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Xia T, Yuan Q, Xing SG. STAS: New explorations and challenges for thoracic surgeons. Clin Transl Oncol 2024:10.1007/s12094-024-03681-4. [PMID: 39230858 DOI: 10.1007/s12094-024-03681-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024]
Abstract
Spread through air spaces (STAS) represents a relatively novel concept in the pathology of lung cancer, and it specifically refers to the dissemination of tumour cells into the parenchymal air spaces adjacent to the primary tumour. In 2015, the World Health Organization (WHO) classified STAS as a new invasive form of lung adenocarcinoma (LUAD). Many studies investigated the role of STAS and revealed its association with the prognosis of LUAD and its influence on the outcomes of other malignant pulmonary neoplasms. Additionally, the underlying mechanisms and predictive models of STAS have received considerable attention in recent years. This paper provides a comprehensive overview of the research advancements and prospects of STAS by examining it from multiple perspectives.
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Affiliation(s)
- Teng Xia
- Department of Thoracic Surgery, Nan Jing Gaochun People's Hospital, The Gaochun Affiliated Hospital of Jiang Su University), Nanjing, 210000, Jiangsu, China
| | - Qian Yuan
- Department of Thoracic Surgery, Nan Jing Gaochun People's Hospital, The Gaochun Affiliated Hospital of Jiang Su University), Nanjing, 210000, Jiangsu, China
| | - Shi-Gui Xing
- Department of Thoracic Surgery, Nan Jing Gaochun People's Hospital, The Gaochun Affiliated Hospital of Jiang Su University), Nanjing, 210000, Jiangsu, China.
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Yildirim S, Alan O, Yuksel Yasar Z, Kaya T, Akdag G, Kinikoglu O, Gecmen GG, Yasar A, Isik D, Surmeli H, Basoglu T, Sever ON, Yildirim ME, Odabas H, Turan N. Prognostic Impact and Clinical Features of Spread through Air Spaces in Operated Lung Cancer: Real-World Analysis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1374. [PMID: 39202654 PMCID: PMC11356374 DOI: 10.3390/medicina60081374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 09/03/2024]
Abstract
Background and Objectives: Lung cancer is the leading cause of cancer-related deaths. Spread through air spaces (STAS) is an adverse prognostic factor that has become increasingly known in recent years. This study aims to investigate the impact of STAS presence on overall survival (OS) and disease-free survival (DFS) in patients with surgically resected stage IA-IIIA lung cancer and to identify clinicopathological features associated with STAS. Materials and Methods: This research involved 311 lung cancer surgery patients. The relationship between the presence of STAS in the patients' surgical pathology and OS and DFS values was examined. Clinicopathological features associated with the presence of STAS were determined. Results: There were 103 (33%) STAS-positive patients. Adenocarcinoma histological subtype, perineural invasion (PNI), and lymphovascular invasion (LVI) were significantly correlated with being STAS positive. STAS significantly predicted DFS and OS. One-year and five-year DFS rates were significantly lower in the STAS-positive group compared to the STAS-negative group (65% vs. 88%, 29% vs. 62%, respectively, p ≤ 0.001). Similarly, one-year and five-year OS rates were significantly lower in the STAS-positive group compared to the STAS-negative group (92% vs. 94%, 54% vs. 88%, respectively, p ≤ 0.001). In multivariate analysis, STAS was found to be an independent prognostic factor for both DFS and OS (HR: 3.2 (95%CI: 2.1-4.8) and 3.1 (95%CI: 1.7-5.5), p < 0.001 and <0.001, respectively). Conclusions: In our study, STAS was found to be an independent prognostic biomarker in operated stage IA-IIIA lung cancer patients. It may be a beneficial pathological biomarker in predicting the survival of patients and managing their treatments.
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Affiliation(s)
- Sedat Yildirim
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Ozkan Alan
- Division of Medical Oncology, School of Medicine, Koc University, Istanbul 34450, Turkey;
| | - Zeynep Yuksel Yasar
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Tugba Kaya
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Goncagul Akdag
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Oguzcan Kinikoglu
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Gonca Gul Gecmen
- Department of Pathology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey;
| | - Alper Yasar
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Deniz Isik
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Heves Surmeli
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Tugba Basoglu
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Ozlem Nuray Sever
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Mahmut Emre Yildirim
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Hatice Odabas
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
| | - Nedim Turan
- Department of Medical Oncology, Health Science University, Kartal Dr. Lütfi Kirdar City Hospital, Istanbul 34865, Turkey; (Z.Y.Y.); (T.K.); (G.A.); (O.K.); (A.Y.); (D.I.); (H.S.); (T.B.); (O.N.S.); (M.E.Y.); (H.O.); (N.T.)
