Copy Number Alterations of Depressed Colorectal Neoplasm Predict the Survival and Response to Oxaliplatin in Proximal Colon Cancer

Chang, Li-Chun; Chiu, Han‐Mo; Ho, Bing-Ching; Chen, Min-Hsuan; Hsu, Yin-Chen; Chiu, Wei-Tzu; Su, Kang‐Yi; Shun, Chia‐Tung; Liang, Jian; Yu, Sung-Liang

doi:10.3390/cancers12061527

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…The experience of endoscopists and surgeons is among these factors and could be a possible confounder for recurrence. However, all T1 cancers were treated using the same protocol at our center, thus mitigating such bias [34]. In view of the retrospective design of this study, measured factors such as tumor size, anatomic location, and macroscopic shape may not have been standardized.…”

Section: Discussionmentioning

confidence: 99%

Recurrence Outcomes Less Favorable in T1 Rectal Cancer than in T1 Colon Cancer

et al. 2021

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Background. With the implementation of screening programs worldwide, diagnosis of early-stage colorectal cancer steadily increased, including T1 cancer. Current T1 cancer treatment does not differ according to anatomic location. We therefore compared the disease-free survival of T1 cancer arising from the rectum versus the colon. Methods. The hospital-based study included subjects with T1 cancer at National Taiwan University Hospital from 2005 to 2014. Clinical, colonoscopy, and histopathology were reviewed for patients with a mean follow-up time of 7.1 (0.7-12.9) years. We conducted Kaplan-Meier analysis to compare the risk of recurrence by cancer location and Cox regression analysis to identify risk factors for T1 cancer recurrence. Results. The final cohort included a total of 343 subjects with T1 cancer (mean age, 64.9 AE 11.7 years; 56.1% male), of whom 25 underwent endoscopic resection alone. Of the subjects who underwent surgery, 50 had lymph node metastasis and 268 did not. Kaplan-Meier analysis showed that the risk of recurrence was higher in T1 rectal cancer than T1 colon cancer (p = .022). Rectal location and larger neoplasm size were independent risk factors for recurrence, with hazard ratios of 4.84 (95% confidence interval, 1.18-19.92), and 1.32 (95% confidence interval, 1.06-1.65), respectively. The occurrence of advanced histology did not differ between T1 rectal and colon cancers (p = .58). Conclusion. T1 cancers arising from the rectum had less favorable recurrence outcomes than those arising from the colon. Further studies are needed to examine whether adjuvant radiotherapy or chemotherapy can reduce the risk of recurrence in T1 rectal cancer. The Oncologist 2021;25:1-7 Implications for Practice: Current T1 colorectal cancer treatment and surveillance do not differ according to anatomic location. Clinical, colonoscopy, and histopathology were reviewed for 343 patients with T1 cancer with a mean follow-up time of 7.1 years. Kaplan-Meier analysis showed that the risk of recurrence was higher in T1 rectal cancer than T1 colon cancer. Moreover, the rectal location was an independent risk factor for recurrence. T1 cancers from the rectum had less favorable recurrence outcomes than those arising from the colon. It is critical to clarify whether adjuvant therapy or more close surveillance can reduce recurrence risk in T1 rectal cancer.

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Section: Discussionmentioning

confidence: 99%

Recurrence Outcomes Less Favorable in T1 Rectal Cancer than in T1 Colon Cancer

et al. 2021

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“…Sun et al used the machine learning method of supervised learning to study and analyze the survival data of colon cancer patients and proposed the expression characteristics of colon cancer genes [ 1 ]. Chang et al conducted research on depressive and polypoid colon cancer tumors and found that depressive colon cancer tumors showed rapid invasive characteristics during patient survival [ 2 ]. Li et al proposed a postoperative survival assessment method for colon cancer patients by analyzing the blood test results of colorectal cancer, combined with data classification and machine learning methods [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

