Due to the advancement of 16S rRNA sequencing technology, the lower respiratory tract microbiota, which was considered non-existent, has been revealed. The correlation between these microorganisms and diseases such as tumor has been a hot topic in recent years. As the bacteria in the surrounding can infiltrate the tumors, researchers have also begun to pay attention to the biological behavior of tumor bacteria and their interaction with tumors. In this review, we present the characteristic of the lower respiratory tract bacteria and summarize recent research findings on the relationship between these microbiota and lung cancer. On top of that, we also summarize the basic feature of bacteria in tumors and focus on the characteristic of the bacteria in lung cancer. The relationship between bacteria in lung cancer and tumor development is also been discussed. Finally, we review the potential clinical applications of bacterial communities in the lower respiratory tract and lung cancer, and summarize key points of sample collection, sequencing, and contamination control, hoping to provide new ideas for the screening and treatment of tumors.
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Humans ; Lung Neoplasms ; RNA, Ribosomal, 16S/genetics* ; Bacteria/genetics* ; Microbiota ; Respiratory System ; Lung/microbiology*

Objective To investigate the change rule of T1ρ value in the process of lumbar intervertebral disc degeneration(IVDD)based on Pfirrmann grading by Meta-analysis.Methods PubMed,EMBASE,Cochrane Library,CNKI,Wanfang Data,VIP and Sinomed were searched to collect studies on quantitative assessment of IVDD using T1ρ imaging technology.The retrieval time limit was from the establishment of the database to December 20,2022.Meta-analysis was performed using RevMan 5.4 and Stata 14.0 software.Results A total of 12 articles were included,and the numbers of Pfirrmann grade Ⅰ-Ⅴ lumbar discs were 316,1 460,769,430 and 98,respectively.T1ρ relaxation time decreased gradually with the increase of the grade of degeneration.The T1ρ values of grade Ⅰlumbar discs were significantly higher than those of grade Ⅱ lumbar discs[weighted mean difference(WMD)=14.55,95%confidence interval(CI)6.35-22.75,P<0.01],and the T1ρ values of grade Ⅱ lumbar discs were significantly higher than those of grade Ⅲlumbar discs(WMD=34.20,95%CI 27.05-41.34,P<0.01).The T1ρ values of grade Ⅲ lumbar discs were significantly higher than that of grade Ⅳ lumbar discs(WMD=22.94,95%CI 17.08-28.80,P<0.01).The T1ρ values of grade Ⅳ lumbar discs were significantly higher than that of grade Ⅴ lumbar discs(WMD=9.35,95%CI 6.81-11.89,P<0.01).Conclusion T1ρ imaging technology can objectively and quantitatively evaluate degeneration at different stages,especially sensitive to IVDD in the early and middle stages,which can provide imaging evidence for clinical diagnosis of early IVDD.

