of IPF and NSCLC progression. This finding may give insight into the underlying molecular mechanisms of IPF and lung cancer progression and provides potential targets for developing new therapeutic agents for IPF patients.

Neoadjuvant Immuno-Chemotherapy: A New Perspective for Stage III NSCLC?

Background: Stage III Non-small cell lung cancer (NSCLC) is a heterogenous disease with novel treatment options. Recently, immunotherapy has attracted a lot of attention for advanced NSCLC. Objective: The objective of our study was to assess the efficacy and safety of neoadjuvant immuno-chemotherapy for resectable stage III NSCLC. Methods: We analyzed 11 stage III primary NSCLC surgical cases who had undergone standard lobectomy or bronchial sleeve resection and lymph node dissection between December 2020 and July 2021. The data analyzed included basic clinical features, serum levels of key biomarkers, clinical efficacy in the perioperative period, postoperative pathological results, postoperative complications and the incidence rates of Immune-Related Adverse Events. Results: Eleven patients were enrolled in our study with a mean age of 67.7 ± 4.8 years, and 10 patients being men with former or current smoking history. Squamous carcinoma (10/11, 91.1%) was the most common cancer type. Six patients had stage IIIa, five had stage IIIb. All patients received two or three cycles of neoadjuvant immuno-chemotherapy, with the median duration between the last treatment and surgery being 39 days (range, 32-46 days). All patients underwent R0 resection with ten patients undergoing single-port video-assisted thoracoscopic surgery. The median operative time was 170 min (range, 120-240 min). Only three (3/11, 27.3%) patients experienced mild postoperative complications and the mean hospital stay time was 6.9 days (range, 4-15 days). Nine (9/11, 81.8%) patients experienced major pathological response of which seven (7/11, 63.6%) was complete pathological response in postoperative results. The pathological stage was downgraded in 10 (10/11, 91.1%) patients, and although the incidence of Immune-Related Adverse Events was slightly higher (8/11, 72.7%), most events were grade 1-2 and did not delay surgery. Conclusion: Our study demonstrated that neoadjuvant immuno-chemotherapy is feasible and relatively safe for resectable stage III primary NSCLC patients. We hope this new neoadjuvant immuno-chemotherapy model can improve overall survival and open a new era for stage III primary NSCLC patients.

circRNA hsa_circ_0018414 inhibits the progression of LUAD by sponging miR-6807-3p and upregulating DKK1.

Lung adenocarcinoma (LUAD) is a subtype of lung cancer with a high incidence and mortality all over the world. In recent years, circular RNAs (circRNAs) have been verified to be a novel subtype of noncoding RNAs that exert vital functions in various cancers. Our research was designed to investigate the role of circ_0018414 in LUAD. We first observed that circ_0018414 was downregulated in LUAD tissues and cells. Also, low expression of circ_0018414 predicted unfavorable prognosis of LUAD patients. Then, upregulation of circ_0018414 repressed cell proliferation and stemness, while promoting cell apoptosis, in LUAD. Moreover, circ_0018414 overexpression enhanced the expression of its host gene, dickkopf WNT signaling pathway inhibitor 1 (DKK1), therefore inactivating the Wnt/ß-catenin pathway. Additionally, circ_0018414 could sponge miR-6807-3p to protect DKK1 mRNA from miR-6807-3p-induced silencing, leading to DKK1 upregulation in LUAD cells. Finally, rescue assays proved that circ_0018414 inhibited the progression of LUAD via the miR-6807-3p/DKK1 axis-inactivated Wnt/ß-catenin pathway. The findings in our work indicated circ_0018414 as a tumor inhibitor in LUAD, which might provide a new perspective for LUAD treatment.