• Title/Summary/Keyword: Cancer Prognostic Prediction

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Molecular Classification of Hepatocellular Carcinoma and Its Impact on Prognostic Prediction and Personized Therapy

  • Dhruba Kadel;Lun-Xiu Qin
    • Journal of Digestive Cancer Research
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    • v.5 no.1
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    • pp.5-15
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    • 2017
  • Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related death in the world. The aggressive but not always predictable pattern of HCC causes the limited treatment option and poorer outcome. Many researches had already proven the heterogeneity of HCC is one of the major challenges for treatment option and prognosis prediction. Molecular subtyping of HCC and selection of patient based on molecular profile can provide the optimization in the treatment and prognosis prediction. In this review, we have tried to summarize the molecular classification of HCC proposed by different valuable researches presented in the logistic way.

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Clinical Prognostic Score for Predicting Disease Remission with Differentiated Thyroid Cancers

  • Somboonporn, Charoonsak;Mangklabruks, Ampica;Thakkinstian, Ammarin;Vatanasapt, Patravoot;Nakaphun, Suwannee
    • Asian Pacific Journal of Cancer Prevention
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    • v.17 no.6
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    • pp.2805-2810
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    • 2016
  • Background: Differentiated thyroid cancer is the most common endocrine malignancy with a generally good prognosis. Knowing long-term outcomes of each patient helps management planning. The study was conducted to develop and validate a clinical prognostic score for predicting disease remission in patients with differentiated thyroid cancer based on patient, tumor and treatment factors. Materials and Methods: A retrospective cohort study of 1,217 differentiated thyroid cancer patients from two tertiary-care hospitals in the Northeast of Thailand was performed. Associations between potential clinical prognostic factors and remission were tested by Cox proportional-hazards analysis in 852 patients (development cohort). The prediction score was created by summation of score points weighted from regression coefficients of independent prognostic factors. Risks of disease remission were estimated and the derived score was then validated in the remaining 365 patients (validation cohort). Results: During the median follow-up time of 58 months, 648 (76.1%) patients in the development cohort had disease remission. Five independent prognostic factors were identified with corresponding score points: duration from thyroid surgery to $^{131}I$ treatment (0.721), distant metastasis at initial diagnosis (0.801), postoperative serum thyroglobulin level (0.535), anti-thyroglobulin antibodies positivity (0.546), and adequacy of serum TSH suppression (0.293). The total risk score for each patient was calculated and three categories of remission probability were proposed: ${\leq}1.628$ points (low risk, 83% remission), 1.629-1.816 points (intermediate risk, 87% remission), and ${\geq}1.817$ points (high risk, 93% remission). The concordance (C-index) was 0.761 (95% CI 0.754-0.767). Conclusions: The clinical prognostic scoring model developed to quantify the probability of disease remission can serve as a useful tool in personalized decision making regarding treatment in differentiated thyroid cancer patients.

Prognostic Factors of Prostate Cancer in Tunisian Men: Immunohistochemical Study

  • Missaoui, Nabiha;Abdelkarim, Soumaya Ben;Mokni, Moncef;Hmissa, Sihem
    • Asian Pacific Journal of Cancer Prevention
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    • v.17 no.5
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    • pp.2655-2660
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    • 2016
  • Background: Prostate cancer is the second most common male cancer and remains a leading cause of cancer death worldwide. Heterogeneity regarding recurrence, tumor progression and therapeutic response reflects the inadequacy of traditional prognostic factors and underlies interest in new genetic and molecular markers. In this work, we studied the prognostic value of the expression of 9 proteins, Ki-67, p53, Bcl-2, PSA, HER2, E-cadherin, $p21^{WAF1/Cip1}$, $p27^{Kip1}$ and $p16^{ink4a}$ in prostate cancer. Materials and Methods: We conducted a retrospective study of 50 prostate cancers diagnosed in Pathology Department of Farhet Hached Hospital, Sousse, Tunisia, during a period of 12 months. Clinico-pathological data and survival were investigated. Protein expression was analyzed by immunohistochemistry on archived material. Results: Expression or over-expression of Ki-67, p53, Bcl-2, PSA, HER2, E-Cadherin, $p21^{WAF1/Cip1}$, $p27^{Kip1}$ and $p16^{ink4a}$ was observed in 68%, 24%, 32%, 78%, 12%, 90%, 20%, 44% and 56% of cases, respectively. Overall five-year survival was 68%. A statistically significant correlation was observed between death occurrence and advanced age (p=0.018), degree of tumor differentiation (p=0.0001), perineural invasion (p=0.016) and metastasis occurrence (p=0.05). Death occurrence was significantly correlated with the expression of p53 (p=0.007), Bcl-2 (p=0.02), Ki-67 (p=0.05) and $p27^{Kip1}$ (p=0.04). Conclusions: The p53, Bcl-2, Ki-67 and $p27^{Kip1}$ proteins may be useful additional prognostic markers for prostate cancer. The use of these proteins in clinical practice can improve prognosis prediction, disease screening and treatment response of prostatic cancer.

