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Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle

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Abstract

Background

Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment.

Methods

Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312).

Results

Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05).

Conclusion

This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

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Data availability

The dataset generated during the current study is available in the GEO (http://www.ncbi.nlm.nih.gov/geo/) repository with accession codes GSE10846, GSE34171, GSE87371, GSE31312. The code to calculate the relative proportion of cells in each cell cycle phase for samples based on gene expression profile is available at https://doi.org/10.5281/zenodo.6613589 (the most recent version as well as the archived version referenced in the study).

Abbreviations

DLBCL:

Diffuse large B-cell lymphoma

DEGs:

Differentially expressed genes

IME:

Immune-enriched

CCE:

Cell-cycle-enriched

CHOP:

Cyclophosphamide, doxorubicin, vincristine, and prednisone

RCHOP:

Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone

MCP-counter:

Microenvironment cell populations-counter

EPIC:

Estimating the proportion of immune and cancer cells

LDH:

Lactate dehydrogenase

GCB:

Germinal center B-cell-like

ABC:

Activated B-cell-like

IPI:

International Prognostic Index

GEO:

Gene Expression Omnibus

GSEA:

Gene set enrichment analysis

log2FC:

Log2 fold change

adj.p :

Adjust p value

EMT:

Epithelial to mesenchymal transition

MSigDB:

Molecular Signatures Database

FDR:

False discovery rate

BH:

Benjamin–Hochberg

AUC:

Area under the curve

ROC:

Receiver operating characteristic analysis

SVM:

Support Vector Machine

DT:

Decision Tree

NB:

Naive Bayesian

RF:

Random Forest

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Acknowledgements

We acknowledge the National Natural Science Foundation of China (Grant Number: 62076015) for financial support.

Funding

This work was supported by the National Natural Science Foundation of China (Grant number: 62076015).

Author information

Author notes

  1. Xujie Zhuang and Bo Liu are the first authors.

Authors and Affiliations

  1. School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Xujie Zhuang, Junqi Long, Huina Wang, Xinchan Ji, Jinmeng Li, Nian Zhu, Lujia Li & Yuhaoran Chen

  2. School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand

    Bo Liu

  3. Department of Medical Oncology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China

    Jiangyong Yu

  4. Department of Thoracic Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China

    Zhidong Liu

  5. Department of Breast Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China

    Shuangtao Zhao

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  1. Xujie Zhuang
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Contributions

SZ conceived and supervised this study. XZ and BL conducted the major bioinformatics and biostatistics analysis of these data, produced all the figures and tables in this manuscript. JL, HW, JY, XJ, JL, NZ, LL, YC, and ZL assisted with sample collection and manuscript revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shuangtao Zhao.

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Table S1

. Marker gene list of G1, S, and G2M for cell cycle analysis. (XLSX 11 kb)

Table S2

. 1027 DEGs selected by limma analysis between two subgroups from 414 DLBCL samples. (XLSX 117 kb)

Table S3

. Immune infiltration result for 414 DLBCL samples from MCP-counter. (XLSX 52 kb)

Table S4

. Immune infiltration result for 414 DLBCL samples from EPIC. (XLSX 44 kb)

Table S5

. The proportion of cells in G1, S, and G2M cell cycle phases for 414 DLBCL samples. (XLSX 33 kb)

Table S6

. Screening of characteristic genes between IME and CCE groups in 414 DLBCL. (XLSX 16 kb)

Fig. S1

. Consensus clustering for 181 CHOP and 233 RCHOP samples from GSE10846. (a) Consensus clustering matrix of 181 CHOP samples from GSE10846 for k = 2 (cluster 1: n = 71, cluster 2: n = 110). (b) Consensus clustering matrix of 181 CHOP samples from GSE10846 for k = 3 (cluster 1: n = 70, cluster 2: n = 71, cluster 3: n = 40). (c) Consensus clustering CDF of 181 CHOP samples for k = 2 to k = 8. (d) Consensus clustering matrix of 233 RCHOP samples from GSE10846 for k = 2 (cluster 1: n = 105, cluster 2: n = 128). (e) Consensus clustering matrix of 233 RCHOP samples from GSE10846 for k = 3 (cluster 1: n = 79, cluster 2: n = 73, cluster 3: n = 81). (f) Consensus clustering CDF of 233 RCHOP samples for k = 2 to k = 8. (PNG 847 kb)

Fig. S2

. Kaplan–Meier survival analysis of CHOP and RCHOP patients in GSE10846. (a) Kaplan–Meier survival analysis of 181 CHOP samples in cluster 1 and cluster 2. (b) Kaplan–Meier survival analysis of 233 RCHOP samples in cluster 1 and cluster 2. (PNG 408 kb)

Fig. S3

. Screening of key genes using Lasso regression, Random Forest, and Point-biserial correlation. (a) Selection of 45 DEGs in IME using the Lasso regression model via minimum criteria. (b) Selection of 15 DEGs in CCE using the Lasso regression model via minimum criteria. (c) The feature importance of the top 30 key genes from 217 DEGs in IME is indicated by the MeanDecreaseAccuracy value from the Random Forest model. (d) The feature importance of 25 DEGs in CCE was indicated by the MeanDecreaseAccuracy value from the Random Forest model. (e) 41 genes with point-biserial correlation coefficients greater than 0.5 in IME. (f) 6 genes with point-biserial correlation coefficients greater than 0.5 in CCE. (PNG 2450 kb)

Fig. S4

. Differential analysis between IME and CCE in three external validation sets including GSE34171 (a), GSE87371 (b), and GSE31312 (c). (PNG 1095 kb)

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Zhuang, X., Liu, B., Long, J. et al. Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle. Clin Transl Oncol 26, 936–950 (2024). https://doi.org/10.1007/s12094-023-03326-y

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