Single-cell RNA sequencing reveals the cellular and molecular changes that contribute to the progression of lung adenocarcinoma

Liu, Bing; Wang, Chen; Fang, Zhanjie; Bai, Jing; Qian, Ying; Ma, Yuanyuan; Ruan, Xiuhang; Shi, Yan; Li, Shaolei; Wang, Yaqi; Dong, Bin; Li, Meng; Xia, Xuefeng; Qu, Hongzhu; Fang, Xiangdong; Wu, Nan

doi:10.3389/fcell.2022.927300

Cited by 3 publications

(1 citation statement)

References 46 publications

(52 reference statements)

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“…A previous study showed that GGN subtypes (pGGNs and mGGNs) had different cellular and molecular networks. Understanding the molecular mechanism in each GGN subtype aids in the further discovery of therapeutic targets (16). Experimental evidence indicates that most mGGNs have primarily malignant components.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Combined High-resolution Computed Tomography and Artificial Intelligence-Aided Diagnosis System in Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules

Zou,

Niu,

Cha

et al. 2024

I J Radiol

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Background: The widespread use of high-resolution computed tomography (HRCT) in lung cancer screening has allowed for an increased detection rate of ground-glass nodules (GGNs) in the lung. Hence, obtaining the correct clinical diagnosis of benign and malignant GGNs has become crucial. Objectives: Most artificial intelligence and computer-aided diagnosis (AI-CAD) systems for the classification of pulmonary GGNs fail to extract CT features. This study used HRCT and AI to analyze the CT features of GGNs to improve the prediction of benign and malignant pulmonary GGNs. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. A total of 204 patients with GGNs were recruited and divided into 2 groups according to their pathological results. Group A consisted of 69 cases with precursor glandular lesions (atypical adenomatous hyperplasia [AAH] and adenocarcinoma in situ [AIS]), inflammatory nodules, and benign nodules. Group B consisted of 135 cases with invasive lesions (minimally invasive adenocarcinoma [MIA], invasive adenocarcinoma [IAC], and other malignant lesions). Various CT features were compared between the 2 groups. The diagnostic efficacy of an AI-CAD system and radiologists’ reports for benign and malignant nodules were analyzed. A multivariate logistic regression analysis was performed to determine independent predictors of malignant GGN. A model that combined the AI system and manual extraction of radiological features was constructed. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the model. Results: Significant differences were found between malignant and benign groups according to the following 7 CT features: The GGN size (long and short diameters), vacuole sign, air bronchogram sign, vascular convergence sign, vascular perforator sign, interlobular septal obstruction sign, and spiculation (P < 0.05). The volume and mean CT values of precursor glandular lesions of the lungs were significantly different from those of invasive lesions (P < 0.05). The logistic regression model showed that the sensitivity and specificity of the AI system in diagnosing malignant groups were 0.756 and 0.696, respectively. The sensitivity and specificity of radiologists’ reports in diagnosing the malignant groups were 0.726 and 0.783, respectively. The combination of the 2 had a sensitivity of 0.768 and a specificity of 0.793. Conclusion: Prediction of the nature of GGNs based on CT features, including the vacuole sign, vascular perforator sign, and interlobular septal obstruction sign, were relatively accurate for a preliminary diagnosis. The AI system had a poorer diagnostic accuracy for GGNs than radiologists’ reports of CT images. The combination of AI and radiologists’ reports showed the highest diagnostic efficacy.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Combined High-resolution Computed Tomography and Artificial Intelligence-Aided Diagnosis System in Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules

Zou,

Niu,

Cha

et al. 2024

I J Radiol

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Gut Dysbiosis Drives Inflammatory Bowel Disease Through the CCL4L2‐VSIR Axis in Glycogen Storage Disease

Lan,

Zhang,

Jin

et al. 2024

Advanced Science

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Patients with glycogen storage disease type Ib (GSD‐Ib) frequently have inflammatory bowel disease (IBD). however, the underlying etiology remains unclear. Herein, this study finds that digestive symptoms are commonly observed in patients with GSD‐Ib, presenting as single or multiple scattered deep round ulcers, inflammatory pseudo‐polyps, obstructions, and strictures, which differ substantially from those in typical IBD. Distinct microbiota profiling and single‐cell clustering of colonic mucosae in patients with GSD are conducted. Heterogeneous oral pathogenic enteric outgrowth induced by GSD is a potent inducer of gut microbiota immaturity and colonic macrophage accumulation. Specifically, a unique population of macrophages with high CCL4L2 expression is identified in response to pathogenic bacteria in the intestine. Hyper‐activation of the CCL4L2‐VSIR axis leads to increased expression of AGR2 and ZG16 in epithelial cells, which mediates the unique progression of IBD in GSD‐Ib. Collectively, the microbiota‐driven pathomechanism of IBD is demonstrated in GSD‐Ib and revealed the active role of the CCL4L2‐VSIR axis in the interaction between the microbiota and colonic mucosal immunity. Thus, targeting gut dysbiosis and/or the CCL4L2‐VISR axis may represent a potential therapy for GSD‐associated IBD.

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Advances in diagnosis and prediction for aggression of pure solid T1 lung cancer

Mu,

Huang,

et al. 2023

Precision Clinical Medicine

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A growing number of early-stage lung cancers presenting as malignant pulmonary nodules have been diagnosed because of the increased adoption of low-dose spiral computed tomography. But pure solid T1 lung cancer with ≤3 cm in the greatest dimension is not always at an early stage, despite its small size. This type of cancer can be highly aggressive and is associated with pathological involvement, metastasis, postoperative relapse, and even death. However, it is easily misdiagnosed or delay diagnosed in clinics and thus poses a serious threat to human health. The percentage of nodal or extrathoracic metastases has been reported to be >20% in T1 lung cancer. As such, understanding and identifying the aggressive characteristics of pure solid T1 lung cancer is crucial for prevention, diagnosis, and therapeutic strategies, and beneficial to improving the prognosis. With the widespread of lung cancer screening, these highly invasive pure solid T1 lung cancer will become the main advanced lung cancer in future. However, there is limited information regarding precision medicine on how to identify these “early-stage” aggressive lung cancers. To provide clinicians with new insights into early recognition and intervention of the highly invasive pure solid T1 lung cancer, this review summarizes its clinical characteristics, imaging, pathology, gene alterations, immune microenvironment, multi-omics, and current techniques for diagnosis and prediction.

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Single-cell RNA sequencing reveals the cellular and molecular changes that contribute to the progression of lung adenocarcinoma

Cited by 3 publications

References 46 publications

Diagnostic Value of Combined High-resolution Computed Tomography and Artificial Intelligence-Aided Diagnosis System in Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules

Diagnostic Value of Combined High-resolution Computed Tomography and Artificial Intelligence-Aided Diagnosis System in Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules

Gut Dysbiosis Drives Inflammatory Bowel Disease Through the CCL4L2‐VSIR Axis in Glycogen Storage Disease

Advances in diagnosis and prediction for aggression of pure solid T1 lung cancer

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