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Zhang H, Cao K, Li G, Zhai Z, Wei G, Qu H, Wang Z, Han J. Active surveillance in long period of total neoadjuvant therapy in rectal cancer: Early prediction of poor regression response. Front Oncol 2022; 12:1049228. [PMID: 36439518 PMCID: PMC9685996 DOI: 10.3389/fonc.2022.1049228] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/25/2022] [Indexed: 11/12/2022] Open
Abstract
Aim To analyze locally advanced rectal cancer (LARC) patients and tumor characteristics during the period of total neoadjuvant therapy (TNT) and explore the risk factors that may predict poor tumor regression in response to TNT. Materials and methods The data of 120 LARC patients who received TNT from December 2016 and September 2019 in our hospital were retrospectively analyzed. The clinicopathological characteristics of patients with different tumor regression responses were compared. Then we divided patients into two groups according to the carcinoembryonic antigen (CEA) clearance pattern after chemoradiation to explore risk factors that might predict the tumor regression response. Results Of 120 LARC patients, 34 (28.3%) exhibited poor regression. Stratified analysis by tumor response showed that patients with poor response to TNT were more likely to obtain elevated CEA during the course of TNT (all P < 0.05). For those with elevated pretreatment CEA, fewer patients with poor response obtained normal CEA after chemoradiation (13.6% vs. 72.7%, P < 0.001). Besides, less patients’ CEA levels in the poor response group decreased by greater than 50% after chemoradiation when compared with that in the good response group (18.2% vs. 60.6%, P = 0.002). Stratified analysis by CEA clearance pattern after chemoradiation showed patients who obtained an elevated pretreatment CEA and decreased by less than 50% after chemoradiation were more likely to have poor response to TNT compared to others (76.2% vs. 18.2%, P < 0.001). Logistic multivariate analysis revealed that cN2 (95% CI 1.553-16.448), larger tumors (95% CI 2.250-21.428) and CEA clearance pattern after chemoradiation (95% CI 1.062-66.992) were independent risk factors for poor tumor regression response. Conclusion Approximately one-fourth of LARC patients with TNT achieved a poor regression response. Here, cN2, larger tumor size before treatment and elevated CEA levels were considered predictive features of a poor response. Active surveillance of CEA levels during the TNT course are potentially important, and CEA levels after chemoradiation might have important implications for the tumor response to TNT.
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Affiliation(s)
| | | | | | | | | | | | | | - Jiagang Han
- *Correspondence: Jiagang Han, ; Zhenjun Wang,
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Chuanji Z, Zheng W, Shaolv L, Linghou M, Yixin L, Xinhui L, Ling L, Yunjing T, Shilai Z, Shaozhou M, Boyang Z. Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery. Transl Oncol 2022; 18:101352. [PMID: 35144092 PMCID: PMC8844801 DOI: 10.1016/j.tranon.2022.101352] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/26/2021] [Accepted: 01/19/2022] [Indexed: 02/07/2023] Open
Abstract
Radiomics analysis of pretreatment MR images could predict overall survival (OS) in patients. Clinical, pathological and MRI imaging indexes were included and models were established. Tumor morphological model, clinicopathological model, radiomics model and comprehensive model were used to evaluate the prognosis of patients with rectal cancer. It can explore the influence of the above factors on the prognosis of rectal cancer from multi-level and multi-angle. The proposed radiomics nomogram showed better prognostic performance than the clinicopathological and imaging model in risk stratification and can classify patients into high- and low-risk groups with significant differences in OS.
We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832–0.912) in the training cohort and 0.944 (95% CI: 0.890–0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.
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Song J, Chen Z, Huang D, Xu B. Prognostic Impact of Pretreatment Elevated and Normalized Carcinoembryonic Antigen Levels After Neoadjuvant Chemoradiotherapy in Resected Locally Advanced Rectal Cancer Patients. Cancer Manag Res 2021; 13:3713-3721. [PMID: 33994811 PMCID: PMC8112854 DOI: 10.2147/cmar.s299364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/18/2021] [Indexed: 01/04/2023] Open
Abstract
Purpose The prognostic significance of pretreatment elevated and normalized CEA after neoadjuvant chemoradiotherapy (nCRT) was evaluated. Materials and Methods The characteristics of 951 locally advanced rectal cancer patients with nCRT were retrieved and were analyzed retrospectively. Pretreatment CEA levels were defined as CEA evaluated one week prior to the nCRT. CEA after nCRT was deemed as CEA measured one week before surgery. The normal CEA levels were set at <5 ng/mL. The normal CEA group was defined as patients with normal pretreatment CEA levels. The normalized CEA group was defined as patients with elevated pretreatment CEA levels and normal CEA levels after nCRT. The elevated CEA group was defined as patients with elevated pretreatment CEA levels and elevated CEA levels after nCRT. Results Compared with the elevated CEA group, the normalized CEA group was associated with better overall survival (OS) (HR: 0.625, 95%CI: 0.416-0.938, P=0.022). There was no difference between the normalized CEA group and the normal CEA group (HR: 1.143, 95%CI: 0.84-1.557, P=0.395). Conclusion In conclusion, the study indicated that OS of the normalized CEA group and the normal CEA group was better than the elevated CEA group.
