scholarly journals Novel Model to Predict the Prognosis of Patients with Stage II–III Colon Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yansong Xu ◽  
Fangfang Liang ◽  
Yi Chen ◽  
Zhen Wang ◽  
Huage Zhong ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Operation Mode ◽  
Stage Ii ◽  
Prognostic Indicators ◽  
Training Set ◽  
Clinical Prognosis ◽  
Kaplan Meier ◽  
Prognosis Model ◽  
Validation Set ◽  
The Relationship

Different opinions exist on the relationship between the C-reactive protein-to-albumin ratio (CAR) and the prognosis of colon cancer. This study is aimed at evaluating the relationship between CAR and prognosis of stage II–III colon cancer and establishing a clinical prognosis model. Patients were randomised to a training set (566 cases) and validation set (110 cases). The relationship between CAR and clinicopathological variables was calculated, and the Kaplan-Meier method was used to analyse the overall survival (OS) rate of colon cancer. In the training set, colon cancer independent risk factors were included in the prognosis model and then tested in the validation set. The accuracy and discrimination of the model were assessed using the C-index and calibration curves. Compared with patients with low CAR, patients with high CAR showed significantly poorer survival ( P = 0.020 ). In the multivariate analysis, CAR, carcinoembryonic antigen (CEA), lymph node metastasis, operation mode, and perineural invasion were identified as independent prognostic indicators and adopted to establish the prediction model. The C-index of the nomogram for predicting OS reached 0.751 in the training set and 0.719 in the validation set. The calibration curve exhibited good consistency. In the present study, the CAR may be an independent prognostic factor for stage II–III colon cancer, and the nomogram has a certain predictive value. However, further prospective large-sample research needs to be conducted to validate our findings.

scholarly journals Prognostic Value of Combined Preoperative Carcinoembryonic Antigen and Prognostic Nutritional Index in Patients With Stage II–III Colon Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan-song Xu ◽  
Gang Liu ◽  
Chang Zhao ◽  
Shao-long Lu ◽  
Chen-yan Long ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Multivariate Analysis ◽  
Prognostic Value ◽  
Operation Mode ◽  
Prognostic Nutritional Index ◽  
Stage Ii ◽  
Nutritional Index ◽  
Actual Observation ◽  
Validation Set ◽  
Patient Prognosis

Background: Tumor status can affect patient prognosis. Prognostic nutritional index (PNI), as a nutritional indicator, is closely related to the prognosis of cancer. However, few studies have examined the combined prognostic value of CEA and PNI in patients. This study investigated the relationship between CEA/PNI and prognosis of colon cancer patients.Methods: A total of 513 patients with stage II–III colon cancer who underwent curative resection at two medical centers from 2009 to 2019 were included. Clinicopathological factors were assessed and overall survival (OS) was assessed in a cohort of 413 patients. Multivariate analysis was used to identify independent prognostic variables to construct histograms predicting 1-year and 3-year OS. Data from 100 independent patients in the validation group was used to validate the prognostic model.Results: The median OS time was 33.6 months, and mortality was observed in 54 patients. Multivariate analysis revealed that preoperative CEA/PNI, lymph node metastasis, peripheral nerve invasion, operation mode, and postoperative chemotherapy were independent factors for prognosis evaluation and thus were utilized to develop the nomogram. The C-index was 0.788 in the learning set and 0.836 in the validation set. The calibration curves reached favorable consensus among the 1-, 3-year OS prediction and actual observation.Conclusion: The combined use of CEA and PNI is an independent prognostic factor and thus can serve as a basis for a model to predict the prognosis of patients with stage II–III colon cancer.