Objective:To explore the efficacy of immune checkpoint inhibitors combined with adjuvant chemotherapy in patients with phase III gastric cancer and esophagogastric junction cancer.Methods:This study used a retrospective cohort study method based on real-world data. Clinical data of 403 patients with stage III gastric/esophagogastric junction cancer who underwent gastrectomy followed by adjuvant therapy in the Department of Gastric Surgery at Sun Yat-sen University Cancer Center from January 2020 to December 2023 were retrospectively collected. The study cohort comprised 147 (36.5%) patients with stage IIIA, 130 (32.3%) with stage IIIB, and 126 (31.3%) with stage IIIC gastric/esophagogastric junction cancer. Of them, 15 (3.7%) were HER-2 positive, 25 (6.2%) dMMR, and 22 (5.5%) patients Epstein-Barr virus encoding RNA (EBER) positive. Based on treatment plans, the patients were divided into immune checkpoint inhibitor combined with chemotherapy group (immune therapy group, n=110, 71 males and 39 females, median age 59 years old) and chemotherapy alone group (chemotherapy group, n=293, 186 males and 107 females, median age 60 years old). All patients in the immunotherapy group received immune checkpoint inhibitors targeting the programmed cell death protein-1 (PD-1) and its ligand (PD-L1). Of them, 85 received pembrolizumab, 10 received sintilimab, 8 received tislelizumab, 4 received camrelizumab, 2 received toripalimab, and 1 received pabocizumab. The adjuvant chemotherapy regimens used among the chemotherapy alone group includes SOX regimen (132 cases), XELOX (102 cases), S-1 monotherapy (44 cases), and other regimens (15 cases). The 3-year DFS rate of the two groups was compared, and subgroup analysis was conducted based on different ages, molecular phenotypes, pTNM staging, extranodal infiltration, and tumor length. Results:The median follow-up was 20.5 months (range 3.1~46.3), with a 3-year overall DFS rate of 61.4% for the entire 403 patients. The 3-year DFS rate for the immunotherapy group was 82.7%, higher than the chemotherapy alone group (58.8%), with a statistically significant difference ( P=0.021). Multivariate analysis showed that postoperative immunotherapy was a protective factor for DFS (HR=0.352, 95%CI: 0.180~0.685). Subgroup analysis showed that stage IIIC (HR=0.416, 95%CI: 0.184~0.940), aged ≥60 years (HR=0.336, 95%CI: 0.121~0.934) and extranodal invasion (HR=0.378, 95%CI: 0.170~0.839) were associated with benefit from the combined immune adjuvant chemotherapy, while no association was observed for MMR, HER-2 or EBER status. Conclusion:Stage III gastric/esophagogastric junction cancer patients may benefite from postoperative immune checkpoint inhibitor combined with adjuvant chemotherapy in real-world settings.

Objective:To explore the efficacy of immune checkpoint inhibitors combined with adjuvant chemotherapy in patients with phase III gastric cancer and esophagogastric junction cancer.Methods:This study used a retrospective cohort study method based on real-world data. Clinical data of 403 patients with stage III gastric/esophagogastric junction cancer who underwent gastrectomy followed by adjuvant therapy in the Department of Gastric Surgery at Sun Yat-sen University Cancer Center from January 2020 to December 2023 were retrospectively collected. The study cohort comprised 147 (36.5%) patients with stage IIIA, 130 (32.3%) with stage IIIB, and 126 (31.3%) with stage IIIC gastric/esophagogastric junction cancer. Of them, 15 (3.7%) were HER-2 positive, 25 (6.2%) dMMR, and 22 (5.5%) patients Epstein-Barr virus encoding RNA (EBER) positive. Based on treatment plans, the patients were divided into immune checkpoint inhibitor combined with chemotherapy group (immune therapy group, n=110, 71 males and 39 females, median age 59 years old) and chemotherapy alone group (chemotherapy group, n=293, 186 males and 107 females, median age 60 years old). All patients in the immunotherapy group received immune checkpoint inhibitors targeting the programmed cell death protein-1 (PD-1) and its ligand (PD-L1). Of them, 85 received pembrolizumab, 10 received sintilimab, 8 received tislelizumab, 4 received camrelizumab, 2 received toripalimab, and 1 received pabocizumab. The adjuvant chemotherapy regimens used among the chemotherapy alone group includes SOX regimen (132 cases), XELOX (102 cases), S-1 monotherapy (44 cases), and other regimens (15 cases). The 3-year DFS rate of the two groups was compared, and subgroup analysis was conducted based on different ages, molecular phenotypes, pTNM staging, extranodal infiltration, and tumor length. Results:The median follow-up was 20.5 months (range 3.1~46.3), with a 3-year overall DFS rate of 61.4% for the entire 403 patients. The 3-year DFS rate for the immunotherapy group was 82.7%, higher than the chemotherapy alone group (58.8%), with a statistically significant difference ( P=0.021). Multivariate analysis showed that postoperative immunotherapy was a protective factor for DFS (HR=0.352, 95%CI: 0.180~0.685). Subgroup analysis showed that stage IIIC (HR=0.416, 95%CI: 0.184~0.940), aged ≥60 years (HR=0.336, 95%CI: 0.121~0.934) and extranodal invasion (HR=0.378, 95%CI: 0.170~0.839) were associated with benefit from the combined immune adjuvant chemotherapy, while no association was observed for MMR, HER-2 or EBER status. Conclusion:Stage III gastric/esophagogastric junction cancer patients may benefite from postoperative immune checkpoint inhibitor combined with adjuvant chemotherapy in real-world settings.

