1
|
Liu CQ, Yu ZB, Gan JX, Mei TM. Preoperative blood markers and intra-abdominal infection after colorectal cancer resection. World J Gastrointest Surg 2024; 16:451-462. [PMID: 38463368 PMCID: PMC10921215 DOI: 10.4240/wjgs.v16.i2.451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/16/2023] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) has one of the highest morbidity and mortality rates among digestive tract tumors. Intra-abdominal infection (IAI) is a common postoperative complication that affects the clinical outcomes of patients with CRC and hinders their rehabilitation process. However, the factors influencing abdominal infection after CRC surgery remain unclear; further, prediction models are rarely used to analyze preoperative laboratory indicators and postoperative complications. AIM To explore the predictive value of preoperative blood markers for IAI after radical resection of CRC. METHODS The data of 80 patients who underwent radical resection of CRC in the Anorectal Surgery Department of Suzhou Hospital affiliated with Anhui Medical University were analyzed. These patients were categorized into IAI (n = 15) and non-IAI groups (n = 65) based on whether IAI occurred. Influencing factors were compared; general data and laboratory indices of both groups were identified. The relationship between the indicators was assessed. Further, a nomogram prediction model was developed and evaluated; its utility and clinical applicability were assessed. RESULTS The risk factors for IAI after radical resection of CRC were neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and carcinoembryonic antigen (CEA) levels. NLR was correlated with PLR and SII (r = 0.604, 0.925, and 0.305, respectively), while PLR was correlated with SII (r = 0.787). The nomogram prediction model demonstrated an area under the curve of 0.968 [95% confidence interval (CI): 0.948-0.988] in the training set (n = 60) and 0.926 (95%CI: 0.906-0.980) in the validation set (n = 20). The average absolute errors of the calibration curves for the training and validation sets were 0.032 and 0.048, respectively, indicating a good model fit. The decision curve analysis curves demonstrated high net income above the 5% threshold, indicating the clinical practicality of the model. CONCLUSION The nomogram model constructed using NLR, PLR, SII, and CEA levels had good accuracy and reliability in predicting IAI after radical resection of CRC, potentially aiding clinical treatment decision-making.
Collapse
Affiliation(s)
- Chang-Qing Liu
- Department of Gastrointestinal Anorectal Surgery, Suzhou Hospital Affiliated to Anhui Medical University, Suzhou 234000, Anhui Province, China
| | - Zhong-Bei Yu
- Department of Gastrointestinal Anorectal Surgery, Suzhou Hospital Affiliated to Anhui Medical University, Suzhou 234000, Anhui Province, China
| | - Jin-Xian Gan
- Department of Gastrointestinal Anorectal Surgery, Suzhou Hospital Affiliated to Anhui Medical University, Suzhou 234000, Anhui Province, China
| | - Tian-Ming Mei
- Department of Gastrointestinal Anorectal Surgery, Suzhou Hospital Affiliated to Anhui Medical University, Suzhou 234000, Anhui Province, China
| |
Collapse
|
2
|
Yang Y, Xu P, Zhang C. Construction of the survival nomograms for colon cancer patients of different ages based on the SEER database. J Cancer Res Clin Oncol 2023; 149:15395-15406. [PMID: 37639008 PMCID: PMC10620318 DOI: 10.1007/s00432-023-05323-8] [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: 06/18/2023] [Accepted: 08/18/2023] [Indexed: 08/29/2023]
Abstract
INTRODUCTION Three nomograms for predicting the outcomes of early- and late-onset colon cancer (COCA) among patients not stratified by age were constructed using data in the Epidemiology and End Results (SEER) database (1975-2019). The accuracy of the nomogram was then assessed. METHOD Clinical data of 6107 patients with COCA were obtained from the SEER database. The patients were randomly divided into training and validation cohorts in a ratio of 7:3. Univariate and multivariate COX analyses of factors that could independently impact the prognosis of COCA were performed, and the corresponding nomograms for early-onset and late-onset COCA were constructed. Calibration curves, ROC curves, and C-index were used to determine the predictive accuracy. The discriminatory ability of the nomograms to assess their clinical utility, which was compared with the TNM staging system of the 8th edition of AJCC, was verified using survival analysis. RESULT Tumor primary site, ethnicity, and serum carcinoembryonic antigen (CEA) level significantly impacted the prognosis of colon cancer. Race, brain metastasis, and CEA were independent factors for predicting COCA prognosis. C-index, ROC, and calibration curves demonstrated that the three nomograms were accurate and superior to the traditional TNM staging system. Among the three nomograms, the early-onset COCA nomogram had the highest predictive accuracy, followed by that of colon cancer not stratified by age. CONCLUSION Three nomograms for patients not stratified by age, early-onset colon cancer, and late-onset colon cancer were constructed. The accuracies of the nomograms were good and were all superior to the conventional TNM staging system. The early- and late-onset COCA nomograms are useful for clinical management and individualized treatment of COCA patients at different ages.