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.

scholarly journals A Novel Systematic Oxidative Stress Score Predicts the Prognosis of Patients with Operable Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Kaiming Zhang ◽  
Liqin Ping ◽  
Tian Du ◽  
Yan Wang ◽  
Ya Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Patients ◽  
Operable Breast Cancer ◽  
Breast Cancer Patients ◽  
Training Set ◽  
Oxidative Stress Biomarkers ◽  
Stress Biomarkers ◽  
Validation Set ◽  
The Relationship

Background. Breast cancer was associated with imbalance between oxidation and antioxidation. Local oxidative stress in tumors is closely related to the occurrence and development of breast cancer. However, the relationship between systematic oxidative stress and breast cancer remains unclear. This study is aimed at exploring the prognostic value of systematic oxidative stress in patients with operable breast cancer. Methods. A total of 1583 operable female breast cancer patients were randomly assigned into the training set and validation set. The relationship between systematic oxidative stress biomarkers and prognosis were analyzed in the training and validation sets. Results. The systematic oxidative stress score (SOS) was established based on five systematic oxidative stress biomarkers including serum creatinine (CRE), serum albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). SOS was an independent prognostic factor for operable breast cancer patients. A nomogram based on SOS and clinical characteristics could accurately predict the prognosis of operable breast cancer patients, and the area under the curve (AUC) of the nomogram was 0.823 in the training set and 0.872 in the validation set, which was much higher than the traditional prognostic indicators. Conclusions. SOS is an independent prognostic indicator for operable breast cancer patients. A prediction model based on SOS could accurately predict the outcome of operable breast cancer patients.

scholarly journals The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Stepwise Regression ◽  
Gene Signature ◽  
Training Set ◽  
Model Based ◽  
Kaplan Meier ◽  
Validation Set

Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma.Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score.Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal.Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.

scholarly journals Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Dan Sun ◽  
Tianren Li
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Risk Group ◽  
Low Risk ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Related Gene ◽  
Training Set ◽  
Kaplan Meier ◽  
Prognosis Model

Abstract Background: High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.Method: The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the Least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.Results: A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant. The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There were differences in some immune cells between the high-risk and low-risk groups as defined by the model. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, many of the immune-related pathways were enriched in the low-risk group, with antigen processing and presentation identified as the most enriched pathways.Conclusion: The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

Association of mucin production and survival in colon cancer.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 512-512 ◽  
Author(s):  
John Hogan ◽  
Georges Samaha ◽  
John Burke ◽  
David Waldron ◽  
Eoin Condon ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Univariate Analysis ◽  
Disease Free Survival ◽  
Free Survival ◽  
Mucin Production ◽  
Kaplan Meier ◽  
The Relationship ◽  
Disease Free

512 Background: Debate persists regarding the relationship between mucin production and cancer-related outcome following curative resection for colon cancer. Lack of consensus is due to (amongst other factors) discrepancies in definition, small cohort studies and the integration of both colon and rectal cancers. This study characterizes the relationship between mucin production and cancer-related outcome in an homogenous single-institute based cohort. Methods: A database spanning demographics, clinico-pathologic characteristics and prognostic factors was generated for all patients undergoing curative-intent colonic resection in the interval 2000 to 2010. Patients were categorized simply as mucin producing (i.e. MC) or non-mucin producing adenocarcinoma (NMC). Primary outcomes included overall survival (time to death from any cause) and disease free survival (time to loco-regional and systemic recurrence). Trends were established for MC and NMC using Kaplan-Meier estimates, plotted and compared using log-rank analysis. Findings significant on univariate analysis were incorporated into multivariate analysis. Cox proportional hazards model was employed to determine the associated hazard of both death and disease recurrence in each group. Statistical analysis was performed using R version 2.15. P < 0.05 was considered significant. Results: 77 mucinous carcinomas (MC) and 358 non mucinous carcinomas (NMC) were included. On univariate analysis, MC was associated with improved overall survival (OS) (P=0.007). Both N1 (HR 1.625, P=0.011) and N2 (HR 2.7, P<0.001) status were associated with adverse OS. On multivariate analysis, MC approached but did not reach statistical significance for improved OS (HR 0.543, P=0.061). A comparison of Kaplan-Meier estimates for overall survival in MC and NMC groups indicated that OS was significantly improved in the MC cohort (P=0.011). There was no difference in disease free survival (P=0.224). Systemic recurrence was greater in the NMC group (P=0.042). Conclusions: Mucin production in colonic adenocarcinoma appears associated with improved overall but not disease-free survival. In addition, the absence of mucin was associated with adverse systemic but not local recurrence.

