scholarly journals Comparison of Resected Malignant Tumors of the Right- and Left-Sided Colon—Is There a Difference?

2021 ◽  
Author(s):  
Adrienn Biró ◽  
László Ternyik ◽  
Krisztián Somodi ◽  
Anna Dawson ◽  
Eszter Csulak ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Survival Data ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Rank Test ◽  
Cox Regression Analysis ◽  
Study Results ◽  
The Right

AbstractEmbryological, anatomical, and immunological differences between the right-sided and left-sided colons are well known, but the difference in oncological behavior of colon tumors has only recently become the main subject of studies. Published articles propose that there is a difference not only in symptoms, but also in survival. Our aim was to analyze the clinicopathological and oncological differences among our patients who had been operated for colon cancer in our department. We examined the historical data of our patients who underwent colon resection for malignancy between 1st of January 2016 and 31st of December 2018. Tumor markers, histological results, postoperative complications, and oncological therapies were investigated. The primary outcome was overall survival. We analyzed our patients’ survival data with Kaplan–Meier log-rank test and Cox regression analysis. In our study, 267 patients were enrolled. One hundred thirty-three (49.8%) patients had right-sided colon cancer; 134 (50.2%) patients had left-sided colon cancer. Patients with right-sided colon cancer were significantly more likely to have mucinous adenocarcinoma (p = 0.037). No significant differences were revealed in overall survival between right-sided colon cancer and left-sided colon cancer patients (p = 0.381). Additional subgroup analysis showed that there were no significant differences in overall survival for laterality neither in the metastatic group (p = 0.824) nor in the non-metastatic group (p = 0.345). Based on the conflicting previous study results, our findings repeatedly highlight that the relationship between tumor location in the colon and overall survival is not straightforward.

scholarly journals Clinicopathological and molecular differences in colorectal cancer according to location

2019 ◽  
Vol 34 (1) ◽  
pp. 47-53 ◽  
Author(s):  
Yu-Lun Hsu ◽  
Chun-Chi Lin ◽  
Jeng-Kai Jiang ◽  
Hung-Hsin Lin ◽  
Yuan-Tzu Lan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Rank Test ◽  
Advanced Tumor Stage ◽  
Tumor Seeding ◽  
Cox Regression Analysis ◽  
Advanced Tumor ◽  
Tumor Node Metastasis Stage

Purpose: The incidence, pathogenesis, molecular pathways, and outcomes of colorectal cancer vary depending on the location of the tumor. This study aimed to compare the difference in tumor characteristics and the outcome between right-sided colon cancer and left-sided colorectal cancer (LCRC). Materials and methods: A total of 1503 patients with colorectal cancer who underwent surgery at the Taipei Veterans General Hospital between 2000 and 2010 were enrolled in this study. Right-sided colon cancer was defined as cancers in the cecum, ascending colon, and transverse colon, while LCRC was defined as cancers in the splenic flexure colon, descending colon, sigmoid colon, and rectum. The endpoint was overall survival. The mutations were detected via polymerase chain reaction and MASS array. The prognostic value was determined using the log-rank test and the Cox regression analysis. Results: A total of 407 and 1096 cases were classified as right-sided colon cancer and LCRC, respectively. Compared to patients with LCRC, those with right-sided colon cancer had more mucinous type cancer (7.4% vs. 3.5%), poorly differentiated tumor (11.5% vs. 3.6%), and advanced tumor-node-metastasis stage. The risk for peritoneal tumor seeding was higher in the right-sided colon cancer group (12.8% vs. 5.7%). Overall survival was better in LCRC than in right-sided colon cancer ( P=0.036). Conclusions: In our study, right-sided colon cancer had a more advanced tumor stage, a higher risk of peritoneal metastasis, and a poorer outcome than LCRC. Moreover, right-sided colon cancer had more gene mutations in BRAF, KRAS, SMAD4, TGF-β, PIK3CA, PTEN, AKT1, and high microsatellite instability.

scholarly journals NKX6.1 Expression is Associated With The Prognosis in Breast Cancer.

2020 ◽  
Author(s):  
Jie Zhang ◽  
Sujie Zhang ◽  
Xiaoyan Li ◽  
Fan Zhang ◽  
Lei Zhao
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Cox Regression ◽  
Expression Level ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Log Rank Test ◽  
Cancer Tissues

Abstract Background: Breast cancer is the most common cancer among women in the world. NKX6.1 is proved to be involved in several human cancers, but fewer researches have reported the functional roles of NKX6.1 in breast cancer. In this study, we investigated the clinical significance of NKX6.1 expression in breast cancer prognosis.Methods: The expression level of NKX6.1 in breast cancer tissues and paired non-cancerous tissues were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Chi-square test was applied to evaluate the relationship between NKX6.1 expression and clinicopathologic parameters. The overall survival of breast cancer patients were analyzed by Kaplan-Meier method with log rank test. Additionally, cox regression analysis was used for prognosis analysis.Results: NKX6.1 expression level is increased in breast cancer tissues (P<0.001). Moreover, the elevated levels were significantly correlated with tumor size (P=0.002), TNM stage (P=0.018) and lymph node metastasis (P=0.007). In addition, breast cancer patients with high NKX6.1 level had a poorer overall survival than those with low level (log rank test, P=0.001). NKX6.1 was an independent prognostic factor for breast cancer (HR=2.961, 95%CI=1.368-6.411, P=0.006).Conclusions: NKX6.1 is up-regulated in breast cancer, which may be a potential prognostic biomarker for the cancer.