Collapse
Affiliation(s)
- Yuzhou Yang
- Department of General Surgery, General Hospital of Northern Theater Command (General Hospital of Shenyang Military Command), Shenyang, Liaoning Province, China
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Peng Xu
- Department of General Surgery, General Hospital of Northern Theater Command (General Hospital of Shenyang Military Command), Shenyang, Liaoning Province, China.
| | - Cheng Zhang
- Department of General Surgery, General Hospital of Northern Theater Command (General Hospital of Shenyang Military Command), Shenyang, Liaoning Province, China.
- Jinzhou Medical University, Jinzhou, Liaoning Province, China.
| |
Collapse
|
3
|
Liao Z, Deng Y, Zhou J, Zhu J, Xia R. A competing risk nomogram to predict cancer-specific mortality of patients with late-onset colorectal cancer. J Cancer Res Clin Oncol 2023; 149:14025-14033. [PMID: 37548769 DOI: 10.1007/s00432-023-05069-3] [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: 05/21/2023] [Accepted: 06/29/2023] [Indexed: 08/08/2023]
Abstract
OBJECTIVE This study aimed to compare the clinical characteristics and survival differences between early-onset colorectal cancer (EOCRC) patients and late-onset colorectal cancer (LOCRC) patients, identify the risk factors for cancer-specific mortality (CSM) in LOCRC patients and construct a mortality risk assessment nomogram. METHODS CRC patients diagnosed pathologically between 2010 and 2019 in the SEER database were included and divided into the early-onset group and the late-onset group, and the late-onset group was divided into the training and validation sets. The Fine-Gray competing risk model was applied to analyze the prognostic factors of LOCRC patients and establish a competing risk nomogram for CSM. RESULTS There are differences in the distribution of multiple clinical features between the early-onset group and the late-onset group. Age, tumor size, histological type, pathological grading, T stage, N stage, M stage, SEER stage, primary tumor surgery, metastatic lesion surgery, radiotherapy, chemotherapy, neural invasion, and carcinoembryonic antigen (CEA) were independent influencing factors of the CSM rate in LOCRC patients. The C-index of the prognosis model outweighed 0.8, and the calibration curves fitted the reference line well. CONCLUSION The CSM competing risk nomogram for LOCRC patients in this study had acceptable predictive performance that could be applied to the clinic.
Collapse
Affiliation(s)
- Zhixiao Liao
- The First Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Guangzhou, China
| | - Yueyang Deng
- Intensive Care Unit, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jingxu Zhou
- Department of Oncology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Jinli Zhu
- Department of Oncology, The First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- The National Clinical Medical Research Center for Acupuncture of Traditional Chinese Medicine, Tianjin, China
| | - Rui Xia
- Intensive Care Unit, Tianjin Cancer Hospital Airport Hospital, Tianjin, China.