scholarly journals FAM225B Is a Prognostic lncRNA for Patients with Recurrent Glioblastoma

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Junsheng Li ◽  
Qian Zhang ◽  
Peicong Ge ◽  
Chaofan Zeng ◽  
Fa Lin ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Value ◽  
Cox Regression ◽  
Recurrent Glioblastoma ◽  
Regulation Of Transcription ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Validation Set ◽  
Pathway Analyses

Objective. The overall survival of patients with recurrent glioblastoma (rGBM) is quite different, so clinical outcome prediction is necessary to guide personalized clinical treatment for patients with rGBM. The expression level of lncRNA FAM225B was analyzed to determine its prognostic value in rGBMs. Methods. We collected 109 samples of Chinese Glioma Genome Atlas (CGGA) RNA sequencing dataset and divided into training set and validation set. Then, we analyzed the expression of FAM225B, clinical characteristics, and overall survival (OS) information. Kaplan-Meier survival analysis was used to estimate the OS distributions. The prognostic value of FAM225B in rGBMs was tested by univariate and multivariate Cox regression analyses. Moreover, we analyzed the biological processes and signaling pathways of FAM225B. Results. We found that FAM225B was upregulated in rGBMs ( P = 0.0009 ). The expression of FAM225B increased with the grades of gliomas ( P < 0.0001 ). The OS of rGBMs in the low-expression group was significantly longer than that in the high-expression group ( P = 0.0041 ). Similar result was found in the training set ( P = 0.0340 ) and verified in the validation set ( P = 0.0292 ). In multivariate Cox regression analysis, FAM225B was identified to be an independent prognostic factor for rGBMs ( P = 0.003 ). Biological process and KEGG pathway analyses implied FAM225B mainly played a functional role on transcription, regulation of transcription, cell migration, focal adhesion, etc. Conclusions. FAM225B is expected to be as a new prognostic biomarker for the identification of rGBM patients with poor outcome. And our study provided a potential therapeutic target for rGBMs.

scholarly journals Expression and Prognostic Characteristics of m6A RNA Methylation Regulators in Colon Cancer

2021 ◽  
Vol 22 (4) ◽  
pp. 2134
Author(s):  
Liting Huang ◽  
Jie Zhu ◽  
Weikaixin Kong ◽  
Peifeng Li ◽  
Sujie Zhu
Keyword(s):  
Colon Cancer ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Cancer Cell Proliferation ◽  
Genetic Changes ◽  
Prognosis Model ◽  
Cancer Genome Atlas ◽  
M6a Rna Methylation ◽  
The Relationship

Colon cancer is a common and leading cause of death and malignancy worldwide. N6-methylation of adenosine (m6A) is the most common reversible mRNA modification in eukaryotes, and it plays a crucial role in various biological functions in vivo. Dysregulated expression and genetic changes of m6A regulators have been correlated with tumorigenesis, cancer cell proliferation, tumor microenvironment, and prognosis in cancers. This study used RNA-seq and colon cancer clinical data to explore the relationship between N6-methylation and colon cancer. Based on the seven m6A regulators related to prognosis, three molecular subgroups of colon cancer were identified. Surprisingly, we found that each subgroup had unique survival characteristics. We then identified three subtypes of tumors based on 299 m6A phenotype-related genes, and one subtype was characterized as an immunosuppressive tumor and patients in this subtype may be more suitable for immunotherapy than other subtypes. Finally, using m6A-related genes and clinical information from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model could be used to predict the prognosis of patients in clinics.

