• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1803-1806
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• 2013

Purpose: Carbohydrate antigen (CA) 242 is inversely related to prognosis in many cancers. However, few data regarding CA 242 in esophageal cancer (EC) are available. The aim of this study was to determine the prognostic value of CA 242 and propose an optimum cut-off point in predicting survival difference in patients with esophageal squamous cell carcinoma (ESCC). Methods: A retrospective analysis was conducted of 192 cases. A receiver operating characteristic (ROC) curve for survival prediction was plotted to verify the optimum cuf-off point. Univariate and multivariate analyses were performed to evaluate prognostic parameters for survival. Results: The positive rate for CA 242 was 7.3% (14/192). The ROC curve for survival prediction gave an optimum cut-off of 2.15 (U/ml). Patients with CA 242 ${\leq}$ 2.15 U/ml had significantly better 5-year survival than patients with CA 242 >2.15 U/ml (45.4% versus 22.6%; P=0.003). Multivariate analysis showed that differentiation (P=0.033), CA 242 (P=0.017), T grade (P=0.004) and N staging (P<0.001) were independent prognostic factors. Conclusions: Preoperative CA 242 is a predictive factor for long-term survival in ESCC, especially in nodal-negative patients. We conclude that 2.15 U/ml may be the optimum cuf-off point for CA 242 in predicting survival in ESCC.

• Journal of Gastric Cancer
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• v.21 no.1
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• pp.1-3
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• 2021

• Journal of KIISE
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• v.45 no.1
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• pp.61-68
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• 2018

The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1281-1284
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• 2012

Background: Platinum-hased chemotherapy for advanced non-small cell lung cancer (NSCLC) is still considered the first choice, presenting a modest survival advantage. However, the patients eventually experience disease progression and require second-line therapy. While there are reliable predictors to identify patients receiving first-line chemotherapy, very little knowledge is available about the prognostic factors in patients who receive second-line treatments. The present study was therefore performed. Methods: We retrospectively reviewed 107 patients receiving second-line treatments from August 2002 to March 2012 in the Dicle University, School of Medicine, Department of Medical Oncology. Fourteen potential prognostic variables were chosen for analysis in this study. Univariate and multivariate analyses were conducted to identify prognostic factors associated with survival. Result: The results of univariate analysis for overall survival (OS) were identified to have prognostic significance: performance status (PS), stage, response to first-line chemotherapy response to second-line chemotherapy and number of metastasis. PS, diabetes mellitus (DM), response to first-line chemotherapy and response to second-line chemotherapy were identified to have prognostic significance for progression-free survival (PFS). Multivariate analysis showed that PS, response to first-line chemotherapy and response to second-line chemotherapy were considered independent prognostic factors for OS. Furthermore, PS and response to second-line chemotherapy were considered independent prognostic factors for PFS. Conclusion: In conclusion, PS, response to first and second-line chemotherapy were identified as important prognostic factors for OS in advanced NSCLC patients who were undergoing second-line palliative treatment. Furthermore, PS and response to second-line chemotherapy were considered independent prognostic factors for PFS. It may be concluded that these findings may facilitate pretreatment prediction of survival and can be used for selecting patients for the correct choice of treatment.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3869-3872
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• 2012

Background: The majority of patients with gastric cancer in developing countries present with advanced disease. Systemic chemotherapy therefore has limited impact on overall survival. Patients eligible for chemotherapy should be selected carefully. The aim of this study was to analyze prognostic factors for survival in advanced gastric cancer patients undergoing first-line palliative chemotherapy. Methods: We retrospectively reviewed 107 locally advanced or metastatic gastric cancer patients who were treated with docetaxel and cisplatin plus fluorouracil (DCF) as first-line treatment between June 2007 and August 2011. Twenty-eight potential prognostic variables were chosen for univariate and multivariate analyses. Results: Among the 28 variables of univariate analysis, nine variables were identified to have prognostic significance: performance status, histology, location of primary tumor, lung metastasis, peritoneum metastasis, ascites, hemoglobin, albumin, weight loss and bone metastasis. Multivariate analysis by Cox proportional hazard model, including nine prognostic significance factors evident in univariate analysis, revealed weight loss, histology, peritoneum metastasis, ascites and serum hemoglobin level to be independent variables. Conclusion: Performance status, weight loss, histology, peritoneum metastasis, ascites and serum hemoglobin level were identified as important prognostic factors in advanced gastric cancer patients. These findings may facilitate pretreatment prediction of survival and can be used for selecting patients for treatment.