Treatment of cancer of the anal canal. The Hamburg experience

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 14062-14062
Author(s):  
V. Rudat ◽  
S. Streller ◽  
D. Rades
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Anal Canal ◽  
Survival Data ◽  
Cox Regression ◽  
Haemoglobin Concentration ◽  
International Standards ◽  
Surgical Approaches ◽  
Rank Test ◽  
Cox Regression Analysis

14062 Background: To compare surgical and non-surgical treatment approaches for anal cancer and to identify prognostic factors. Methods: Survival data of 214 patients with cancer of the anal canal were reviewed who were referred for radiotherapy to the Department of Radiation Oncology of the University of Hamburg, Germany between 1/88 and 3/05. 75 patients received a definitive radiochemotherapy (RCT) with 5-FU and MMC according to international standards, 43 an operation followed by RCT (OP+RCT), 37 an operation followed by irradiation (OP+RT), 25 an irradiation alone (RT) and 34 an operation alone because they refused a planned adjuvant RCT or RT. The operations had been performed by different referring hospitals in and around Hamburg. Results: The median follow-up time of the living patients was 67 months (1–200 months). The 10 year overall survival rate for RCT was 0.62 (95%CI 0.46–0.77), for OP+RCT 0.65 (95%CI 0.47–0.83), for OP+RT 0.55 (95%CI 0.37–0.74), for OP alone 0.51 (95%CI 0.26–0.75) and for RT alone 0.27 (95%CI 0.05–0.48). There was no statistical difference between the overall survival of patients who received RCT, OP+RCT and OP+RT according to Kaplan Meier analysis (log rank test, p = 0.71). Cox regression analysis was used to examine the simultaneous influence of the prognostic factors T, N, age, haemoglobin concentration before radiotherapy, gender, and grading on the survival of patients who were treated with RCT. The model (p = 0.015) revealed T and N to be the only statistically significant prognostic factors. Conclusions: The different surgical and non-surgical approaches to treat cancer of the anal canal in Hamburg obviously reflect the individual preferences of the different physicians. Statistical analysis did not show a benefit of an OP added to RCT. Prognostic factors for survival after RCT were the T- and N-stage. No significant financial relationships to disclose.

scholarly journals Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2074
Author(s):  
Stefan Schiele ◽  
Tim Tobias Arndt ◽  
Benedikt Martin ◽  
Silvia Miller ◽  
Svenja Bauer ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Cox Regression ◽  
Locally Advanced ◽  
Risk Groups ◽  
High Risk Group ◽  
Binary Image ◽  
Rank Test ◽  
Cox Regression Analysis

In this study, we developed the Binary ImaGe Colon Metastasis classifier (BIg-CoMet), a semi-guided approach for the stratification of colon cancer patients into two risk groups for the occurrence of distant metastasis, using an InceptionResNetV2-based deep learning model trained on binary images. We enrolled 291 colon cancer patients with pT3 and pT4 adenocarcinomas and converted one cytokeratin-stained representative tumor section per case into a binary image. Image augmentation and dropout layers were incorporated to avoid overfitting. In a validation collective (n = 128), BIg-CoMet was able to discriminate well between patients with and without metastasis (AUC: 0.842, 95% CI: 0.774–0.911). Further, the Kaplan–Meier curves of the metastasis-free survival showed a highly significant worse clinical course for the high-risk group (log-rank test: p < 0.001), and we demonstrated superiority over other established risk factors. A multivariable Cox regression analysis adjusted for confounders supported the use of risk groups as a prognostic factor for the occurrence of metastasis (hazard ratio (HR): 5.4, 95% CI: 2.5–11.7, p < 0.001). BIg-CoMet achieved good performance for both UICC subgroups, especially for UICC III (n = 53), with a positive predictive value of 80%. Our study demonstrates the ability to stratify colon cancer patients via a semi-guided process on images that primarily reflect tumor architecture.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zheng Yao ◽  
Song Wen ◽  
Jun Luo ◽  
Weiyuan Hao ◽  
Weiren Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Decision Tree ◽  
Survival Data ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Cancer Hallmarks ◽  
Mtorc1 Signaling

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC.

scholarly journals Identification and Validation of a Glycolysis Related Metabolic Reprogramming Gene Signature for Predicting Survival in Colon Cancer Patients

2021 ◽  
Author(s):  
Gang Liu ◽  
Xiaowang WU ◽  
Jian Chen
Keyword(s):  
Colon Cancer ◽  
Prediction Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Prognosis Model ◽  
The Relationship

