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Abstract Molecular profiles of mesenchymal stem cells (MSCs) are needed to standardize the composition and effectiveness of MSC therapeutics. This study employs RNA sequencing to identify genes to be used in concert with CD264 as a molecular profile of aging MSCs at a clinically relevant culture passage. CD264⁻and CD264⁺populations were isolated by fluorescence-activated cell sorting from passage 4 MSC cultures. CD264⁺MSCs exhibited an aging phenotype relative to their CD264⁻counterpart. Donor-matched CD264^−/+mRNA samples from 5 donors were subjected to pair-ended, next-generation sequencing. An independent set of 5 donor MSCs was used to validate differential expression of select genes with quantitative reverse transcription PCR. Pairwise differential expression analysis identified 2,322 downregulated genes and 2,695 upregulated genes in CD264⁺MSCs relative to donor-matched CD264⁻MSCs with a Benjamini–Hochberg adjustedp-value (BHp_adj) < 0.1. Nearly 25% of these genes were unique to CD264^−/+MSCs and not differentially expressed at a significance level of BHp_adj < 0.1 in previous RNA sequencing studies of early- vs. late-passage MSCs. Least Absolute Shrinkage and Selection Operator regression identified microtubule-associated protein 1A (MAP1A) and pituitary tumor-transforming gene 1 (PTTG1) as predictive genes of CD264⁺MSCs. CombinedMAP1AandPTTG1expression correctly classified CD264 status of MSC samples with an accuracy of 100%. Differential expression and predictive ability ofMAP1AandPTTG1compared favorably with that of existing senescence markers expressed in early passage CD264^−/+MSCs. This study provides the first linkage ofMAP1Ato CD264, aging and senescence. Our findings have application as quality metrics to standardize the composition of MSC therapies and as molecular targets to slow/reverse cellular aging. 

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.