Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

Wang, Libo; Liu, Zaoqu; Liang, Ruopeng; Wang, Weijie; Zhu, Ruijie; Li, Jian; Xing, Zhe; Weng, Siyuan; Han, Xinwei; Sun, Yuling

doi:10.7554/elife.80150

Cited by 35 publications

(25 citation statements)

References 77 publications

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“…To further verify the clinical solution of AIDPS, AIDPS has demonstrated promising predictive performance in various other diseases. 43 Furthermore, the relationship between AIDPS and chemotherapy efficacy remains unclear and warrants further investigation. We hypothesize that AIDPS may exhibit a complementary interaction with chemotherapy, thereby enhancing treatment comprehensiveness and improving patient prognosis.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of the interaction of antigen presentation during anti‐tumour immunity and establishment of AIDPS systems in ovarian cancer

Sun,

Xu,

Gao

et al. 2024

J Cellular Molecular Medi

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There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation‐associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival‐SVM). Then, in the train cohort, the prediction model was fitted using a leave‐one cross‐validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C‐index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C‐index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning‐based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C‐index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C‐index and the highest C‐index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C‐index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen‐presenting anti‐tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen‐presenting model obtained by the RSF + Lasso method has the best C‐INDEX, which plays a key role in developing antigen‐presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of the interaction of antigen presentation during anti‐tumour immunity and establishment of AIDPS systems in ovarian cancer

Sun,

Xu,

Gao

et al. 2024

J Cellular Molecular Medi

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“…The AIDPI model for OSA was created following a wellestablished workflow, 13,14 Cox regression analysis for the risk scores of all models across these three sets. (5) The model displaying the highest average C-index was automatically selected as optimal.…”

Section: Construction Of the Aidpimentioning

confidence: 99%

Identifying squalene epoxidase as a metabolic vulnerability in high‐risk osteosarcoma using an artificial intelligence‐derived prognostic index

Wang,

Ma,

et al. 2024

Clinical & Translational Med

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BackgroundOsteosarcoma (OSA) presents a clinical challenge and has a low 5‐year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high‐risk OSA patients and guide treatment.MethodsWe combined 10 machine‐learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single‐cell RNA sequencing (scRNA‐seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA‐seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.ResultsThe artificial intelligence‐derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high‐AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K–Akt signalling, and diminished immune infiltration. ScRNA‐seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE‐specific inhibitor FR194738 demonstrated anti‐OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.ConclusionsAIDPI is a robust biomarker for identifying the high‐risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.

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“…We performed our previous workflow to construct a consensus prognosis model for EC patients (20,21). Firstly, we constructed a combination of 89 machine learning algorithms based on the characteristics of the nine algorithms, including random forest (RSF), gradient boosting machine (GBM), survival support vector machine (Survival-SVM), supervised principal components (SuperPC), ridge regression, partial least squares regression for Cox (plsRcox), CoxBoost, Stepwise Cox (StepCox), and elastic network (Enet).…”

Section: Machine Learning-derived Prognostic Neddylation-related Risk...mentioning

confidence: 99%

Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer

Niu

Wang

2023

Front. Oncol.

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Endometrial cancer (EC) is women’s fourth most common malignant tumor. Neddylation plays a significant role in many diseases; however, the effect of neddylation and neddylation-related genes (NRGs) on EC is rarely reported. In this study, we first used MLN4924 to affect the activation of neddylation in different cell lines (Ishikawa and HEC-1-A) and determined the critical role of neddylation-related pathways for EC progression. Subsequently, we screened 17 prognostic NRGs based on expression files of the TCGA-UCEC cohort. Based on unsupervised consensus clustering analysis, patients with EC were classified into two neddylation patterns (C1 and C2). In terms of prognosis, substantial differences were observed between the two patterns. Compared with C2, C1 exhibited low levels of immune infiltration and promoted tumor progression. More importantly, based on the expression of 17 prognostic NRGs, we transformed nine machine-learning algorithms into 89 combinations. The random forest (RSF) was selected to construct the neddylation-related risk score according to the average C-index of different cohorts. Notably, our score had important clinical implications for EC. Patients with high scores have poor prognoses and a cold tumor state. In conclusion, neddylation-related patterns and scores can distinguish tumor microenvironment (TME) and prognosis and guide personalized treatment in patients with EC.

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Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

Cited by 35 publications

References 77 publications

Comprehensive analysis of the interaction of antigen presentation during anti‐tumour immunity and establishment of AIDPS systems in ovarian cancer

Comprehensive analysis of the interaction of antigen presentation during anti‐tumour immunity and establishment of AIDPS systems in ovarian cancer

Identifying squalene epoxidase as a metabolic vulnerability in high‐risk osteosarcoma using an artificial intelligence‐derived prognostic index

Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer

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