| |
Collapse
|
4
|
Wu Z, Liu C, Ma Z, Li Z, Wang S, Chen Y, Han M, Huang S, Zhou Q, Zhang C, Hou B. A hierarchical prognostic model for Co-diabetes pancreatic adenocarcinoma. Heliyon 2023; 9:e21642. [PMID: 38027595 PMCID: PMC10663840 DOI: 10.1016/j.heliyon.2023.e21642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Co-diabetes pancreatic adenocarcinoma has a poorer prognosis than pancreatic adenocarcinoma without diabetes. This study aimed to develop a reliable prognostic model for patients with co-diabetes pancreatic adenocarcinoma. Method Overall, 169 patients with co-diabetes pancreatic adenocarcinoma were included in our study. First, the independent risk factors affecting the prognosis of patients with co-diabetes pancreatic adenocarcinoma were determined by univariate and multivariate Cox regression analyses. Based on these identified risk factors, we developed a nomogram and evaluated its predictive ability using the concordance index, receiver operating characteristic curve, calibration plot, decision curve, and net reclassification index. Results In this study, prealbumin, transferrin, carcinoembryonic antigen, distant metastasis, tumor differentiation neutrophil count, lymphocyte count and fasting blood glucose were confirmed as significant prognostic factors. Based on these predictors, a new nomogram was developed. Compared with the American Joint Committee on Cancer 8 staging system and other models, the nomogram achieved a higher concordance index in the training (0.795) and validation (0.729) queues. The area under the nomogram's curve for predicting patient survival at 0.5, 1, and 1.5 years in the training queue was >0.8. Patients were risk-stratified using the nomogram, and Kaplan-Meier survival curves of subgroups were plotted. The Kaplan-Meier curve also showed better separation than the American Joint Committee on Cancer 8 staging system, indicating that our model has a better risk hierarchical ability. Conclusions Compared to the American Joint Committee on Cancer 8 staging system and other predictive models, our model showed better predictive ability for patients with co-diabetes pancreatic adenocarcinoma. Our model will help in patients' risk stratification and improves their prognosis.
Collapse
Affiliation(s)
- Zelong Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Chunsheng Liu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Zuyi Ma
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100005, China
| | - Zhenchong Li
- German Cancer Research Center (DKFZ), Junior Clinical Cooperation Unit Translational Gastrointestinal Oncology and Preclinical Models, Heidelberg, Germany
| | - Shujie Wang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Yubin Chen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| | - Mingqian Han
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
| | - Shanzhou Huang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| | - Qi Zhou
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
- Department of General Surgery, Hui Ya Hospital of the First Affiliated Hospital, Sun Yat-sen University, Huizhou, Guangdong 516081, China
| | - Chuanzhao Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| | - Baohua Hou
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Heyuan People's Hospital, Heyuan 517000, China
- South China University of Technology School of Medicine, Guangzhou 51000, China
| |
Collapse
|
5
|
Pîrvu EE, Severin E, Pătru RI, Niță I, Toma SA, Macarie RR, Cocioabă CE, Florescu I, Coniac S. Correlations between Demographic, Clinical, and Paraclinical Variables and Outcomes in Patients with KRAS-Mutant or KRAS Wild-Type Metastatic Colorectal Cancer-A Retrospective Study from a Tertiary-Level Center in Romania. Diagnostics (Basel) 2023; 13:2930. [PMID: 37761297 PMCID: PMC10528401 DOI: 10.3390/diagnostics13182930] [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: 08/01/2023] [Revised: 08/16/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Colorectal cancer (CRC) is a significant global public health concern and its characteristics in Eastern Europe are underexplored. In this retrospective study, data of 225 patients with metastatic colorectal cancer (mCRC) from the Colțea Clinical Hospital's Oncology Department in Bucharest were analyzed between 2015 and 2023. They were divided into two groups based on the presence of KRAS mutation. The primary objective of the study was to investigate whether the presence of KRAS mutations influenced the prognosis of mCRC and to identify any demographic, clinical, or paraclinical factors associated with KRAS mutations in stage IV CRC. The overall survival for the entire study population was 29 months. There was a trend towards increased survival in the KRAS wild-type group (31 months) compared to the KRAS-mutant group (26 months), but this difference did not reach statistical significance. We found that lower levels of education, advanced T stage, advanced N stage, and M1 stage at diagnosis negatively impacted prognosis. Real-world data are crucial in shaping public policy strategies to better support patients with metastatic CRC. Understanding the correlations between the demographic, clinical, and paraclinical variables and the outcomes in mCRC patients with KRAS-mutant and KRAS wild-type colorectal cancer is essential for improving patient care and treatment strategies in Romania and beyond.