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Feng Y, Ding H, Huang X, Zhang Y, Lu M, Zhang T, Wang H, Chen Y, Mao Q, Xia W, Chen B, Zhang Y, Chen C, Gu T, Xu L, Dong G, Jiang F. Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma. NPJ Precis Oncol 2024; 8:173. [PMID: 39103596 DOI: 10.1038/s41698-024-00664-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 07/25/2024] [Indexed: 08/07/2024] Open
Abstract
Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72-0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.
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Affiliation(s)
- Yipeng Feng
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Hanlin Ding
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Xing Huang
- Pathological Department of Jiangsu Cancer Hospital, Nanjing, P. R. China
| | - Yijian Zhang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Mengyi Lu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Te Zhang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Hui Wang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Yuzhong Chen
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Qixing Mao
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Wenjie Xia
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Bing Chen
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Yi Zhang
- Pathological Department of Jiangsu Cancer Hospital, Nanjing, P. R. China
| | - Chen Chen
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Tianhao Gu
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Lin Xu
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Gaochao Dong
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China.
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China.
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China.
| | - Feng Jiang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 21009, Nanjing, China.
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China.
- The Fourth Clinical College of Nanjing Medical University, Nanjing, China.
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Travis WD, Eisele M, Nishimura KK, Aly RG, Bertoglio P, Chou TY, Detterbeck FC, Donnington J, Fang W, Joubert P, Kernstine K, Kim YT, Lievens Y, Liu H, Lyons G, Mino-Kenudson M, Nicholson AG, Papotti M, Rami-Porta R, Rusch V, Sakai S, Ugalde P, Van Schil P, Yang CFJ, Cilento VJ, Yotsukura M, Asamura H. The International Association for the Study of Lung Cancer (IASLC) Staging Project for Lung Cancer: Recommendation to Introduce Spread Through Air Spaces as a Histologic Descriptor in the Ninth Edition of the TNM Classification of Lung Cancer. Analysis of 4061 Pathologic Stage I NSCLC. J Thorac Oncol 2024; 19:1028-1051. [PMID: 38508515 DOI: 10.1016/j.jtho.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/06/2024] [Accepted: 03/13/2024] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Spread through air spaces (STAS) consists of lung cancer tumor cells that are identified beyond the edge of the main tumor in the surrounding alveolar parenchyma. It has been reported by meta-analyses to be an independent prognostic factor in the major histologic types of lung cancer, but its role in lung cancer staging is not established. METHODS To assess the clinical importance of STAS in lung cancer staging, we evaluated 4061 surgically resected pathologic stage I R0 NSCLC collected from around the world in the International Association for the Study of Lung Cancer database. We focused on whether STAS could be a useful additional histologic descriptor to supplement the existing ones of visceral pleural invasion (VPI) and lymphovascular invasion (LVI). RESULTS STAS was found in 930 of 4061 of the pathologic stage I NSCLC (22.9%). Patients with tumors exhibiting STAS had a significantly worse recurrence-free and overall survival in both univariate and multivariable analyses involving cohorts consisting of all NSCLC, specific histologic types (adenocarcinoma and other NSCLC), and extent of resection (lobar and sublobar). Interestingly, STAS was independent of VPI in all of these analyses. CONCLUSIONS These data support our recommendation to include STAS as a histologic descriptor for the Ninth Edition of the TNM Classification of Lung Cancer. Hopefully, gathering these data in the coming years will facilitate a thorough analysis to better understand the relative impact of STAS, LVI, and VPI on lung cancer staging for the Tenth Edition TNM Stage Classification.
