Novel nomograms to predict lymph node metastasis and liver metastasis in patients with early colon carcinoma

Yan, Yongcong; Liu, Haohan; Mao, Kai; Zhang, Mengyu; Zhou, Qicun; Yu, Wei; Shi, Bingchao; Wang, Jie; Xiao, Zhiyu

doi:10.1186/s12967-019-1940-1

Cited by 34 publications

(28 citation statements)

References 34 publications

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“…2 Due to the fact that clinical staging is critical for disease course prediction and diagnosis and that tailoring of treatment to CRC stage can positively impact CRC patient outcomes, clinicians recommend use of TNM staging to guide patient treatment. 3 High-throughput omics analysis technologies, such as genome sequencing and other omics-based methods, have greatly enhanced our understanding of molecular mechanisms underlying complex diseases such as cancer. 4 Over the past 20 years of high-throughput omics technical development, numerous researchers have used bioinformatics screening tools to discover gene candidates with cancer-specific expression for use as cancer biomarkers.…”

Section: Introductionmentioning

confidence: 99%

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BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p

Chen¹,

Qin

Dai³

et al. 2020

CMAR

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Purpose We explored specific expression profiles of BGN and COL11A1 genes and studied their biological functions in CRC using bioinformatics tools. Patients and Methods A total of 68 pairs of cancer and non-cancerous tissues from CRC patients were enrolled in this study. Methods we used in this articles including: qRT-PCR, Western blot analysis, ELISA, GO and KEGG regulatory network analysis, tumor infiltration, luciferase reporter-based protein and etc. Results According to The Cancer Genome Atlas (TCGA) data, BGN and COL11A1 expression levels were significantly higher in CRC patient samples than in samples from healthy controls. Moreover, levels were much higher in late-stage CRC than in early-stage disease, warranting evaluation of these genes as CRC prognostic biomarkers. Subsequently, qRT-PCR, Western blot analysis, and ELISA results obtained from analyses of CRC cells, tissues, and patient sera aligned with TCGA results. GO and KEGG regulatory network analysis revealed BGN- and COL11A1- associated genes that were functionally related to extracellular matrix (ECM) receptor pathway activation, with transcription factor genes RELA and NFKB1 positively associated with BGN expression and CEBPZ and SIRT1 with COL11A1 expression. Meanwhile, BGN and COL11A1 expression were separately and significantly correlated to tumor infiltration by six immune cell types. Additionally, kinase genes PLK1 and LYN appeared to be downstream targets of differentially expressed BGN and COL11A1 , respectively. In addition, the expression of PLK1 mRNA was down-regulated while BGN was down-regulated. Finally, BGN effects on CRC cell proliferation, cycle, apoptosis, invasion, and migration were studied using molecular biological methods, including luciferase reporter-based protein analysis, qRT-PCR, and Western blot results, which revealed that miR-6828-5p may regulate BGN expression. Conclusion We speculate that the use of BGN and COL11A1 as CRC biomarkers would improve CRC staging, while also providing several novel targets for use in the development of more effective CRC treatments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p

Chen¹,

Qin

Dai³

et al. 2020

CMAR

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“…It may be caused by different hormone levels between male and female, which deserved to be confirmed by further molecular mechanism research. Several reports indicated that the tumor with larger size had the higher risk of distant metastasis [ 20 – 22 ]. Lymph node metastasis and higher pathological grades are important predictors for the survival of ICC patients [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Construction of Nomograms for Predicting Lung and Bone Metastases in Patients with Intrahepatic Cholangiocarcinoma and Identification of Patients Who Can Benefit from Chemotherapy