Collapse
Affiliation(s)
- Edvina Elena Pîrvu
- Department of Genetics, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | - Emilia Severin
- Department of Genetics, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Raluca Ileana Pătru
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | - Irina Niță
- Department of Medical Oncology, Medicover Hospital, 020331 Bucharest, Romania
| | - Stefania Andreea Toma
- Department of Medical Oncology, Ponderas Academic Hospital, 014142 Bucharest, Romania
| | - Roxana Rodica Macarie
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | | | - Ioana Florescu
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| | - Simona Coniac
- Department of Medical Oncology, “Coltea” Clinical Hospital, 030167 Bucharest, Romania
| |
Collapse
|
6
|
Ma Z, Yang S, Yang Y, Luo J, Zhou Y, Yang H. Development and validation of prediction models for the prognosis of colon cancer with lung metastases: a population-based cohort study. Front Endocrinol (Lausanne) 2023; 14:1073360. [PMID: 37583430 PMCID: PMC10424923 DOI: 10.3389/fendo.2023.1073360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/20/2023] [Indexed: 08/17/2023] Open
Abstract
Background Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients. Method Data on 1,284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with 7:3 (stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, the suitable predictors were entered into Cox proportional hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups. Results Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation, and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in the high-risk group than in the low-risk group, which suggested that the models had significant discrimination for patients with different prognoses. Conclusion After verification, our prediction models of CCLM are reliable and can predict the OS and CSS of CCLM patients in the next 1, 3, and 5 years, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.
Collapse
Affiliation(s)
| | | | | | | | | | - Huiyong Yang
- School of Medicine, Huaqiao University, Quanzhou, China
| |
Collapse
|
7
|
Zhang Y, Zhang Z, Wei L, Wei S. Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer. Front Public Health 2022; 10:1008137. [PMID: 36605237 PMCID: PMC9810140 DOI: 10.3389/fpubh.2022.1008137] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose The purpose of this study was to investigate the clinical and non-clinical characteristics that may affect the early death rate of patients with metastatic colorectal carcinoma (mCRC) and develop accurate prognostic predictive models for mCRC. Method Medical records of 35,639 patients with mCRC diagnosed from 2010 to 2019 were obtained from the SEER database. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. X-tile software was utilized to identify the optimal cutoff point for age and tumor size. Univariate and multivariate logistic regression models were used to determine the independent predictors associated with overall early death and cancer-specific early death caused by mCRC. Simultaneously, predictive and dynamic nomograms were constructed. Moreover, logistic regression, random forest, CatBoost, LightGBM, and XGBoost were used to establish machine learning (ML) models. In addition, receiver operating characteristic curves (ROCs) and calibration plots were obtained to estimate the accuracy of the models. Decision curve analysis (DCA) was employed to determine the clinical benefits of ML models. Results The optimal cutoff points for age were 58 and 77 years and those for tumor size of 45 and 76. A total of 15 independent risk factors, namely, age, marital status, race, tumor localization, histologic type, grade, N-stage, tumor size, surgery, radiation, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis, were significantly associated with the overall early death rate of patients with mCRC and the cancer-specific early death rate of patients with mCRC, following which nomograms were constructed. The ML models revealed that the random forest model accurately predicted outcomes, followed by logistic regression, CatBoost, XGBoost, and LightGBM models. Compared with other algorithms, the random forest model provided more clinical benefits than other models and can be used to make clinical decisions in overall early death and specific early death caused by mCRC. Conclusion ML algorithms combined with nomograms may play an important role in distinguishing early deaths owing to mCRC and potentially help clinicians make clinical decisions and follow-up strategies.