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Affiliation(s)
- William D Travis
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Megan Eisele
- Cancer Research And Biostatistics (CRAB), Seattle, Washington
| | | | - Rania G Aly
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pietro Bertoglio
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Teh-Ying Chou
- Department of Pathology and Laboratory Medicine, Taipei, Veterans General Hospital, Taipei, Taiwan
| | | | | | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Jiaotong University Medical School, Shanghai, People's Republic of China
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec - Université Laval, Quebec City, Canada
| | - Kemp Kernstine
- Department of Cardiovascular and Thoracic Surgery, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Young Tae Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yolande Lievens
- Radiation Oncology, Ghent University Hospital and Ghent University, Gent, Belgium
| | - Hui Liu
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangdong, People's Republic of China
| | - Gustavo Lyons
- Buenos Aires British Hospital, Buenos Aires, Argentina
| | - Mari Mino-Kenudson
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew G Nicholson
- Department of Histopathology, Royal Brompton Hospital, London, United Kingdom
| | - Mauro Papotti
- Department of Oncology, University of Turin, Torino, Italy
| | - Ramon Rami-Porta
- Department of Thoracic Surgery, Hospital Universitari Mútua Terrassa, University of Barcelona, and CIBERES Lung Cancer Group, Terrassa, Barcelona, Spain
| | - Valerie Rusch
- Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Shuji Sakai
- Tokyo Women's Medical University, Tokyo, Japan
| | - Paula Ugalde
- Brigham & Women's Hospital, Boston, Massachusetts
| | - Paul Van Schil
- Antwerp University and Antwerp University Hospital, (Edegem) Antwerp, Belgium
| | - Chi-Fu Jeffrey Yang
- Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | | | - Masaya Yotsukura
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Hisao Asamura
- Department of Thoracic Surgery, Keio University, Tokyo, Japan
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Ye G, Wu G, Li K, Zhang C, Zhuang Y, Liu H, Song E, Qi Y, Li Y, Yang F, Liao Y. Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography. Acad Radiol 2024; 31:1686-1697. [PMID: 37802672 DOI: 10.1016/j.acra.2023.08.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 10/08/2023]
Abstract
RATIONALE AND OBJECTIVES To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. MATERIALS AND METHODS The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. RESULTS The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules. CONCLUSION Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.
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Affiliation(s)
- Guanchao Ye
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.)
| | - Guangyao Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.W., F.Y.)
| | - Kuo Li
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.)
| | - Chi Zhang
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.)
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Y.Z., H.L., E.S.)
| | - Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Y.Z., H.L., E.S.)
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Y.Z., H.L., E.S.)
| | - Yu Qi
- Department of Thoracic Surgery of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (Y.Q.)
| | - Yiying Li
- Department of Breast Surgery of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (Y.L.)
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.W., F.Y.)
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.).
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Lin SS, Chang TM, Wei AIC, Lee CW, Lin ZC, Chiang YC, Chi MC, Liu JF. Acetylshikonin induces necroptosis via the RIPK1/RIPK3-dependent pathway in lung cancer. Aging (Albany NY) 2023; 15:14900-14914. [PMID: 38126996 PMCID: PMC10781480 DOI: 10.18632/aging.205316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/06/2023] [Indexed: 12/23/2023]
Abstract
Despite advances in therapeutic strategies, lung cancer remains the leading cause of cancer-related death worldwide. Acetylshikonin is a derivative of the traditional Chinese medicine Zicao and presents a variety of anticancer properties. However, the effects of acetylshikonin on lung cancer have not been fully understood yet. This study explored the mechanisms underlying acetylshikonin-induced cell death in non-small cell lung cancer (NSCLC). Treating NSCLC cells with acetylshikonin significantly reduced cell viability, as evidenced by chromatin condensation and the appearance of cell debris. Acetylshikonin has also been shown to increase cell membrane permeability and induce cell swelling, leading to an increase in the population of necrotic cells. When investigating the mechanisms underlying acetylshikonin-induced cell death, we discovered that acetylshikonin promoted oxidative stress, decreased mitochondrial membrane potential, and promoted G2/M phase arrest in lung cancer cells. The damage to NSCLC cells induced by acetylshikonin resembled results involving alterations in the cell membrane and mitochondrial morphology. Our analysis of oxidative stress revealed that acetylshikonin induced lipid oxidation and down-regulated the expression of glutathione peroxidase 4 (GPX4), which has been associated with necroptosis. We also determined that acetylshikonin induces the phosphorylation of receptor-interacting serine/threonine-protein kinase 1 (RIPK1)/RIPK3 and mixed lineage kinase domain-like kinase (MLKL). Treatment with RIPK1 inhibitors (necrostatin-1 or 7-Cl-O-Nec-1) significantly reversed acetylshikonin-induced MLKL phosphorylation and NSCLC cell death. These results indicate that acetylshikonin activated the RIPK1/RIPK3/MLKL cascade, leading to necroptosis in NSCLC cells. Our findings indicate that acetylshikonin reduces lung cancer cells by promoting G2/M phase arrest and necroptosis.