A Node2Vec-Based Gene Expression Image Representation Method for Effectively Predicting Cancer Prognosis (암 예후를 효과적으로 예측하기 위한 Node2Vec 기반의 유전자 발현량 이미지 표현기법)

  • Choi, Jonghwan;Park, Sanghyun
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.10
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    • pp.397-402
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    • 2019
  • Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

Can Recurrence and Progression be Predicted by HYAL-1 Expression in Primary T1 Bladder Cancer?

  • Mammadov, Elnur;Aslan, Guven;Tuna, Burcin;Bozkurt, Ozan;Yorukoglu, Kutsal
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.23
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    • pp.10401-10405
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    • 2015
  • Background: Molecular prognostic markers have been under investigation for the last decade and no validated marker to date has been proven to be used in daily clinical practice for urinary bladder cancers. The aim of the present study is to evaluate the significance of HYAL-1 expression in prediction of recurrence and progression in pT1 urothelial carcinomas. Materials and Methods: Eighty-nine urothelial carcinoma cases staged as T1 according to 2004 WHO classification were studied. Representative sections from every case were stained immunohistochemically for HYAL-1 and scored between 0 and +3, according to staining density, and graded as low and high for the scores 0-1 and 2-3, respectively. Results: Of the 89 pT1 bladder cancer patients, HYAL-1 expression was high in 92.1% (82 patients; 72 patients +3 and 10 patients +2) and low in 7.9% (only 7 patients; 6 patients +1 and 1 patient 0) of the cases. Of the 89 patients, 38 (42.7%) had recurrence and 22 (24.7%) showed progression. HYAL-1 staining did not show significant characteristics for tumor grade, accompanying CIS, multiplicity, tumor size, age and sex. HYAL-1 expression did not have any prognostic value in estimating recurrence or progression. Conclusions: HYAL-1 expression was found to be high, but did not have any prognostic importance in T1 bladder urothelial carcinomas.

Prognostic Significance of 18F-fluorodeoxyglucose Positron Emission Tomography (PET)-based Parameters in Neoadjuvant Chemoradiation Treatment of Esophageal Carcinoma

  • Ma, Jin-Bo;Chen, Er-Cheng;Song, Yi-Peng;Liu, Peng;Jiang, Wei;Li, Ming-Huan;Yu, Jin-Ming
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.4
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    • pp.2477-2481
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    • 2013
  • Aims and Background: The purpose of the research was to study the prognostic value of tumor 18F-FDG PET-based parameters in neoadjuvant chemoradiation for patients with squamous esophageal carcinoma. Methods: Sixty patients received chemoradiation therapy followed by esophagectomy and two 18FDG-PET examinations at pre- and post-radiation therapy. PET-based metabolic-response parameters were calculated based on histopathologic response. Linear regression correlation and Cox proportional hazards models were used to determine prognostic value of all PET-based parameters with reference to overall survival. Results: Sensitivity (88.2%) and specificity (86.5%) of a percentage decrease of SUVmax were better than other PET-based parameters for prediction of histopathologic response. Only percentage decrease of SUVmax and tumor length correlated with overall survival time (linear regression coefficient ${\beta}$: 0.704 and 0.684, P<0.05). The Cox proportional hazards model indicated higher hazard ratio (HR=0.897, P=0.002) with decrease of SUVmax compared with decrease of tumor size (HR=0.813, P=0.009). Conclusion: Decrease of SUVmax and tumor size are significant prognostic factors in chemoradiation of esophageal carcinoma.

Prognostic Value of Vascular Endothelial Growth Factor Expression in Resected Gastric Cancer

  • Liu, Lei;Ma, Xue-Lei;Xiao, Zhi-Lan;Li, Mei;Cheng, Si-Hang;Wei, Yu-Quan
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.7
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    • pp.3089-3097
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    • 2012
  • Background and Aims: Vascular endothelial growth factor (VEGF) is a potential prognostic biomarker for patients with resected gastric cancer. However, its role remains controversial. The objective of this study was to conduct a systematic review and meta-analysis of published literature. Methods: Relevant literature was identified using Medline and survival data from published studies were collected following a methodological assessment. Quality assessment of eligible studies and meta-analysis of hazard ratio (HR) were performed to review the correlation of VEGF overexpression with survival and recurrence in patients with gastric cancer. Results: Our meta-analysis included 44 published studies with 4,794 resected patients. VEGF subtype for the prediction of overall survival (OS) included tissue VEGF (HR=2.13, 95% CI 1.71-2.65), circulating VEGF (HR=4.22, 95% CI 2.47-7.18), tissue VEGF-C (HR=2.21, 95% CI 1.58-3.09), tissue VEGF-D (HR=1.73, 95% CI 1.25-2.40). Subgroup analysis showed that HRs of tissue VEGF for OS were, 1.78 (95% CI 0.90-3.51) and 2.31 (95% CI 1.82-2.93) in non-Asians and Asians, respectively. The meta-analysis was also conducted for disease free survival (DFS) and disease specific survival (DSS). Conclusion: Positive expression of tissue VEGF, circulating VEGF, VEGF-C and VEGF-D were all associated with poor prognosis in resected gastric cancer. However, VEGF demonstrated no significant prognostic value for non-Asian populations. Circulating VEGF may be better than tissue VEGF in predicting prognosis.

