Survival Nomogram for Metastasis Colon Cancer Patients Based on SEER Database

Tai, Qin-wen; Xue, Wei; Li, Mengying; Zhuo, Shuli; Zhang, Heng; Fang, Fa; Zhang, Jinhui

doi:10.3389/fgene.2022.832060

Cited by 14 publications

(10 citation statements)

References 28 publications

(41 reference statements)

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“…Donnelly et al ( 24 ) used univariate and multivariable analyses and found that old age, being unmarried, and living alone formed the independent risk factors of CRC in the United Kingdom. Moreover, Tai et al utilized Cox analysis and found that eight factors including age, grade, surgery, and primary site were significant prognostic factors of mCRC ( 20 ), which is consistent with our research. However, compared with previous studies, our study has some distinct advantages.…”

Section: Discussionsupporting

confidence: 91%

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Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang

Wei

et al. 2022

Front. Public Health

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PurposeThe purpose of this study was to investigate the clinical and non-clinical characteristics that may affect the early death rate of patients with metastatic colorectal carcinoma (mCRC) and develop accurate prognostic predictive models for mCRC.MethodMedical records of 35,639 patients with mCRC diagnosed from 2010 to 2019 were obtained from the SEER database. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. X-tile software was utilized to identify the optimal cutoff point for age and tumor size. Univariate and multivariate logistic regression models were used to determine the independent predictors associated with overall early death and cancer-specific early death caused by mCRC. Simultaneously, predictive and dynamic nomograms were constructed. Moreover, logistic regression, random forest, CatBoost, LightGBM, and XGBoost were used to establish machine learning (ML) models. In addition, receiver operating characteristic curves (ROCs) and calibration plots were obtained to estimate the accuracy of the models. Decision curve analysis (DCA) was employed to determine the clinical benefits of ML models.ResultsThe optimal cutoff points for age were 58 and 77 years and those for tumor size of 45 and 76. A total of 15 independent risk factors, namely, age, marital status, race, tumor localization, histologic type, grade, N-stage, tumor size, surgery, radiation, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis, were significantly associated with the overall early death rate of patients with mCRC and the cancer-specific early death rate of patients with mCRC, following which nomograms were constructed. The ML models revealed that the random forest model accurately predicted outcomes, followed by logistic regression, CatBoost, XGBoost, and LightGBM models. Compared with other algorithms, the random forest model provided more clinical benefits than other models and can be used to make clinical decisions in overall early death and specific early death caused by mCRC.ConclusionML algorithms combined with nomograms may play an important role in distinguishing early deaths owing to mCRC and potentially help clinicians make clinical decisions and follow-up strategies.

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Section: Discussionsupporting

confidence: 91%

“…mCRC in more than 65% of patients recur after surgical treatment ( 19 ). The long-term survival of patients with mCRC has attracted wide public attention; however, studies focusing on early death in patients with mCRC are rare and should be conducted ( 20 , 21 ).…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang

Wei

et al. 2022

Front. Public Health

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“…Over the last decade, the median overall survival for patients diagnosed with mCRC has doubled (26). Regarding treatment options, fluoropyrimidines alone or combined with oxaliplatin or irinotecan became a standard regimen choice.…”

Section: Colorectal Cancermentioning

confidence: 99%

Modulation of tumor environment in colorectal cancer – could gut microbiota be a key player?

Mendes

Vicente²,

Vitorino³

et al. 2022

Front. Gastroenterol.

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The treatment paradigm of neoplastic diseases has dramatically shifted with the introduction of immune checkpoint inhibitors (ICI). They induce a durable response in a wide variety of solid tumors, but this response depends on the infiltration of lymphocytes capable of recognizing and killing tumor cells. The primary predictor of intrinsic immune resistance to ICIs is the absence of lymphocytes in the tumor, the so-called “cold tumors”. Colorectal cancer (CRC) remains one of the most common and challenging cancer, but it is not traditionally considered a highly immunogenic tumor. In fact, immunotherapy showed a remarkable antitumoral activity only on a small subset of CRC patients – the ones with microsatellite instability-high/deficient DNA mismatch repair (MSI-H/dMMR). Most CRCs display a molecular microsatellite stability/proficient DNA mismatch repair (MSS/pMMR) profile, so strategies to improve tumor immunogenicity are crucial. Therefore, ongoing studies investigate new approaches to convert “cold” to “hot” tumors in MSS/pMMR CRCs. In addition, it has been described that gut microbiota influences tumor development and the host immune response. Hence, the microbiota may modulate the immune response, becoming a promising biomarker to identify patients who will benefit from ICIs. Future data will help to better understand microbiota mechanisms and their role in ICI efficacy. Precision medicine in cancer treatment could involve modulation of the microbiota through different strategies to improve tumor immunogenicity. In this review, we aim to present the potential relationship between gut microbiota and the modulation of the immune system and the hypothetical implications in CRC treatment, namely ICIs.

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“…In previous studies, we constructed a clinical prediction model based on the clinical data of metastatic colon cancer patients extracted from the SEER database. The nomogram developed with high prognosis prediction accuracy to evaluate the 1-, 3-, and 5-year survival of metastatic colon cancer patients, which will help clinical decision-making of metastatic colon cancer patients after surgery and individualized treatment [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

et al. 2022

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Background. The current high mortality rate of female breast cancer (BC) patients emphasizes the necessity of identifying powerful and reliable prognostic signatures in BC patients. Epithelial-mesenchymal transition (EMT) was reported to be associated with the development of BC. The purpose of this study was to identify prognostic biomarkers that predict overall survival (OS) in female BC patients by integrating data from TCGA database. Method. We first downloaded the dataset in TCGA and identified gene signatures by overlapping candidate genes. Differential analysis was performed to find differential EMT-related genes. Univariate regression analysis was then performed to identify candidate prognostic variables. We then developed a prognostic model by multivariate analysis to predict OS. Calibration curves, receiver operating characteristics (ROC) curves, C -index, and decision curve analysis (DCA) were used to test the veracity of the prognostic model. Result. In this study, we identified and validated a prognostic model integrating age and six genes (CD44, P3H1, SDC1, COL4A1, TGFβ1, and SERPINE1). C -index values for BC patients were 0.672 (95% CI 0.611–0.732) and 0.692 (95% CI 0.586–0.798) in the training cohort and test set, respectively. The calibration curve and the DCA curve show the good predictive performance of the model. Conclusion. This study offered a robust predictive model for OS prediction in female BC patients and may provide a more accurate treatment strategy and personalized therapy in the future.

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Survival Nomogram for Metastasis Colon Cancer Patients Based on SEER Database

Cited by 14 publications

References 28 publications

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Modulation of tumor environment in colorectal cancer – could gut microbiota be a key player?

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

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