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.

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.

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.

The short-term outcomes of nonintubated anesthesia compared with intubated anesthesia in single-port video-assisted lung surgery in enhanced recovery after thoracic surgery: results from a single-center retrospective study.

Background: Nonintubated anesthesia avoids invasive tracheal intubation operations and reduces trauma. in addition, it has advantages in lung surgery in some patients with poor lung function, in line with the concept of rapid recovery. However, few studies have discussed the clinical significance of Enhanced recovery after surgery (ERAS) combined with nonintubated anesthesia in single-port video-assisted thoracoscopic surgery (VATS). We conducted a retrospective study to examine the safety and availability of nonintubated anesthesia single-port video-assisted lung surgery (NI-SP-VALS) combined with ERAS programs in patients. Methods: This was a single-center retrospective study. All patients were preoperatively diagnosed with lung nodules and underwent NI-SP-VALS or intubated anesthesia SP-VALS (I-SP-VALS) combined with ERAS programs between July 2021 and March 2022. Short-term postoperative outcomes were compared in 2 cohorts. Results: In total, 272 patients were included. Among them, 91 patients received NI-SP-VALS combined with ERAS programs (observation group), and 181 underwent intubation anesthesia (control group). Baseline data were statistically different between the two groups, and 1:1 propensity score matching (PSM) matching was used. A total of 73 patients remained in each group after PSM, and baseline characteristics were not significantly different between the 2 cohorts. The time of hospital stay [4.00 (4.00-5.00) vs. 44.50 (0.00-5.75) d; P=0.029] and catheter stay [0.50 (0.20-2.00) vs. 2.00 (2.00-2.00) d; P<0.001] were significantly shorter, the white blood cell count (WBC) [9.45 (8.08-11.30) vs. 11 (8.50-12.80)/L; P=0.009] and the lowest SpO2 in operation [96.00 (94.00-97.50) vs. 97.00 (95.00-98.50); P=0.035] were also lower in the nonintubated group than those of the intubated group. No differences were observed in variables of intraoperation, other routine blood indexes, postoperative drainage, postoperative medicine use, postoperative symptoms, complications, hospitalization expenses, postoperative follow-up index, or self-assessment of anxiety. Conclusions: The data after PSM shows that compared with intubated anesthesia, NI-SP-VALS combined with ERAS programs is safe and effective. Nonintubated anesthesia promotes rapid recovery of patients and reduces postoperative inflammatory reactions. Hence, nonintubated anesthesia may conform to the idea of ERAS and has application value in thoracic surgery.