FOXA1: a Promising Prognostic Marker in Breast Cancer

  • Hu, Qing;Luo, Zhou;Xu, Tao;Zhang, Jun-Ying;Zhu, Ying;Chen, Wei-Xian;Zhong, Shan-Liang;Zhao, Jian-Hua;Tang, Jin-Hai
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.1
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    • pp.11-16
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    • 2014
  • Accurate diagnosis and proper monitoring of cancer patients remain important obstacles for successful cancer treatment. The search for cancer biomarkers can aid in more accurate prediction of clinical outcome and may also reveal novel predictive factors and therapeutic targets. One such prognostic marker seems to be FOXA1. Many studies have shown that FOXA1 is strongly expressed in a vast majority of cancers, including breast cancer, in which high expression is associated with a good prognosis. In this review, we summarize the role of this transcription factor in the development and prognosis of breast cancer in the hope of providing insights into utility of FOXA1 as a novel biomarker.

Providing Reliable Prognosis to Patients with Gastric Cancer in the Era of Neoadjuvant Therapies: Comparison of AJCC Staging Schemata

  • Kim, Gina;Friedmann, Patricia;Solsky, Ian;Muscarella, Peter;McAuliffe, John;In, Haejin
    • Journal of Gastric Cancer
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    • v.20 no.4
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    • pp.385-394
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    • 2020
  • Purpose: Patients with gastric cancer who receive neoadjuvant therapy are staged before treatment (cStage) and after treatment (ypStage). We aimed to compare the prognostic reliability of cStage and ypStage, alone and in combination. Materials and Methods: Data for all patients who received neoadjuvant therapy followed by surgery for gastric adenocarcinoma from 2004 to 2015 were extracted from the National Cancer Database. Kaplan-Meier (KM)curves were used to model overall survival based on cStage alone, ypStage alone, cStage stratified by ypStage, and ypStage stratified by cStage. P-values were generated to summarize the differences in KM curves. The discriminatory power of survival prediction was examined using Harrell's C-statistics. Results: We included 8,977 patients in the analysis. As expected, increasing cStage and ypStage were associated with worse survival. The discriminatory prognostic power provided by cStage was poor (C-statistic 0.548), while that provided by ypStage was moderate (C-statistic 0.634). Within each cStage, the addition of ypStage information significantly altered the prognosis (P<0.0001 within cStages I-IV). However, for each ypStage, the addition of cStage information generally did not alter the prognosis (P=0.2874, 0.027, 0.061, 0.049, and 0.007 within ypStages 0-IV, respectively). The discriminatory prognostic power provided by the combination of cStage and ypStage was similar to that of ypStage alone (C-statistic 0.636 vs. 0.634). Conclusions: The cStage is unreliable for prognosis, and ypStage is moderately reliable. Combining cStage and ypStage does not improve the discriminatory prognostic power provided by ypStage alone. A ypStage-based prognosis is minimally affected by the initial cStage.

Validity and Necessity of Sub-classification of N3 in the 7th UICC TNM Stage of Gastric Cancer

  • Li, Fang-Xuan;Zhang, Ru-Peng;Liang, Han;Quan, Ji-Chuan;Liu, Hui;Zhang, Hui
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.3
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    • pp.2091-2095
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    • 2013
  • Background: The $7^{th}$ TNM staging is the first authoritative standard for evaluation of effectiveness of treatment of gastric cancer worldwide. However, revision of pN classification within TNM needs to be discussed. In particular, the N3 sub-stage is becoming more conspicuous. Methods: Clinical data of 302 pN3M0 stage gastric cancer patients who received radical gastrectomy in Tianjin Medical University Cancer Institute and Hospital from January 2001 to May 2006 were retrospectively analyzed. Results: Location of tumor, depth of invasion, extranodal metastasis, gastric resection, combined organs resection, lymph node metastasis, rate of lymph node metastasis, negative lymph nodes count were important prognostic factors of pN3M0 stage gastric cancers. TNM stage was also associated with prognosis. Patients at T2N3M0 stage had a better prognosis than other sub-classification. T3N3M0 and T4aN3aM0 patients had equal prognosis which followed the T2N3M0. T4aN3bM0 and T4bN3aM0 had lower survival rate than the formers. T4bN3bM0 had worst prognosis. In multivariate analysis, TNM stage group and rate of lymph node metastasis were independent prognostic factors. Conclusions: The sub-stage of N3 may be useful for more accurate prediction of prognosis; it should therefore be applied in the TNM stage system.