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Affiliation(s)
- Jianyuan Song
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China.,Department of Medical Imaging Technology, College of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China.,Fujian Medical University Union Clinical Medicine College, Fuzhou, Fujian Province, People's Republic of China
| | - Zhuhong Chen
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
| | - Daxin Huang
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
| | - Benhua Xu
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China.,Department of Medical Imaging Technology, College of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China.,Fujian Medical University Union Clinical Medicine College, Fuzhou, Fujian Province, People's Republic of China
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Huang W, Li G, Wang Z, Zhou L, Yin X, Yang T, Wang P, Teng X, Feng Y, Yu H. A Ten-N 6-Methyladenosine (m 6A)-Modified Gene Signature Based on a Risk Score System Predicts Patient Prognosis in Rectum Adenocarcinoma. Front Oncol 2021; 10:567931. [PMID: 33680913 PMCID: PMC7925823 DOI: 10.3389/fonc.2020.567931] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 12/16/2020] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVES The study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ. MATERIALS AND METHODS RNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N6-methyladenosine (m6A)-modified genes in READ were downloaded from the "m6Avar" database. Differentially expressed m6A-modified genes in READ stratified by different clinicopathological characteristics were identified using the "limma" package in R. Protein-protein interaction (PPI) network and co-expression analysis of differentially expressed genes (DEGs) were performed using "STRING" and Cytoscape, respectively. Principal component analysis (PCA) was done using R. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to functionally annotate the differentially expressed genes in different subgroups. Univariate Cox regression analyses were conducted to identify the powerful independent prognostic factors in READ associated with overall survival (OS). A robust likelihood-based survival model was built using the "rbsurv" package to screen for survival-associated signature genes. The Support Vector Machine (SVM) was used to predict the prognosis of READ through the risk score of survival-associated signature genes. Correlation analysis were carried out using GraphPad prism 8. RESULTS We screened 974 differentially expressed m6A-modified genes among four types of READ samples. Two READ subgroups (group 1 and group 2) were identified by K means clustering according to the expression of DEGs. The two subgroups were significantly different in overall survival and pathological stages. Next, 118 differentially expressed genes between the two subgroups were screened and the expression of 112 genes was found to be related to the prognosis of READ. Next, a panel of 10 survival-associated signature genes including adamtsl1, csmd2, fam13c, fam184a, klhl4, olfml2b, pdzd4, sec14l5, setbp1, tmem132b was constructed. The signature performed very well for prognosis prediction, time-dependent receiver-operating characteristic (ROC) analysis displaying an area under the curve (AUC) of 0.863, 0.8721, and 0.8752 for 3-year survival rate, prognostic status, and pathological stage prediction, respectively. Correlation analysis showed that the expression levels of the 10 m6A-modified genes were positively correlated with that of m6A demethylase FTO and ALKBH5. CONCLUSION This study identified potential m6A-modified genes that may be involved in the pathophysiology of READ and constructed a novel gene expression panel for READ risk stratification and prognosis prediction.
