Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques

Cui, Yunpeng; Wang, Qiwei; Shi, Xuedong; Ye, Qianwen; Lei, Mingxing; Wang, Bailin

doi:10.3389/fonc.2022.1095059

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…Secondly, the use of a limited number of marker genes could introduce noise into practical applications; substituting them with more extensive sets of markers could enhance score reliability without substantial accuracy compromise. Thirdly, some more advanced techniques, such as machine learning, can be used to improve the accuracy of the prediction (47)(48)(49). Lastly, our analysis relied on bioinformatics approaches; additional cell or animal experiments are requisite to unveil the prospective roles of the identified genes in the progression of colon cancer.…”

Section: Discussionmentioning

confidence: 99%

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

Chai,

Zhao,

et al. 2023

Front. Oncol.

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BackgroundColon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model.MethodsscRNA-seq data was downloaded from the GSE161277 database. R packages including “Seurat”, “Harmony”, and “singleR” were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using “Metascape”. DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the “oncoPredict” R package.ResultsUsing scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications.ConclusionWe developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.

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Section: Discussionmentioning

confidence: 99%

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

Chai,

Zhao,

et al. 2023

Front. Oncol.

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“…Machine learning approaches offer flexibility and scalability, making them suitable for various tasks such as risk stratification and survival estimation, particularly when analyzing big data [ 35 ]. Previously, machine learning has been used to develop prediction models to predict the mortality among bone metastases from specific cancer types, such as hepatocellular carcinoma [ 15 ], lung cancer [ 16 ], cancer of unknown primary site [ 36 ], and breast cancer [ 37 ]. However, our study was specifically designed to develop a machine learning model to predict the risk of early death among patients with general bone metastases.…”

Section: Discussionmentioning

confidence: 99%

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study

Lei,

Wu,

Zhang

et al. 2023

J Med Internet Res

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Background Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. Objective This study aimed to develop a machine learning–based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. Methods This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches—logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine—were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. Results In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was −0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. Conclusions This study develops a machine learning–based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.

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“…Random Forest (RF) is an extension of decision tree learning that combines multiple randomly generated decision trees to improve decision making. Given the complexity of biology, data scientists usually prefer RF to decision tree ( Breiman, 2001 ; Cui et al, 2022b ).…”

Section: Machine Learning In Cancer Researchmentioning

confidence: 99%

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Mondello,

Dal Bo,

Toffoli

et al. 2024

Front. Pharmacol.

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Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

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Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques

Cited by 8 publications

References 27 publications

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

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