• Journal of Breast Disease
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• v.6 no.2
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• pp.35-38
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• 2018

Purpose: The eighth American Joint Committee on Cancer staging system for breast cancer was recently published to more accurately predict the prognosis by adding biomarkers such as estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2. However, this system is very complicated and difficult to use by clinicians. The authors developed a program to aid in setting up the staging system and confirmed its usefulness by applying it to theoretical combinations and actual clinical data. Methods: The program was developed using the Microsoft Excel Macro. It was used for the anatomic, clinical and pathological prognostic staging of 588 theoretical combinations. The stages were also calculated the stages using 840 patients with breast cancer without carcinoma in situ or distant metastasis who did not undergo preoperative chemotherapy. Results: The anatomic, clinical and pathological prognostic stages were identical in 240 out of 588 theoretical combinations. In the actual patients' data, stages IB and IIIB were more frequent in clinical and pathological prognostic stages than in the anatomic stage. The anatomic stage was similar to the clinical prognostic stage in 58.2% and to the pathological prognostic stage in 61.9% of patients. Oncotype DX changed the pathological prognostic stage in 2.1% of patients. Conclusion: We developed a program for the new American Joint Committee on Cancer staging system that will be useful for clinical prognostic prediction and large survival data analysis.

• Journal of Hospice and Palliative Care
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• v.26 no.1
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• pp.1-6
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• 2023

This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make end-of-life decisions. The clinician's prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician's prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2601-2611
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• 2015

Prostate cancer, with a lifetime prevalence of one in six men, is the second cause of malignancy-related death and the most prevalent cancer in men in many countries. Nowadays, prostate cancer diagnosis is often based on the use of biomarkers, especially prostate-specific antigen (PSA) which can result in enhanced detection at earlier stage and decreasing in the number of metastatic patients. However, because of the low specificity of PSA, unnecessary biopsies and mistaken diagnoses frequently occur. Prostate cancer has various features so prognosis following diagnosis is greatly variable. There is a requirement for new prognostic biomarkers, particularly to differentiate between inactive and aggressive forms of disease, to improve clinical management of prostate cancer. Research continues into finding additional markers that may allow this goal to be attained. We here selected a group of candidate biomarkers including PSA, PSA velocity, percentage free PSA, $TGF{\beta}1$, AMACR, chromogranin A, IL-6, IGFBPs, PSCA, biomarkers related to cell cycle regulation, apoptosis, PTEN, androgen receptor, cellular adhesion and angiogenesis, and also prognostic biomarkers with Genomic tests for discussion. This provides an outline of biomarkers that are presently of prognostic interest in prostate cancer investigation.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.1841-1844
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• 2012

Background: The majority of patients with pancreatic cancer present with advanced disease. Systemic chemotherapy has limited impact on overall survival (OS) so that eligible patients should be selected carefully. The aim of this study was to analyze prognostic factors for survival in Turkish advanced pancreatic cancer patients who survived more than one year from the diagnosis of recurrent and/or metastatic disease and receiving gemcitabine (Gem) alone or gemcitabine plus cisplatin (GemCis). Methods: This retrospective evaluation was performed for patients who survived more than one year from the diagnosis of recurrent and/or metastatic disease and who received gemcitabine between December 2005 and August 2011. Twenty-seven potential prognostic variables were chosen for univariate and multivariate analyses to identify prognostic factors associated with survival. Results: Among the 27 variables in univariate analysis, three were identified to have prognostic significance: sex (p = 0.04), peritoneal dissemination (p =0.02) and serum creatinine level (p=0.05). Multivariate analysis by Cox proportional hazard model showed only peritoneal dissemination to be an independent prognostic factor for survival. Conclusion: In conclusion, peritoneal metastasis was identified as an important prognostic factor in metastatic pancreatic cancer patients who survived more than one year from the diagnosis of recurrent and/or metastatic disease and receiving Gem or GemCis. The findings should facilitate pretreatment prediction of survival and can be used for selecting patients for treatment.