Background: Increasing evidence has demonstrated that circular RNAs (circRNAs) may play an important role in oncogenesis and tumor development; however, their role in lung adenocarcinoma (LUAD) remains unclear. We identified the differentially expressed circRNAs in LUAD and investigated the potential mechanisms for cancer progression.
Methods: We examined differentially expressed circRNAs in LUAD and paired normal tissues using downloaded circRNA microarrays from the Gene Expression Omnibus. We constructed gene co-expression networks based on the degree of Pearson correlation to predict the critical circRNA in LUAD. Gene Ontology analysis was performed on the genes in the network. We observed one novel circRNA upregulated in LUAD, hsa_circ_0000792, as well as its potential sponged microRNA, miR-375. Subsequent real-time quantitative PCR was used to verify the bioinformatics analysis.
Results: Several circRNAs showed significantly different expression levels in LUAD tissues. Real-time quantitative PCR and further co-expression network analysis of 42 matched tissue samples showed a significant difference in expression between LUAD and normal tissues in hsa_circ_0000792 (P < 0.001). We built a network of hsa_circ_0000792-targeted miRNA gene interactions, including miR-375 and the corresponding messenger RNAs. Gene Ontology analysis revealed that hsa_circ_0000792 could participate in signal transduction and cell communication during LUAD development. Larger area under the curve by receiver operating characteristic curve analysis of hsa_circ_0000792 and miR-375 (0.815 and 0.772, respectively) in LUAD indicated greater potential as biomarkers.
Conclusions: We identified hsa_circ_0000792 as a potential LUAD biomarker; however, further studies are required to determine the mechanism of this circRNA in LUAD development.
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http://dx.doi.org/10.1111/1759-7714.12761 | DOI Listing |
Thorac Cancer
January 2025
Department of Thoracic Surgery and Lung Transplantation, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong, China.
Background: The mycobiome in the tumor microenvironment of non-smokers with early-stage lung adenocarcinoma (ES-LUAD) has been minimally investigated.
Methods: In this study, we conducted ultra-deep metagenomic and transcriptomic sequencing on 128 samples collected from 46 nonsmoking ES-LUAD patients and 41 healthy controls (HC), aiming to characterize the tumor-resident mycobiome and its interactions with the host.
Results: The results revealed that ES-LUAD patients exhibited fungal dysbiosis characterized by reduced species diversity and significant imbalances in specific fungal abundances.
Front Immunol
January 2025
Tianjin Chest Hospital, Tianjin University, Tianjin, China.
Background: Macrophages play a dual role in the tumor microenvironment(TME), capable of secreting pro-inflammatory factors to combat tumors while also promoting tumor growth through angiogenesis and immune suppression. This study aims to explore the characteristics of macrophages in lung adenocarcinoma (LUAD) and establish a prognostic model based on macrophage-related genes.
Method: We performed scRNA-seq analysis to investigate macrophage heterogeneity and their potential pseudotime evolutionary processes.
Eur J Radiol Open
June 2025
Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, No. 181 Hanyu road, Shapingba district, Chongqing 400030, China.
Purpose: The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.
Methods: This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort).
Proceedings (IEEE Int Conf Bioinformatics Biomed)
December 2024
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, USA.
Lung cancer remains a predominant cause of cancer-related deaths, with notable disparities in incidence and outcomes across racial and gender groups. This study addresses these disparities by developing a computational framework leveraging explainable artificial intelligence (XAI) to identify both patient- and cohort-specific biomarker genes in lung cancer. Specifically, we focus on two lung cancer subtypes, Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), examining distinct racial and sex-specific cohorts: African American males (AAMs) and European American males (EAMs).
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Purpose: To develop and validate a radiomics nomogram model for predicting the micropapillary pattern (MPP) in lung adenocarcinoma (LUAD) tumors of ≤2 cm in size.
Methods: In this study, 300 LUAD patients from our institution were randomly divided into the training cohort (n = 210) and an internal validation cohort (n = 90) at a ratio of 7:3, besides, we selected 65 patients from another hospital as the external validation cohort. The region of interest of the tumor was delineated on the computed tomography (CT) images, and radiomics features were extracted.
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