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Affiliation(s)
- Wei Huang
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Gen Li
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Zihang Wang
- School of Information Science and Technology, University of Science and Technology of China, Hefei, China
| | - Lin Zhou
- School of Information Science and Technology, University of Science and Technology of China, Hefei, China
| | - Xin Yin
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Tianshu Yang
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Pei Wang
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Xu Teng
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Yajuan Feng
- School of Information Science and Technology, University of Science and Technology of China, Hefei, China
| | - Hefen Yu
- Beijing Key Laboratory of Cancer Invasion and Metastasis Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
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Li Y, Liu D, Zhao L, Güngör C, Song X, Wang D, Liu W, Tan F. Accurate nomograms with excellent clinical value for locally advanced rectal cancer. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:296. [PMID: 33708923 PMCID: PMC7944304 DOI: 10.21037/atm-20-4144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Rectal cancer accounts for approximately 30–50% of colorectal cancer. Despite its widespread use and convenience, the American Joint Committee on Cancer (AJCC) staging system for predicting survival is prone to inaccuracy, even including a survival paradox for locally advanced rectal cancer (LARC). An accurate risk stratification of LARC is essential for proper treatment selection and prognostic evaluation. Therefore, we aimed to create prognostic nomograms for LARC capable of assessing overall survival (OS) and cancer-specific survival (CSS) precisely and intuitively. Methods The Surveillance, Epidemiology, and End Results (SEER) database was accessed. All of the significant variables in the multivariate analysis were integrated to build the nomograms. Results Data for a total of 23,055 patients with LARC were collected from the SEER database in this study. Based on the multivariate Cox regression analysis, both OS and CSS were significantly associated with 13 variables: age, marital status, race, pathological grade, histological type, T stage, N stage, surgery, radiotherapy, chemotherapy, regional nodes examined (RNE), tumor size, and carcinoembryonic antigen (CEA). These were included in the construction of nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA), concordance index, and calibration curves demonstrated the discriminative superiority of the nomograms. Conclusions The nomograms, which effectively solve the issue of the survival paradox in the AJCC staging system regarding LARC, may act as excellent tools for integrating clinical characteristics and to guiding therapeutic choices for LARC patients.
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Affiliation(s)
- Yuqiang Li
- Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, China.,Department of General Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Da Liu
- Shanxi Medical University, Taiyuan, China
| | - Lilan Zhao
- Department of Thoracic surgery, Fujian Provincial Hospital, Fuzhou, China
| | - Cenap Güngör
- Department of General Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Xiangping Song
- Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Dan Wang
- Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, China.,Department of General Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Wenxue Liu
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, China.,Department of Rheumatology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Fengbo Tan
- Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha, China
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Song J, Chen Z, Huang D, Wu Y, Lin Z, Chi P, Xu B. Nomogram Predicting Overall Survival of Resected Locally Advanced Rectal Cancer Patients with Neoadjuvant Chemoradiotherapy. Cancer Manag Res 2020; 12:7375-7382. [PMID: 32884350 PMCID: PMC7443447 DOI: 10.2147/cmar.s255981] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/10/2020] [Indexed: 12/23/2022] Open
Abstract
PURPOSE The overall survival (OS) of resected locally advanced rectal cancer patients who underwent neoadjuvant chemoradiotherapy (nCRT) was significantly different, even among patients with the same tumor stage. The nomogram was designed to predict OS of rectal cancer with nCRT and divide the patients into different risk groups. MATERIALS AND METHODS Based on materials from 911 rectal cancer patients with nCRT, the multivariable Cox regression model was carried out to select the significant prognostic factors for overall survival. And then, the nomogram was formulated using these independent prognostic factors. The discrimination of the nomogram was assessed by concordance index (C-index), calibration curves and time-dependent area under curve (AUC). The patients respective risk scores were calculated through the nomogram. The best cut-off risk score was calculated to stratify the patients. The survival curves of the two different risk cohorts were performed, which assessed the predictive ability of the nomogram. RESULTS Age, cT stage, pretreatment CEA, pretreatment CA19-9, surgery, posttreatment CEA, posttreatment CA19-9, pT stage, pN stage and adjuvant chemotherapy were selected for the construction of the nomogram. And then the nomogram was constructed with independent prognostic factors. The C-index of the nomogram was 0.724, which showed the nomogram provided good discernment. The acceptable agreement between the predictions of nomogram and actual observations was illustrated by calibration plots for 3-, 5- and 10-year OS in the cohort. Time-dependent AUC with 6-fold cross-validation also showed consistent results of the nomogram. Risk group stratification confirmed that the nomogram had great capacity for distinguishing the prognosis. CONCLUSION The nomogram was developed and validated to predict overall survival of resected locally advanced rectal cancer patients with nCRT. The proposed nomogram might help clinicians to develop individualized treatment strategies. However, further studies are warranted to optimize the nomogram by finding out other unknown prognostic factors, and more external validation is still required.
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Affiliation(s)
- Jianyuan Song
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
- Department of Oncology, Fujian Medical University Union Clinical Medicine College, Fuzhou, Fujian Province, People's Republic of China
- Department of Medical Imaging Technology, College of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Zhuhong Chen
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
| | - Daxin Huang
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
| | - Yimin Wu
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
| | - Zhuangbin Lin
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
- Department of Oncology, Fujian Medical University Union Clinical Medicine College, Fuzhou, Fujian Province, People's Republic of China
| | - Pan Chi
- Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
| | - Benhua Xu
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, People's Republic of China
- Department of Oncology, Fujian Medical University Union Clinical Medicine College, Fuzhou, Fujian Province, People's Republic of China
- Department of Medical Imaging Technology, College of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
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