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196 results on '"Wu, Lei-Lei"'

7. Macro CD5L+ deteriorates CD8+T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma

Author

Lu, Jia-Cheng, Wu, Lei-Lei, Sun, Yi-Ning, Huang, Xiao-Yong, Gao, Chao, Guo, Xiao-Jun, Zeng, Hai-Ying, Qu, Xu-Dong, Chen, Yi, Wu, Dong, Pei, Yan-Zi, Meng, Xian-Long, Zheng, Yi-Min, Liang, Chen, Zhang, Peng-Fei, Cai, Jia-Bin, Ding, Zhen-Bin, Yang, Guo-Huan, Ren, Ning, Huang, Cheng, Wang, Xiao-Ying, Gao, Qiang, Sun, Qi-Man, Shi, Ying-Hong, Qiu, Shuang-Jian, Ke, Ai-Wu, Shi, Guo-Ming, Zhou, Jian, Sun, Yi-Di, and Fan, Jia

Published
2024
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21. The clinical-histological and prognostic characteristics in patients with second primary non-small cell lung cancer after lobectomy

Author

Wu, Lei-Lei, primary, Wang, Rang-Rang, additional, Qian, Jia-Yi, additional, Liu, Yu’e, additional, Ma, Shang-Shang, additional, Li, Ming-Jun, additional, Xie, Long-Yan, additional, Li, Zhi-Xin, additional, Li, Kun, additional, Sheng, Bing-Yong, additional, Ding, Jun-Rong, additional, and Xie, Dong, additional

Published
2023
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23. Macro CD5L+ deteriorates CD8+T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma.

Author

Lu, Jia-Cheng, Wu, Lei-Lei, Sun, Yi-Ning, Huang, Xiao-Yong, Gao, Chao, Guo, Xiao-Jun, Zeng, Hai-Ying, Qu, Xu-Dong, Chen, Yi, Wu, Dong, Pei, Yan-Zi, Meng, Xian-Long, Zheng, Yi-Min, Liang, Chen, Zhang, Peng-Fei, Cai, Jia-Bin, Ding, Zhen-Bin, Yang, Guo-Huan, Ren, Ning, and Huang, Cheng

Subjects
CHOLANGIOCARCINOMA, T cells, IMMUNITY, CD8 antigen, TUMOR microenvironment, OXALIPLATIN
Abstract

Intratumoral immune status influences tumor therapeutic response, but it remains largely unclear how the status determines therapies for patients with intrahepatic cholangiocarcinoma. Here, we examine the single-cell transcriptional and TCR profiles of 18 tumor tissues pre- and post- therapy of gemcitabine plus oxaliplatin, in combination with lenvatinib and anti-PD1 antibody for intrahepatic cholangiocarcinoma. We find that high CD8 GZMB+ and CD8 proliferating proportions and a low Macro CD5L+ proportion predict good response to the therapy. In patients with a poor response, the CD8 GZMB+ and CD8 proliferating proportions are increased, but the CD8 GZMK+ proportion is decreased after the therapy. Transition of CD8 proliferating and CD8 GZMB+ to CD8 GZMK+ facilitates good response to the therapy, while Macro CD5L+–CD8 GZMB+ crosstalk impairs the response by increasing CTLA4 in CD8 GZMB+. Anti-CTLA4 antibody reverses resistance of the therapy in intrahepatic cholangiocarcinoma. Our data provide a resource for predicting response of the combination therapy and highlight the importance of CD8+T-cell status conversion and exhaustion induced by Macro CD5L+ in influencing the response, suggesting future avenues for cancer treatment optimization. The role of the tumor microenvironment in immunotherapy response in intrahepatic cholangiocarcinoma remains unclear. Here, single cell RNA and TCR sequencing of samples before and after immunotherapy highlights the role of CD8 T-cell status conversion and exhaustion induced by Macro CD5L+ in treatment response. [ABSTRACT FROM AUTHOR]

Published
2024
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30. A Nomogram Based on Atelectasis/Obstructive Pneumonitis Could Predict the Metastasis of Lymph Nodes and Postoperative Survival of Pathological N0 Classification in Non-small Cell Lung Cancer Patients

Author

Liu, Yi-Han, primary, Wu, Lei-Lei, additional, Qian, Jia-Yi, additional, Li, Zhi-Xin, additional, Shi, Min-Xing, additional, Wang, Zi-Ran, additional, Xie, Long-Yan, additional, Liu, Yu’e, additional, Xie, Dong, additional, and Cao, Wei-Jun, additional

Published
2023
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32. The postoperative prognosis of skip-N2 metastasis is favorable in small-cell lung carcinoma patients with pathological N2 classification: a propensity-score-adjusted retrospective multicenter study

Author

Wu, Lei-Lei, primary, Liang, Shen-Hua, additional, Jiang, Feng, additional, Qiu, Li-Hong, additional, Chen, Xiaolu, additional, Yu, Wan-Jun, additional, Li, Chong-Wu, additional, Qian, Jia-Yi, additional, Huang, Yang-Yu, additional, Lin, Peng, additional, Long, Hao, additional, Li, Zhi-Xin, additional, Li, Kun, additional, Ma, Guo-Wei, additional, and Xie, Dong, additional

Published
2023
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33. The characterization of tumor microenvironment infiltration and the construction of predictive index based on cuproptosis-related gene in primary lung adenocarcinoma

