• Genomics & Informatics
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• v.20 no.2
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• pp.23.1-23.9
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• 2022

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3053-3059
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• 2012

Human epidermal growth factor receptor 2 (HER2) is a member of the epidermal growth factor receptor family of receptor tyrosine kinases that plays important roles in all processes of cell development. Their overexpression is related to many cancers, including examples in the breast, ovaries and stomach. Anticancer therapies targeting the HER2 receptor have shown promise, and monoclonal antibodies against subdomains II and IV of the HER2 extra-cellular domain (ECD), Pertuzumab and Herceptin, are currently used in treatments for some types of breast cancers. Since anti HER2 antibodies targeting distinct epitopes have different biological effects on cancer cells; in this research linear and conformational B cell epitopes of HER2 ECD, subdomain III, were identified by bioinformatics analyses using a combination of linear B cell epitope prediction web servers such as ABCpred, BCPREDs, Bepired, Bcepred and Elliprro. Then, Discotope, CBtope and SUPERFICIAL software tools were employed for conformational B cell epitope prediction. In contrast to previously reported epitopes of HER2 ECD we predicted conformational B cell epitopes $P1_C$: 378-393 (PESFDGDPASNTAPLQ) and $P2_C$: 500-510 (PEDECVGEGLA) by the integrated strategy and P4: PESFDGD-X-TAPLQ; P5: PESFDGDP X TAPLQ; P6: ESFDGDP X NTAPLQP; P7: PESFDGDP-X-NTAPLQ; P8: ESFDG-XX-TAPLQPEQL and P9: ESFDGDP-X-NTAPLQP by SUPERFICIAL software. These epitopes could be further used as peptide antigens to actively immune mice for development of new monoclonal antibodies and peptide cancer vaccines that target different epitopes or structural domains of HER2 ECD.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1481-1489
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• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• Biomolecules & Therapeutics
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• v.28 no.6
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• pp.491-502
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• 2020

Sex/gender disparity has been shown in the incidence and prognosis of many types of diseases, probably due to differences in genes, physiological conditions such as hormones, and lifestyle between the sexes. The mortality and survival rates of many cancers, especially liver cancer, differ between men and women. Due to the pronounced sex/gender disparity, considering sex/gender may be necessary for the diagnosis and treatment of liver cancer. By analyzing research articles through a PubMed literature search, the present review identified 12 genes which showed practical relevance to cancer and sex disparities. Among the 12 sex-specific genes, 7 genes (BAP1, CTNNB1, FOXA1, GSTO1, GSTP1, IL6, and SRPK1) showed sex-biased function in liver cancer. Here we summarized previous findings of cancer molecular signature including our own analysis, and showed that sex-biased molecular signature CTNNB1^High, IL6^High, RHOA^High and GLIPR1^Low may serve as a female-specific index for prediction and evaluation of OS in liver cancer patients. This review suggests a potential implication of sex-biased molecular signature in liver cancer, providing a useful information on diagnosis and prediction of disease progression based on gender.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1067-1074
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• 2014

In Morocco, breast cancer is the most prevalent cancer in women and a major public health problem. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. It is therefore interesting to establish the genetic and molecular profile of Moroccan patients with breast cancer. In this paper, we will highlight some pertinent hypotheses that may enhance breast cancer care in Moroccan patients. This review will give a precise description of breast cancer in Morocco and propose some new markers for detection and prediction of breast cancer prognosis.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.5891-5893
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• 2013

Incidence/mortality of liver cancer follow logistic curves because there is a limit reflecting the prevalence of hepatitis virus carriers in the cohort. The author fitted logistic curves to incidence/mortality data covering the nine five-year cohorts born in 1911-1955 of both sexes. Goodness-of-fit of logistic curves was sufficiently precise to be used for future predictions. Younger cohorts born in 1936 or later were predicted to show constant decline in incidence/mortality in the future. The male cohort born in 1931-35 showed an elevated incidence/mortality of liver cancer early in their lives supporting the previous claim that this particular cohort had suffered massive HCV infection due to nation-wide drug abuse in the 1950s. Declining case-fatality observed in younger cohorts suggested improved treatment of liver cancer. This study demonstrated that incidence/mortality of liver cancer follow logistic curves and fitted logistic formulae can be used for future prediction. Given the predicted decline of incidence/mortality in younger cohorts, liver cancer is likely to be lost to history in the not-so-distant future.

• The Korean Journal of Health Service Management
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• v.7 no.1
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• pp.1-9
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• 2013

This study intends to identify the characteristics of cancer in-patients and those of cancer patients who defected to other medical institutions based on the summary of hospital discharge information of a university hospital for the purpose of improving work efficiency and maximizing the number of patients. The study used data on cancer patients registered in the database of C University Hospital in Gyeonggi Province for a period of one year between January 1 and December 31. The analysis results suggest that the commonalities of the cancer patients who defected to other medical institutions include no specific job, old age, and hospitalization through emergency room. In conclusion, hospitals need to identify the characteristics of cancer patients classified as patients who are prone to defect and the defection factors through this prediction model.

• Genomics & Informatics
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• v.11 no.4
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• pp.174-179
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• 2013

The large amount of data on cancer genome research has contributed to our understanding of cancer biology. Indeed, the genomics approach has a strong advantage for analyzing multi-factorial and complicated problems, such as cancer. It is time to think about the actual usage of cancer genomics in the clinical field. The clinical cancer field has lots of unmet needs in the management of cancer patients, which has been defined in the pre-genomic era. Unmet clinical needs are not well known to bioinformaticians and even non-clinician cancer scientists. A personalized approach in the clinical field will bring potential additional challenges to cancer genomics, because most data to now have been population-based rather than individualbased. We can maximize the use of cancer genomics in the clinical field if cancer scientists, bioinformaticians, and clinicians think and work together in solving unmet clinical needs. In this review, we present one imaginary case of a cancer patient, with which we can think about unmet clinical needs to solve with cancer genomics in the diagnosis, prediction of prognosis, monitoring the status of cancer, and personalized treatment decision.

• Macromolecular Research
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• v.13 no.3
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• pp.167-175
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• 2005

This review explores recent works involving the use of the self-assembled nanoparticles of bile acid-modified glycol chitosans (BGCs) as a new drug carrier for cancer therapy. BGC nanoparticles were produced by chemically grafting different bile acids through the use of l-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC). The precise control of the size, structure, and hydrophobicity of the various BGC nanoparticles could be achieved by grafting different amounts of bile acids. The BGC nanoparticles so produced formed nanoparticles ranging in size from 210 to 850 nm in phosphate-buffered saline (PBS, pH=7.4), which exhibited substantially lower critical aggregation concentrations (0.038-0.260 mg/mL) than those of other low-molecular-weight surfactants, indicating that they possess high thermodynamic stability. The SOC nanoparticles could encapsulate small molecular peptides and hydrophobic anticancer drugs with a high loading efficiency and release them in a sustained manner. This review also highlights the biodistribution of the BGC nanoparticles, in order to demonstrate their accumulation in the tumor tissue, by utilizing the enhanced permeability and retention (EPR) effect. The different approaches used to optimize the delivery of drugs to treat cancer are also described in the last section.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.