Causal effects of gut microbiota in the development of lung cancer and its histological subtypes: A Mendelian randomization study.

BACKGROUND: Numerous studies have characterized the gut microbiome (GM) in lung cancer (LC). Yet, the causality between GM and LC and its subtypes remain uncharacterized. METHODS: Two-sample Mendelian randomization (MR) was designed to investigate the causal relationship between the GM and LC and its subtypes, using publicly available summary data of genome-wide association studies. The researchers ran two groups of MR analyses, including the genome-wide statistical significance threshold (5 × 10-8 ) and the locus-wide significance level (1 × 10-5 ). RESULTS: Using MR analysis, we ascertained 42 groups of GM that are intimately linked to LC and its subtypes at the locus-wide significance level. Of the 42 groups, 12 were in LC, nine in non-small cell lung cancer (NSCLC), six in small cell lung cancer (SCLC), two in lung adenocarcinomas, and 13 in lung squamous carcinomas. After false discovery rate correction, we still found a remarkable causal interaction between the Eubacterium ruminantium group and SCLC. Moreover, five groups of GM closely linked to LC and its subtypes were recognised at the genome-wide statistical significance threshold. This finding included one group each in LC, NSCLC and SCLC, two groups in lung adenocarcinoma and none in lung squamous carcinoma. None of the foregoing findings were heterogeneous or horizontal pleiotropy. Reverse MR revealed that genetic susceptibility to LC and its subtypes caused significant changes in three groups of GM. CONCLUSION: Our findings substantiate the causality between GM and LC and its subtypes. This study offers fresh insights into the function of GM in mediating the progression of LC.

An open set model for pest identification.

Agricultural pest identification is a prerequisite for increasing crop production and meeting global food demands. Generally, numerous phenotypic and genotypic features are widely utilized for species-level pest identification. However, the approaches are time-consuming and require expert knowledge in relevant fields. Numerous image-based machine learning (ML) models also exist to identify insect pests in agricultural fields. The models are significantly rely on a large, manually curated dataset and are close-set in nature. Our study aims to develop an open set pest identification approach by adding the capability of rejecting any irrelevant inputs. Tephritid fruit flies (Diptera:Tephritidae) are considered as an example since they are the most economically important agricultural pests worldwide. Images of the fruit flies were collected from a publicly available database and filtered to exclude uninformative images using a deep learning model (Inception-V3) and an unsupervised k-means clustering method. For the closed-set identification task, our EfficientNet-B2 model classified four major genera of notorious tephritid flies, namely, Anastrepha, Ceratitis, Rhagoletis, and Bactrocera with an accuracy of 89.65%. We further improvise our proposed model for open-set recognition tasks to leverage the identification beyond the trained datasets. The open set model achieved an overall accuracy of 86.48% and a macro F1-score of 94.44% on the four genera and an unknown class. Our proposed model can be a practical and effective pest identification tool for harmful fruit flies. In addition, the model is easy to implement with existing agricultural pest control systems in an open-world scenario.

Vesicular Release and Uptake of Circular LSD1-RNAs from Non-Cancer and Cancer Lung Cells.

Lysine-specific demethylase 1 (LSD1) is highly expressed in many cancer types and strongly associated with cancer progression and metastasis. Circular RNAs (circRNAs) are produced by back-splicing and influence the interactive RNA network by microRNA and protein sponging. In the present study, we aimedto identify circRNAs that derive from the LSD1-encoding KDM1A gene, and to investigate their potential to be released and uptaken by lung cancer versus non-cancer epithelial cells. We identified four circLSD1-RNAs by RT-PCR with divergent primers, followed by sequencing. The expression level of circLSD1-RNAs was then studied by quantitative PCR on cellular and extracellular fractions of lung cancer (PC9) and non-cancer primary small airway epithelial (PSAE) cells. Moreover, we established the transgenic overexpression of circLSD1-RNAs. We show that circLSD1-RNAs are primarily located in the cytoplasm, but are packaged and released from lung cancer and non-cancer cells by extracellular vesicles (EVs) and ribonucleoprotein (RNP) complexes, respectively. Proteomics demonstrated a different protein pattern of EV fractions released from PC9 versus PSAE cells. Importantly, released circLSD1-RNAs were differently taken up by PSAE and PC9 cells. In conclusion, our findings provide primary evidence that circLSD1-RNAs participate in the intercellular communication of lung cancer cells with the tumor environment.