Collapse
Affiliation(s)
- Yalong Zhang
- Department of Ultrasound Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zunni Zhang
- Department of Clinical Laboratory, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Liuxiang Wei
- Department of Ultrasound Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shujing Wei
- Department of Ultrasound Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China,*Correspondence: Shujing Wei ✉
| |
Collapse
|
8
|
Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients. DISEASE MARKERS 2022; 2022:1289445. [PMID: 35937944 PMCID: PMC9352481 DOI: 10.1155/2022/1289445] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/06/2022] [Accepted: 07/15/2022] [Indexed: 11/17/2022]
Abstract
Background. The current high mortality rate of female breast cancer (BC) patients emphasizes the necessity of identifying powerful and reliable prognostic signatures in BC patients. Epithelial-mesenchymal transition (EMT) was reported to be associated with the development of BC. The purpose of this study was to identify prognostic biomarkers that predict overall survival (OS) in female BC patients by integrating data from TCGA database. Method. We first downloaded the dataset in TCGA and identified gene signatures by overlapping candidate genes. Differential analysis was performed to find differential EMT-related genes. Univariate regression analysis was then performed to identify candidate prognostic variables. We then developed a prognostic model by multivariate analysis to predict OS. Calibration curves, receiver operating characteristics (ROC) curves,
-index, and decision curve analysis (DCA) were used to test the veracity of the prognostic model. Result. In this study, we identified and validated a prognostic model integrating age and six genes (CD44, P3H1, SDC1, COL4A1, TGFβ1, and SERPINE1).
-index values for BC patients were 0.672 (95% CI 0.611–0.732) and 0.692 (95% CI 0.586–0.798) in the training cohort and test set, respectively. The calibration curve and the DCA curve show the good predictive performance of the model. Conclusion. This study offered a robust predictive model for OS prediction in female BC patients and may provide a more accurate treatment strategy and personalized therapy in the future.
Collapse
|
9
|
Chen G, Lian D, Zhao L, Wang Z, Wuyun Q, Zhang N. The long non-coding RNA T cell leukemia homeobox 1 neighbor enhances signal transducer and activator of transcription 5A phosphorylation to promote colon cancer cell invasion, migration, and metastasis. Bioengineered 2022; 13:11083-11095. [PMID: 35502613 PMCID: PMC9278427 DOI: 10.1080/21655979.2022.2068781] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Colon cancer is among the most prevalent gastrointestinal tumor types. The long noncoding RNA (lncRNA) T cell leukemia homeobox 1 neighbor (TLX1NB) is up-regulated in colorectal cancer (CRC). However, the functional role of this lncRNA in colon cancer remains unknown. In our study, we investigated the clinical significance of TLX1NB in colon cancer through bioinformatics analysis and explored its role in migration, invasion and metastasis of colon cancer cell with a series of experiments. Firstly, TLX1NB was up-regulated in colon cancer tissues and increased TLX1NB expression was significantly associated with advanced N stages. In wound healing assays and transwell assays, TLX1NB overexpression promoted HCT116 cell migration and invasion while TLX1NB knockdown inhibited SW620 cell migration and invasion. In vivo, TLX1NB knockdown suppressed pulmonary metastasis of SW620 cell and vimentin expression but increased E-cadherin expression. Then, TLX1NB overexpression enhanced signal transducer and activator of transcription 5A (STAT5A) phosphorylation and TLX1NB knockdown suppressed STAT5A phosphorylation. Moreover, the inhibition of STAT5A phosphorylation reversed TLX1NB overexpression-associated increase in HCT116 cell migratory and invasive activity. In conclusion, TLX1NB enhances STAT5A phosphorylation to promote colon cancer cell invasion, migration, and metastasis.
Collapse
Affiliation(s)
- Guanyang Chen
- Department of General Surgery, Peking University Ninth School of Clinical Medicine, Beijing, China
| | - Dongbo Lian
- Department of General Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Lei Zhao
- Department of Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Zheng Wang
- Department of General Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Qiqige Wuyun
- Department of General Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Nengwei Zhang
- Department of General Surgery, Peking University Ninth School of Clinical Medicine, Beijing, China
| |
Collapse
|