Tian

Chen

2023

Contrast Media & Molecular Imaging

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Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent on the personal ability of the doctors. In the process of patient treatment, it not only increases the workload and work pressure for doctors, but also has some problems with traditional medical image analysis methods. Moreover, the traditional medical image analysis methods have problems such as insufficient prediction accuracy, slow prediction speed, and the risk of errors in prediction. When analyzing 18F-FDG PET/CT images by traditional medical image analysis methods, it is easy to cause problems such as untimely treatment plans and errors in diagnosis, which will adversely affect the survival of colon cancer patients. Although 18F-FDG PET/CT images have certain advantages in image clarity and accuracy compared with traditional medical imaging methods, the analysis method based on 18F-FDG PET/CT images also has certain effects in predicting the survival of colon cancer patients, but there are still many shortcomings: the 18F-FDG PET/CT image analysis method overly relies on the technical advantages of 8F-FDG PET/CT images; in the analysis and prediction of image data, it has not gotten rid of the dependence on the personal medical quality of the doctors; traditional medical image analysis methods are still used when analyzing and predicting images; there is no breakthrough in image analysis effects. In order to solve these problems, this paper combined deep learning theory, using three algorithms of the improved RBM algorithm, image feature extraction method based on deep learning, and regression neural network to analyze and predict 18F-FDG PET/CT images, and applied some algorithms to analyze and predict 18F-FDG PET/CT images, and also established a deep learning-based 18F-FDG PET/CT image survival analysis prediction model. Four aspects survival prediction accuracy, survival prediction speed, survival prediction precision, and physician satisfaction were studied through this model. The research results have shown that compared with traditional medical image analysis methods, the prediction accuracy of 18F-FDG PET/CT image survival analysis prediction model based on deep learning is improved by 0.83%, and the prediction speed is improved by 3.42%, as well as the prediction precision increased by 6.13%. The research results show that the deep learning-based 18F-FDG PET/CT image survival analysis prediction model established in this paper is of great significance to improve the survival rate of colon cancer patients, and also promotes the development of the medical industry.

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“…A higher TMB suggests a likelihood of a better response to immunotherapy [52]. Furthermore, it has previously been demonstrated that tumors with microsatellite instabilityare associated with a higher TMB, which would be more susceptible to the treatment of immunotherapy [35,53,54]. Therefore, further investigations on the relationship between TMB, PD1/PD-L1 expression, and P-CA score were performed.…”

Section: Discussionmentioning

confidence: 99%

Characterization of pyruvate metabolism and citric acid cycle patterns predicts response to immunotherapeutic and ferroptosis in gastric cancer

Wang

et al. 2022

Cancer Cell Int

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Background Gastric cancer is one of the most common malignancies of the digestive system with a high lethal rate. Studies have shown that inherited and acquired mutations in pyruvate metabolism and citric acid cycle (P-CA) enzymes are involved in tumorigenesis and tumor development. However, it is unclear how different P-CA patterns affect the tumor microenvironment (TME), which is critical for cancer progression. Methods This study mainly concentrated on investigating the role of the P-CA patterns in multicellular immune cell infiltration of GC TME. First, the expression levels of P-CA regulators were profiled in GC samples from The Cancer Genome Atlas and Gene Expression Omnibus cohorts to construct a consensus clustering analysis and identify three distinct P-CA clusters. GSVA was conducted to reveal the different biological processes in three P-CA clusters. Subsequently, 1127 cluster-related differentially expressed genes were identified, and prognostic-related genes were screened using univariate Cox regression analysis. A scoring system was then set up to quantify the P-CA gene signature and further evaluate the response of the patients to the immunotherapy. Results We found that GC patients in the high P-CA score group had a higher tumor mutational burden, higher microsatellite instability, and better prognosis. The opposite was observed in the low P-CA score group. Interestingly, we demonstrated P-CA gene cluster could predict the sensitivity to immunotherapy and ferroptosis-induced therapy. Conclusion Collectively, the P-CA gene signature in this study exhibits potential roles in the tumor microenvironment and predicts the response to immunotherapeutic. The identification of these P-CA patterns may significantly accelerate the strategic development of immunotherapy for GC.

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Copy Number Alterations of Depressed Colorectal Neoplasm Predict the Survival and Response to Oxaliplatin in Proximal Colon Cancer

Cited by 6 publications

References 36 publications

Recurrence Outcomes Less Favorable in T1 Rectal Cancer than in T1 Colon Cancer

Recurrence Outcomes Less Favorable in T1 Rectal Cancer than in T1 Colon Cancer

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

Characterization of pyruvate metabolism and citric acid cycle patterns predicts response to immunotherapeutic and ferroptosis in gastric cancer

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