A Novel m6A-Related LncRNA Signature for Predicting Prognosis, Chemotherapy and Immunotherapy Response in Patients with Lung Adenocarcinoma.

N6-methyladenosine (m6A) and long non-coding RNA (lncRNA) have been associated with cancer prognosis and the effect of immunotherapy. However, the roles of m6A-related lncRNAs in the prognosis and immunotherapy in lung adenocarcinoma (LUAD) patients remain unclear. We evaluated the m6A modification patterns of 695 samples based on m6A regulators, and prognostic m6A-related lncRNAs were identified via a weighted gene co-expression network analysis. Twelve abnormal m6A regulators and nine prognostic lncRNAs were identified. The tumor microenvironment cell-infiltrating characteristics of three m6A-related lncRNA clusters were highly consistent with the three immune phenotypes of tumors, including immune-excluded, immune-inflamed and immune-desert phenotypes. The lncRNA score system was established, and high lncRNA score patients were associated with better overall survival. The lncRNA score was correlated with the expression of the immune checkpoints. Two immunotherapy cohorts supported that the high lncRNA score enhanced the response to anti-PD-1/L1 immunotherapy and was remarkably correlated with the inflamed immune phenotype, showing significant therapeutic advantages and clinical benefits. Furthermore, the patients with high lncRNA scores were more sensitive to erlotinib and axitinib. The lncRNA score was associated with the expression of miRNA and the regulation of post-transcription. We constructed an applied lncRNA score-system to identify eligible LUAD patients for immunotherapy and predict the sensitivity to chemotherapeutic drugs.

Cross-talk between cuproptosis and ferroptosis regulators defines the tumor microenvironment for the prediction of prognosis and therapies in lung adenocarcinoma.

Cuproptosis, a newly identified form of programmed cell death, plays vital roles in tumorigenesis. However, the interconnectivity of cuproptosis and ferroptosis is poorly understood. In our study, we explored genomic alterations in 1162 lung adenocarcinoma (LUAD) samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) cohort to comprehensively evaluate the cuproptosis regulators. We systematically performed a pancancer genomic analysis by depicting the molecular correlations between the cuproptosis and ferroptosis regulators in 33 cancer types, indicating cross-talk between cuproptosis and ferroptosis regulators at the multiomic level. We successfully identified three distinct clusters based on cuproptosis and ferroptosis regulators, termed CuFeclusters, as well as the three distinct cuproptosis/ferroptosis gene subsets. The tumor microenvironment cell-infiltrating characteristics of three CuFeclusters were highly consistent with the three immune phenotypes of tumors. Furthermore, a CuFescore was constructed and validated to predict the cuproptosis/ferroptosis pathways in individuals and the response to chemotherapeutic drugs and immunotherapy. The CuFescore was significantly associated with the expression of miRNA and the regulation of post-transcription. Thus, our research established an applied scoring scheme, based on the regulators of cuproptosis/ferroptosis to identify LUAD patients who are candidates for immunotherapy and to predict patient sensitivity to chemotherapeutic drugs.

Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

KCNQ1OT1 lncRNA affects the proliferation, apoptosis, and chemoresistance of small cell lung cancer cells via the JAK2/STAT3 axis.

BACKGROUND: Small cell lung cancer (SCLC) is a devastating and aggressive neuroendocrine carcinoma characterized by high cellular proliferation and early metastatic spread. Numerous studies have demonstrated that long noncoding RNAs (lncRNAs) can regulate tumor generation and development, including in SCLC. The current study aimed to assess the effect of the lncRNA, KCNQ1OT1, on the proliferation, apoptosis, and chemoresistance of SCLC and the potential underlying molecular mechanism. METHODS: Matched chemo-resistant and sensitive cells were applied to RNA isolation and followed by expression profiling by microarray analysis and subsequent quantitative polymerase chain reaction (qPCR) validation. Cell viability and apoptosis were determined by Cell Counting Kit-8 and flow cytometry to examine the chemoresistance and apoptosis of KCNQ1OT1 knockdown with lentivirus-mediated RNA interference. Furthermore, cell proliferation was studied by colony formation, and invasion and migration were tested by Transwell cell invasion and wound-healing assays, respectively. A tumor xenograft model was established to determine the role of KCNQ1OT1 in tumor growth and chemoresistance in response to KCNQ1OT1 knockdown in vivo. Western blot analysis, qPCR, and immunohistochemistry were used to detect the levels of messenger RNA (mRNA) Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) pathway-related markers. RESULTS: Higher expression of KCNQ1OT1 was detected in SCLC chemo-resistant verso chemo-sensitive cells. Knockdown of KCNQ1OT1 inhibited SCLC cell viability and cloning ability, hindered cell migration and invasion, induced apoptosis in vitro, and suppressed tumor growth and chemoresistance in vivo, by activating the JAK2/STAT3 signaling pathway. CONCLUSIONS: This is the first study to indicate that lncRNA KCNQ1OT1 promotes cell proliferation and invasion, and prevents apoptosis of SCLC by activating the JAK2/STAT3 pathway.

