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Background: Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction. Objective: This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots. Results: A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations). Conclusions: Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.
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Background: Numerous studies have revealed that the abnormal expression of pyroptosis-related genes is closely related to the prognosis of lung adenocarcinoma (LUAD); however, a comprehensive analysis has yet to be conducted. This study aimed to reveal the influence of pyroptosis-related genes on the prognosis of LUAD and establish a prognostic model based on those genes, in order to evaluate the prognosis of LUAD. Methods: The data of tumor and normal samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential analysis was used to identify pyroptosis-related genes (obtained from the GeneCards database) that were differentially expressed (DE) in TCGA database. Univariate and stepwise multivariate Cox proportional hazards regression analyses were used to screen feature genes related to LUAD overall survival (OS) and construct gene signature. Gene set enrichment analysis (GSEA) was then performed to reveal potential functions related to gene signature. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to reveal distinctions in each cell-subtype groups in the immune landscape of LUAD. Results: Overall, 26 DE genes (DEGs) associated with pyroptosis were obtained. Among them, 4 (MKI67, BTK, MST1, and TUBB6) were selected as prognostic genes and a 4-gene signature with a good prognostic performance in the TCGA and GEO was constructed. The gene signature was shown to be an independent prognostic factor of LUAD in subsequent analysis. Functional enrichment indicated that the 4-gene signature may participate in the tumorigenesis and development of LUAD through various pathways related to tumor progression to play a prognostic role in LUAD. Additionally, the results of the immune landscape indicated that the 4-gene signature may affect the prognosis of LUAD via cooperating with changes in the immune microenvironment. Conclusions: The key biomarkers and pathways identified in this study would deepen the comprehension of the molecular mechanism of pyroptosis in LUAD. More importantly, the 4-gene signature may serve as a novel potential prognostic model for LUAD.
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BACKGROUND/AIM: Xihuang Wan (XHW), a traditional Chinese medicine (TCM), has been used in China for a variety of cancers including lung cancer. The present study evaluated the efficacy of XHW on a Lewis lung mouse model and explored the potential mechanism via transcriptomics. MATERIALS AND METHODS: The mice were randomized into 6 groups: 1) untreated control (n=10); 2) low-dose XHW; 3) medium-dose XHW; 4) high-dose XHW; 5) cisplatin; and 6) untreated blank (n=4). Lewis lung carcinoma (LLC) cells were injected subcutaneously except for the 4 mice in the blank group. The body weight and tumor length and width were measured every 3 days. RNA-sequencing was performed on tumors in the high-dose XHW group and the control group. RESULTS: XHW inhibited the growth of LLC in a syngeneic mouse model, without toxicity, with equivalent efficacy to cisplatin. RNA-sequencing demonstrated that many signaling pathways were involved in XHW-mediated inhibition of LLC, including tumor necrosis factor, estrogen, cyclic guanosine 3', 5'-monophosphate-protein kinase G, apelin and the peroxisome proliferator-activated receptor signaling pathways. CONCLUSION: XHW inhibited LLC carcinoma through different pathways and shows clinical promise for patients who cannot tolerate platinum-based drugs.