A multi-omics machine learning framework in predicting the survival of colorectal cancer patients

Yang, Min; Yang, Huandong; Ji, Lei; Hu, Xuan; Tian, Geng; Wang, Bing; Yang, Jialiang

doi:10.1016/j.compbiomed.2022.105516

Cited by 44 publications

(35 citation statements)

References 44 publications

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“…To our knowledge, this is the first attempt to construct prognostic nomograms for OS and RFS to predict the prognosis of patients with HCC with different invasive treatments, including LT, LR, and minimally invasive approach (RFA or MWA). Although our nomograms had good performance in predicting survival at 3 and 5 years in patients with HCC, it might be further improved by integrating more types of data, such as pathological images and multi-omics data (17,21), and by applying more advanced classification algorithms as used in cancer diagnosis and other biological problems (15,22). In the future, we will explore these directions.…”

Section: Discussionmentioning

confidence: 95%

“…Several other factors were reported as potential predictor for the postoperative outcomes of patients with HCC, such as aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin score (ALBI), the Model for Endstage Liver Disease (MELD) score and the Child-Pugh score (11,13,14). In addition, there are a few machine learning models for predicting the prognosis of cancer patients for other types of cancers based on histopathological images and multi-omics data (15)(16)(17)(18). However, to our best knowledge, an accurate model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

et al. 2022

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BackgroundTreatments for patients with early‐stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.MethodsWe retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan–Meier survival curve, and decision curve analyses (DCAs), respectively.ResultsPrognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.ConclusionsWe developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

et al. 2022

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“…It is critical to identify prognostic biomarkers that can predict the efficacy of a treatment, as well as the recurrence and survival of cancer patients. Many studies have focused on identifying these biomarkers ( 14 – 18 ), among which TMB received more and more attention. TMB refers to the sum of substitution, insertion, and deletion mutations in the coding region of the evaluated tumor cell genes ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

Liang²,

Dai³

2022

Front. Oncol.

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There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.

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“…Growing research has suggested that microbial communities influence the occurrence, progression, metastasis, and response to therapy of multiple cancers (Cullin et al, 2021;Yang M. et al, 2022). For example, studies have shown that Fusobacterium nucleatum may trigger cancer through multiple ways, and is related to cancer cell invasion and metastasis (Bullman et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Bacterial biomarkers capable of identifying recurrence or metastasis carry disease severity information for lung cancer

Yuan

Wang

et al. 2022

Front. Microbiol.

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BackgroundLocal recurrence and distant metastasis are the main causes of death in patients with lung cancer. Multiple studies have described the recurrence or metastasis of lung cancer at the genetic level. However, association between the microbiome of lung cancer tissue and recurrence or metastasis remains to be discovered. Here, we aimed to identify the bacterial biomarkers capable of distinguishing patients with lung cancer from recurrence or metastasis, and how it related to the severity of patients with lung cancer.MethodsWe applied microbiome pipeline to bacterial communities of 134 non-recurrence and non-metastasis (non-RM) and 174 recurrence or metastasis (RM) samples downloaded from The Cancer Genome Atlas (TCGA). Co-occurrence network was built to explore the bacterial interactions in lung cancer tissue of RM and non-RM. Finally, the Kaplan–Meier survival analysis was used to evaluate the association between bacterial biomarkers and patient survival.ResultsCompared with non-RM, the bacterial community of RM had lower richness and higher Bray–Curtis dissimilarity index. Interestingly, the co-occurrence network of non-RM was more complex than RM. The top 500 genera in relative abundance obtained an area under the curve (AUC) of 0.72 when discriminating between RM and non-RM. There were significant differences in the relative abundances of Acidovorax, Clostridioides, Succinimonas, and Shewanella, and so on between RM and non-RM. These biomarkers played a role in predicting the survival of lung cancer patients and were significantly associated with lung cancer stage.ConclusionThis study provides the first evidence for the prediction of lung cancer recurrence or metastasis by bacteria in lung cancer tissue. Our results highlights that bacterial biomarkers that distinguish RM and non-RM are also associated with patient survival and disease severity.

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A multi-omics machine learning framework in predicting the survival of colorectal cancer patients

Cited by 44 publications

References 44 publications

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

Bacterial biomarkers capable of identifying recurrence or metastasis carry disease severity information for lung cancer

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