Shi

Zou

Zhang

et al. 2020

Journal of Oncology

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Objective. The purpose of our study is to build nomograms for predicting the possibility of lung metastasis (LM) and bone metastasis (BM) in patients with intrahepatic cholangiocarcinoma (ICC). Methods. 1527 patients diagnosed with ICC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable logistic regression analyses were used to recognize the predictors of LM and BM, respectively. Then two nomograms were established. We applied the C-index, calibration plot, receiver-operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the novel nomograms. The maximum values of the Youden indexes from the ROC curves were utilized to select the cutoff points of the nomograms. The Kaplan–Meier survival curves were used to evaluate the effect of chemotherapy in different groups. The bootstrap resampling method was chosen for internal validation. Results. Five predictors for LM and three predictors for BM were identified, and two nomograms were constructed. The nomograms had high values of C-indexes, reaching 0.821 (95% CI 0.772–0.871) for LM and 0.759 (95% CI 0.700–0.818) for BM. C-indexes of 0.814 for LM and 0.749 for BM were also observed in internal validation. The calibration plots, ROC curves, and DCAs exhibited favorable performances for predicting LM and BM. The cutoff points of total points in nomograms were 108 for LM and 144 for BM, which could distinguish between high-risk and low-risk groups for LM and BM. Chemotherapy is suggested to undergo for patients in high-risk groups. Conclusions. The nomograms could assess the possibility of LM and BM in ICC patients and determine the optimal treatment.

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“…Similarly, in the testing set, the value of nomogram was also more signi cant than mitotic rate (0.711, 0.680 to 0.739, P < 0.001), tumor size (0.682, 0.652 to 0.712, P < 0.001), N stage (0.598, 0.566 to 0.629, P < 0.001) and tumor location (0.574, 0.542 to 0.606, P < 0.001). Furthermore, by the use of the maximum Youden index in the training group [19], the cutoff values of 170 and 188 were get, the patients were then divided into high-risk and low-risk groups with the results shown as pie charts ( Fig.3c, d).…”

Section: Construction and Validation Of Lim Nomogrammentioning

confidence: 99%

“…In recent years, Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram has been worldwide used to generate the probability of a clinical event through a complex computational formula [18,19]. With the aid of nomogram, clinicians can assess the risk of the clinical event and then design individual treatment plans, determine the use of adjuvant therapy, optimize aspects of therapies and consider appropriate patient counselling [20].…”

Section: Introductionmentioning

confidence: 99%

A novel nomogram for predicting liver metastasis in patients with gastrointestinal stromal tumor: A SEER-based study

Zhou

Xiao

Gong

et al. 2020

Preprint

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Background: Liver metastasis (LIM) of gastrointestinal stromal tumor (GIST) is associated with poor prognosis. The present study aimed at developing and validating nomogram to predict LIM in patients with GIST, thus helping clinical diagnosis and treatment. Methods: The data of GIST patients derived from Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016, which were then screened by univariate and multivariate logistic regression for the construction of LIM nomogram. The model discrimination of LIM nomogram was evaluated by concordance index (C-index) and calibration plots, while the predictive accuracy and clinical values were measured by decision curve analysis (DCA) and clinical impact plot. Furthermore, we validated predictive nomogram in the internal testing set. Results: A total of 3797 patients were enrolled and divided randomly into training and validating groups in a 3-to-1 ratio. After logistic regression, the significant variables were sex, tumor location, tumor size, N stage and mitotic rate. The calibration curves showed the perfect agreement between nomogram predictions and actual observations, while the DCA and clinical impact plot showed the clinical utility of LIM nomogram. C-index of the nomogram was 0.812. What’s more, receiver operating characteristic curves (ROC) also showed good discrimination and calibration in the training set (AUC=0.794, 95% CI: 0.778 to 0.808) and the testing set (AUC=0.775, 95% CI: 0.748 to 0.802). Conclusion: The nomogram for patients with GIST can effectively predict the individualized risk of liver metastasis and provide insightful information to clinicians to optimize therapeutic regimens.

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Novel nomograms to predict lymph node metastasis and liver metastasis in patients with early colon carcinoma

Cited by 34 publications

References 34 publications

BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p

BGN and COL11A1 Regulatory Network Analysis in Colorectal Cancer (CRC) Reveals That BGN Influences CRC Cell Biological Functions and Interacts with miR-6828-5p

Construction of Nomograms for Predicting Lung and Bone Metastases in Patients with Intrahepatic Cholangiocarcinoma and Identification of Patients Who Can Benefit from Chemotherapy

A novel nomogram for predicting liver metastasis in patients with gastrointestinal stromal tumor: A SEER-based study

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