Abstract Background Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with high mortality rate. Because of malignancy and easily metastasis feather, and limited treatments, the prognosis of CC remains poor. Glycolysis is a metabolic process of glucose in anoxic environments which is an important way to provide energy for tumor. The role of glycolysis in CC largely remains unknown and is necessary to be explored. Method In our study, we analyzed glycolysis related genes expression in CC, patients gene expression and corresponding clinical data were downloaded from GEO dataset, glycolysis related genes sets were collected from Msigdb. Through COX regression analysis, prognosis model based on glycolysis-related genes was established. The efficacy of gene model was tested by Survival analysis, ROC analysis and PCA analysis. Furthermore, the relationship between risk scores and clinical characteristic was researched. Results Our findings identified 13 glycolysis related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2 and OGDHL) consisted prognostic prediction model with relative high accuracy. The relationship between prediction model and clinical feathers were specifically studied, results showed age > 65years, TNM III-IV, T3-4, N1-3, M1 and high-risk score were independent prognostic risk factors with poorer prognosis. Finally, model genes were significantly expressed and EMT were activated in CC patients. Conclusion This study provided a new aspect to advance our understanding in the potential mechanism of glycolysis in CC.

miR-181 Expression is Associated With The Prognosis in Lung Cancer.

2020 ◽  
Author(s):  
Yue Zhao ◽  
Xiangjun Kong ◽  
Hongbing Wang
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Prognostic Value ◽  
Cox Regression ◽  
Suppressor Gene ◽  
Rank Test ◽  
High Morbidity ◽  
Cox Regression Analysis ◽  
Log Rank Test ◽  
Cancer Tissues

Abstract Background: Lung cancer is one of the most common cancers, with high morbidity and mortality. MiRNAs are proved to play important roles in various human cancers. In our study, we aimed to explore the prognostic value of miR-181 in lung cancerMethods: Quantitative real-time polymerase chain reaction (QRT-PCR) was used to detect the expression level of miR-181 in lung cancer tissues and the paired non-cancerous tissues. The relationship between miR-181 expression and clinicopathologic parameters were analyzed by chi-square test. Kaplan-Meier method with log rank test was applied for overall survival analysis. Furthermore, the Cox regression analyses were performed to evaluate the prognostic value of miR-181 in lung cancer.Results: Down-regulated miR-181 expression was observed in lung cancer tissues (P<0.001), moreover, its expression was significantly correlated with TNM stage (P=0.015) and metastasis (P=0.000). In addition, lung cancer patients with lower miR-181 expression level had poorer overall survival than those with higher expression (log rank test, P=0.011). Cox regression analysis suggested that miR-181 was an independent prognostic factor for lung cancer (HR=1.961, 95%CI=1.135-3.388, P=0.016).Conclusion: MiR-181 may be a tumor suppressor gene in lung cancer, which can predict outcomes for the patients.

scholarly journals Cohort Study of the Impact of Time to Antibiotic Administration on Mortality in Patients with Febrile Neutropenia

2014 ◽  
Vol 58 (7) ◽  
pp. 3799-3803 ◽  
Author(s):  
Regis G. Rosa ◽  
Luciano Z. Goldani
Keyword(s):  
Cohort Study ◽  
Febrile Neutropenia ◽  
Cancer Patients ◽  
Cox Regression ◽  
Mortality Rates ◽  
Cox Proportional Hazards Model ◽  
Rank Test ◽  
Antibiotic Administration ◽  
Cox Regression Analysis ◽  
The Impact

ABSTRACTThe time to antibiotic administration (TTA) has been proposed as a quality-of-care measure in febrile neutropenia (FN); however, few data regarding the impact of the TTA on the mortality of adult cancer patients with FN are available. The objective of this study was to determine whether the TTA is a predictor of mortality in adult cancer patients with FN. A prospective cohort study of all consecutive cases of FN, evaluated from October 2009 to August 2011, at a single tertiary referral hospital in southern Brazil was performed. The TTA was assessed as a predictive factor for mortality within 28 days of FN onset using the Cox proportional hazards model. Kaplan-Meier curves were used for an assessment of the mortality rates according to different TTAs; the log-rank test was used for between-group comparisons. In total, 307 cases of FN (169 subjects) were evaluated. During the study period, there were 29 deaths. In a Cox regression analysis, the TTA was independently associated with mortality within 28 days (hazard ratio [HR], 1.18; 95% confidence interval [CI], 1.10 to 1.26); each increase of 1 h in the TTA raised the risk of mortality within 28 days by 18%. Patients with FN episodes with a TTA of ≤30 min had lower 28-day mortality rates than those with a TTA of between 31 min and 60 min (3.0% versus 18.1%; log-rankP= 0.0002). Early antibiotic administration was associated with higher survival rates in the context of FN. Efforts should be made to ensure that FN patients receive effective antibiotic therapy as soon as possible. A target of 30 min to the TTA should be adopted for cancer patients with FN.

scholarly journals Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhen Tan ◽  
Yubin Lei ◽  
Bo Zhang ◽  
Si Shi ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Cancer Center ◽  
Cox Regression Analysis

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.MethodIn this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan–Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.ResultsA four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.ConclusionWe constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

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