The continuous advancement of molecular detection technology has greatly propelled the develop-ment of precision medicine for lung cancer.However,tumor heterogeneity is closely associated with tumor metastasis,recurrence,and drug resistance.Additionally,different lung cancer patients with the same genetic mutation may exhibit varying treatment responses to different therapeutic strategies.Therefore,the development of modern precision medicine urgently requires the precise formulation of personalized treatment strategies through personalized tumor models.Lung cancer organoid(LCO)can highly simulate the biological characteristics of tumor in vivo,facilitating the application of innovative drugs such as antibody-drug conjugate in precision medicine for lung cancer.With the development of co-culture model of LCO with tumor microenvironment and tissue engineering technology such as microfluidic chip,LCO can better preserve the biological characteristics and functions of tumor tissue,further improving high-throughput and automated drug sensitivity experiment.In this review,we combine the latest research progress to summarize the applica-tion progress and challenges of LCO in precision medicine for lung cancer.

The pathogeneses of oral squamous cell carcinoma and most oral mucosal diseases are unclear. Therefore, establishing animal models with similar pathogeneses is significant for clinical prevention, diagnosis, and treatment of related diseases. At present, scholars have established animal models for different focuses. This paper aims to introduce the methods for establishing animal models of oral squamous cell carcinoma and common oral mucosal diseases, compare their advantages and disadvantages, and provide evidence for related basic research.

Objective:To evaluate the associations between the renalase single-nucleotide polymorphisms rs2576178 and rs10887800 and the risk of hypertension in OSA patients. Methods:A total of 3, 570 male OSA subjects diagnosed via standard polysomnography were included in this retrospective study. We recorded anthropometric, genomic, and polysomnographic parameters and blood pressure levels. All subjects were divided into four groups based on quartiles of the apnea-hypopnea index (AHI). The relationships between rs2576178 and rs10887800 and the risk of hypertension were evaluated using the binary logistic regression, and haplotype analysis.Results:In the bottom AHI quartile, rs10887800 was significantly associated with the risk of hypertension according to the dominant model [odds ratio( OR)=0.691, 95% confidence interval ( CI)=0.483-0.990, P=0.044] even after adjustment for age, sex, and the body mass index. The G-A haplotype was associated with a co-effect of the two SNPs, namely, the risk of hypertension decreased ( OR=0.879, 95% CI=0.784-0.986, P=0.028). Conclusions:We find no association between single rs2576178 or rs10887800 variants with the risk of hypertension in our OSA population. But, the synergistic effect of the two polymorphisms is associated with the risk of hypertension in OSA patients.

Lung cancer is one of the leading causes of cancer-related morbidity and mortality. Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) have become the standard treatment for EGFR-mutated advanced non-small cell lung cancer (NSCLC). Unfortunately, drug resistance is inevitable in most cases. EGFR-TKI combined with angiogenesis inhibitors is a treatment scheme being explored to delay the therapeutic resistance, which is called "A+T treatment". Several clinical trials have demonstrated that the A+T treatment can improve the progression free survival (PFS) of the NSCLC patients. However, compared to EGFR-TKI monotherapy, the benefits of the A+T treatment based on different EGFR-TKIs, as well as its safety and exploration prospects are still unclear. Therefore, we reviewed the literature related to all three generations EGFR-TKIs combined with angiogenesis inhibitors, and summarized the mechanism, benefit, safety, optimal target population of A+T treatment.
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Angiogenesis Inhibitors/therapeutic use* ; Carcinoma, Non-Small-Cell Lung/genetics* ; ErbB Receptors/genetics* ; Humans ; Lung Neoplasms/genetics* ; Mutation ; Protein Kinase Inhibitors/therapeutic use*

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.