scholarly journals Improving the Prognosis of Colon Cancer Through Knowledge-Based Clinical-Molecular Integrated Analysis

2020 ◽  
Author(s):  
Danyang Tong ◽  
Yu Tian ◽  
Qiancheng Ye ◽  
Jun Li ◽  
Kefeng Ding ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Prognostic Model ◽  
Cox Model ◽  
Stage Ii ◽  
Integrated Analysis ◽  
Knowledge Based ◽  
Validation Set ◽  
Random Survival Forest

Abstract BackgroundColon cancer has high morbidity and mortality rates among cancers. Existing clinical staging systems cannot accurately assess the prognostic risk of colon cancer patients. Therefore, new prognostic factors are needed. In this study, a new pathway-based prognostic factor was discovered through a knowledge-based clinical-molecular integrated analysis.MethodsA total of 374 samples from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset were used as the discovery set and 98 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset were used as the validation set. After converting gene expression data into pathway dysregulation scores (PDS), the random survival forest and multicovariate Cox model were used to identify the best prognostic supplementary factors. Finally, a clinical prognostic model, a molecular prognostic model and a clinical-molecular integrated prognostic model were constructed to verify the supplementary effect of the discovered prognostic supplementary factor.ResultsThe PDS of 14 pathways played important roles in prognostic prediction together with clinical prognostic factors through the random survival forest. Further screening through the multicovariate Cox model revealed that the PDS of pathway hsa00532 was the best clinical prognostic supplementary factor. The clinical-molecular integrated prognostic model constructed with clinical prognostic factors and the discovered prognostic factor was superior to the clinical prognostic model and molecular prognostic model in discriminative performance (C-indexes of 0.773, 0.746, and 0.619 in the discovery set and 0.893, 0.808, and 0.810 in the validation set, respectively). The Kaplan-Meier (KM) curves of patients grouped by PDS suggested that patients with a higher PDS had poorer prognosis, and stage II patients could be distinctly distinguished.ConclusionThe PDS of pathway hsa00532 was a considerable clinical prognostic supplementary factor for colon cancer and may represent a potential prognostic marker for stage II colon cancer. The PDS calculation involves only 16 genes, which supports its potential clinical application prospects.

scholarly journals The Clinical Significance and Biological Function of PCDH7 in Cervical Cancer

2020 ◽  
Author(s):  
Shitong Zhang ◽  
Xianhu Fu
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Migration And Invasion ◽  
Cancer Cell Proliferation ◽  
Clinical Prognosis ◽  
Kaplan Meier ◽  
Cancer Tissues ◽  
The Relationship

Abstract Background: Cervical cancer is a common malignant tumor in women that is prone to recurrence and metastasis. Recently, many people have explored the role of protocadherin 7 (PCDH7) in cancer, and found that PCDH7 is abnormally expressed in many cancers. The purpose of this study is to investigate the expression and mechanism of PCDH7 in cervical cancer and evaluate its clinical prognostic significance.Methods: The expression of PCDH7 in cervical cancer and cells was detected by qRT-PCR. The relationship between PCDH7 expression and clinical prognosis was calculated by the Kaplan-Meier method and Cox regression analyses. The effects of PCDH7 on cancer cell proliferation, migration, and invasion were studied by MTT assay and transwell assays.Results: The expression of PCDH7 in cervical cancer tissues and cell lines was significantly down-regulated compared with the control. Low PCDH7 expression was associated with low survival rate. PCDH7 expression was significantly correlated with lymph node metastasis, cell differentiation, and FIGO staging. PCDH7 can be used as an independent prognostic factor for cervical cancer. Up-regulation of PCDH7 significantly inhibited the proliferation, migration, and invasion of cancer cells.Conclusions: PCDH7 may be used as a biomarker for the prognosis of cervical cancer.

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