Author

Li, Kun, primary, Wu, Lei-Lei, additional, Wang, Hui, additional, Cheng, Hao, additional, Zhuo, Hui-Min, additional, Hao, Yun, additional, Liu, Zhi-Yuan, additional, Li, Chong-Wu, additional, Qian, Jia-Yi, additional, Li, Zhi-Xin, additional, Xie, Dong, additional, and Chen, Chang, additional

Published
2022
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35. Additional file 2 of Reconsidering N component of cancer staging for T1-2N0-2M0 small-cell lung cancer: a retrospective study based on multicenter cohort

Author

Wu, Lei-Lei, Qiu, Li-Hong, Chen, Xiaolu, Yu, Wan-Jun, Li, Chong-Wu, Qian, Jia-Yi, Liang, Shen-Hua, Lin, Peng, Long, Hao, Zhang, Lan-Jun, Li, Zhi-Xin, Li, Kun, Jiang, Feng, Ma, Guo-Wei, and Xie, Dong

Abstract

Supplementary Material 2: The X-tile software determined the optimal cutoff point of LNR. LNR: lymph node ratio

Published
2023
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36. Establishment and validation of a prognostic risk classification for patients with stage T1-3N0M0 esophageal squamous cell carcinoma.

Author

Huang, Yang-Yu, Zheng, Yan, Liang, Shen-Hua, Wu, Lei-Lei, Liu, Xuan, Xing, Wen-Qun, and Ma, Guo-Wei

Subjects
ESOPHAGEAL cancer, SQUAMOUS cell carcinoma, PROGNOSTIC models, CANCER prognosis, REGRESSION analysis, LOGISTIC regression analysis
Abstract

Introduction: At present, clinical factors and hematological indicators have been proved to have great potential in predicting the prognosis of cancer patients, and no one has combined these two valuable indicators to establish a prognostic model for esophageal squamous cell carcinoma (ESCC) patients with stage T1-3N0M0 after R0 resection. To verify, we aimed to combine these potential indicators to establish a prognostic model. Methods: Stage T1-3N0M0 ESCC patients from two cancer centers (including training cohort: N = 819, and an external validation cohort: N = 177)—who had undergone esophagectomy in 1995–2015 were included. We integrated significant risk factors for death events by multivariable logistic regression methods and applied them to the training cohort to build Esorisk. The parsimonious aggregate Esorisk score was calculated for each patient; the training set was divided into three prognostic risk classes according to the 33rd and 66th percentiles of the Esorisk score. The association of Esorisk with cancer-specific survival (CSS) was assessed using Cox regression analyses. Results: The Esorisk model was: [10 + 0.023 × age + 0.517 × drinking history − 0.012 × hemoglobin–0.042 × albumin − 0.032 × lymph nodes]. Patients were grouped into three classes—Class A (5.14–7.26, low risk), Class B (7.27–7.70, middle risk), and Class C (7.71–9.29, high risk). In the training group, five-year CSS decreased across the categories (A: 63%; B: 52%; C: 30%, Log-rank P < 0.001). Similar findings were observed in the validation group. Additionally, Cox regression analysis showed that Esorisk aggregate score remained significantly associated with CSS in the training cohort and validation cohort after adjusting for other confounders. Conclusions: We combined the data of two large clinical centers, and comprehensively considered their valuable clinical factors and hematological indicators, established and verified a new prognostic risk classification that can predict CSS of stage T1-3N0M0 ESCC patients. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Nomogram predicts the prognosis of patients with thymic carcinoma: A population-based study using SEER data

Author

Huang, Yang-Yu, Liu, Xuan, Liang, Shen-Hua, Wu, Lei-Lei, and Ma, Guo-Wei

Abstract

Background: Thymic carcinoma (TC) is a rare malignant tumor that can have a poor prognosis, and accurate prognostication prediction remains difficult. We aimed to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) based on a large cohort of patients.Methods: The Surveillance Epidemiology and End Results (SEER) database was searched to identify TC patients (1975–2016). Univariate and multivariable Cox regression analyses were used to identify predictors of OS and CSS, which were used to construct nomograms. The nomograms were evaluated using the concordance index (C-index), calibration curve, receiver operating characteristic curve, and decision curve analysis (DCA). Subgroup analysis was performed to identify high-risk patients.Results: The analysis identified six predictors of OS (Masaoka stage, surgical method, lymph node metastasis, liver metastasis, bone metastasis, and radiotherapy) and five predictors of CSS (Masaoka stage, surgical method, lymph node metastasis, tumor size, and brain metastasis), which were used to create nomograms for predicting three-year and five-year OS and CSS. The nomograms had reasonable C-index values (OS: 0.687 [training] and 0.674 [validation], CSS: 0.712 [training] and 0.739 [validation]). The DCA curve revealed that the nomograms were better for predicting OS and CSS, relative to the Masaoka staging system.Conclusion: We developed nomograms using eight clinicopathological factors that predicted OS and CSS among TC patients. The nomograms performed better than the traditional Masaoka staging system and could identify high-risk patients. Based on the nomograms’ performance, we believe they will be useful prognostication tools for TC patients.

Published
2023
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50. Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis

Author

Wu, Lei-Lei, primary, Wang, Jin-Long, additional, Huang, Wei, additional, Liu, Xuan, additional, Huang, Yang-Yu, additional, Zeng, Jing, additional, Cui, Chun-Yan, additional, Lu, Jia-Bin, additional, Lin, Peng, additional, Long, Hao, additional, Zhang, Lan-Jun, additional, Wei, Jun, additional, Lu, Yao, additional, and Ma, Guo-Wei, additional

Published
2021
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