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Affiliation(s)
- Shih-Sen Lin
- Division of Chest Medicine, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
| | - Tsung-Ming Chang
- Translational Medicine Center, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Augusta I-Chin Wei
- Translational Medicine Center, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
| | - Chiang-Wen Lee
- Department of Orthopaedic Surgery, Chang Gung Memorial Hospital, Puzi City 613016, Taiwan
- Department of Nursing, Division of Basic Medical Sciences, Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Puzi City 613016, Taiwan
- Department of Safety Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
| | - Zih-Chan Lin
- Department of Nursing, Division of Basic Medical Sciences, Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Puzi City 613016, Taiwan
| | - Yao-Chang Chiang
- Department of Nursing, Division of Basic Medical Sciences, Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Puzi City 613016, Taiwan
| | - Miao-Ching Chi
- Department of Nursing, Division of Basic Medical Sciences, Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Puzi City 613016, Taiwan
- Division of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Chiayi 613016, Taiwan
- Department of Respiratory Care, Chang Gung University of Science and Technology, Chiayi 613016, Taiwan
| | - Ju-Fang Liu
- Translational Medicine Center, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
- School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404328, Taiwan
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Wang GX, Huang ZN, Ye YQ, Tao SM, Xu MQ, Zhang M, Xie MR. Prognostic analysis of the plasma fibrinogen combined with neutrophil-to-lymphocyte ratio in patients with non-small cell lung cancer after radical resection. Thorac Cancer 2023; 14:1383-1391. [PMID: 37037492 DOI: 10.1111/1759-7714.14883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND To investigate the correlation between the fibrinogen combined with neutrophil-to-lymphocyte ratio (F-NLR) and the clinicopathologic features of non-small cell lung cancer (NSCLC) patients who underwent radical resection. METHODS This study reviewed the medical records of 289 patients with NSCLC who underwent radical resection. The patients were stratified into three groups based on F-NLR as follows: patients with low NLR and fibrinogen were group A, patients with high NLR or fibrinogen were group B, and patients with high NLR and fibrinogen were group C. Receiver operating characteristic curve and Youden index were used to determine the cutoff value of the NLR and fibrinogen. Survival curves were described by Kaplan-Meier method and compared by log-rank test. The univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the prognostic factors. RESULTS A value of 3.19 was taken as the optimal cutoff value of NLR in this study. A value of 309 was used as the optimal cutoff value of fibrinogen. Cox multivariate analysis showed that tumor, nodes, metastasis (TNM) stage and F-NLR were independent prognostic factors affecting the survival rate of patients. The first-, third-, and fifth-year survival rates in group A were 99.2%, 96.6%, and 95.0%, respectively. The first-, third-, and fifth-year survival rates in group B were 98.4%, 76.6%, and 63.2%, respectively. The first-, third-, and fifth-year survival rates in group C were 91.3%, 41.1%, and 22.8%, respectively. F-NLR was significantly correlated with overall survival in patients with NSCLC (p < 0.001). CONCLUSIONS The F-NLR level is markedly related to the prognosis of patients with NSCLC undergoing radical surgery. Therefore, closer attention should be given to patients with NSCLC with a high F-NLR before surgery to provide postoperative adjuvant therapy.
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Affiliation(s)
- Gao-Xiang Wang
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Integrated Traditional Chinese and Western Medicine, Anhui Medical University, Hefei, China
| | - Zhi-Ning Huang
- Department of Thoracic Surgery, The First Affiliated Hospital of USTC, Hefei, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ying-Quan Ye
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Integrated Traditional Chinese and Western Medicine, Anhui Medical University, Hefei, China
| | - Shan-Ming Tao
- Department of Thoracic Surgery, The First Affiliated Hospital of USTC, Hefei, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Mei-Qing Xu
- Department of Thoracic Surgery, The First Affiliated Hospital of USTC, Hefei, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Mei Zhang
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Integrated Traditional Chinese and Western Medicine, Anhui Medical University, Hefei, China
| | - Ming-Ran Xie
- Department of Thoracic Surgery, The First Affiliated Hospital of USTC, Hefei, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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