Stereotactic Body Radiotherapy Versus Surgery for Early-Stage Non-Small-Cell Lung Cancer.

BACKGROUND: Surgery is the gold standard therapy for patients with early-stage non-small-cell lung cancer (NSCLC). However, stereotactic body radiotherapy (SBRT) may provide as an alternative for patients who are medically inoperable or refuse surgical resection. The optimal treatment (SBRT or surgery) for patients with early-stage NSCLC is not clear. METHODS: A systematic search was performed from PubMed, MEDLINE, Embase, and the Cochrane Library. Study heterogeneity and publication bias were estimated. RESULTS: Fourteen cohort studies involving 1438 participants (719 who received SBRT and 719 who received surgery) were included in the meta-analysis. The main bias sources between the two groups, such as age, gender, tumor diameter, forced expiratory volume in 1 s, and Charlson comorbidity index were matched. The surgery was associated with a better overall survival (OS) and long-term distant control (DC) for early-stage NSCLC. The pooled OR and 95% confidence interval (CI) for 1-y, 3-y, 5-y OS, and 5-y DC were 1.56 (1.12-2.15), 1.86 (1.50-2.31), 2.43 (1.80-3.28), and 2.74 (1.12-6.67), respectively. No difference was found between the treatments in the 1-y and 3-y disease-free survival; 1-y, 3-y and 5-y locoregional control; or 1-y and 3-y DC. CONCLUSIONS: Our results found a superior OS and long-term DC for early-stage NSCLC after surgery compared with SBRT after propensity score matching.

H3K27me3 induces multidrug resistance in small cell lung cancer by affecting HOXA1 DNA methylation via regulation of the lncRNA HOTAIR.

BACKGROUND: The long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) serves as a powerful predictor of tumor progression and overall survival in patients. Our previous studies showed that HOTAIR modulated HOXA1 DNA methylation by reducing DNMT1 and DNMT3b expression in drug-resistant small cell lung cancer (SCLC). Moreover, H3 lysine 27 trimethylation (H3K27me3) is catalyzed by enhancer of zeste homolog 2 (EZH2) and plays a critical role in SCLC chemoresistance. However, it is not completely clear whether H3K27me3 affects HOXA1 DNA methylation or whether this effect is mediated by HOTAIR. METHODS: The levels of EZH2 and H3K27me3 were identified in SCLC tissues by immunohistochemical (IHC) staining and in SCLC multidrug-resistant cells by Western blotting. Cell counting kit-8 (CCK-8) and flow cytometry were used to detect and analyze the biological function of H3K27me3. Then, we assessed the role of HOTAIR in the regulation of EZH2 and H3K27me3 by using lentivirus and small interfering RNA. Further, bisulfite sequencing PCR was conducted to detect the methylation levels of HOXA1 DNA. Finally, Western blotting was performed to examine the regulatory role of H3K27me3 in controlling HOTAIR expression in SCLC. RESULTS: In this study, we found that EZH2 and H3K27me3 levels were markedly higher in SCLC tissues and multidrug-resistant SCLC cells. The results indicated that H3K27me3 was related to multidrug resistance. HOTAIR overexpression and knockdown showed that EZH2 and H3K27me3 were regulated by HOTAIR. Moreover, H3K27me3 affected HOXA1 DNA methylation levels. Strikingly, we found that H3K27me3 acted as a negative feedback regulator of HOTAIR. CONCLUSIONS: Our study showed that H3K27me3 affects HOXA1 DNA methylation via HOTAIR regulation, indicating that H3K27me3 may be a potential therapy target for SCLC chemoresistance.