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Objective: This study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model. Methods: From The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model. Results: Multivariable cox analysis (HR = 1.488, 95 % CI 1.012–2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008–2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P

Objective: An mRNA expression-based stemness index (mRNAsi) has been developed to characterize cancer stemness. However, the predictive value of mRNAsi-based signature in therapeutic resistance and immunotherapy in thyroid cancer (THCA) remains unclarified. This study evaluated and validated the role of mRNAsi in drug sensitivity, its relationship between mRNAsi and THCA clinical features and immunity based on bioinformatics. Methods: Based on transcriptome data of THCA patients from the Tumor Genome Atlas Project (TCGA) database, and expression data of multifunctional stem cell samples from the Progenitor Cell Biology Consortium (PCBC) databases, mRNAsi was calculated by the '' one class logistic regression (OCLR)'' method, Molecular subtypes of TCGA-THCA samples were identified with mRNAsi-related genes using ConsensusClusterPlus method. The gene mutation, clinical characteristics, immune characteristics, TIDE and drug sensitivity were compared among molecular subtypes. A prognostic model was designed with Lasso cox method. Modulation of malignant phenotype of THCA cell lines by model characterization genes is validated by CCK-8, flow cytometry. DNA methylation disorder in promoter region was analyzed between risk groups. The model was validated for survival in the internal Test dataset, while TCGA pan-cancer and immunotherapy datasets were further employed to validate the performance of this model. Results: We obtained a total of 78 stem cell samples, each containing the expression profile of 8087 mRNA genes. Based on mRNAsi, THCA was divided into 3 subtypes. Subtype C2 had the poorest prognosis and highest immune score, while subtype C3 had the best prognosis, lowest mRANsi and highest TIDE score. Patients in subtype C2 showed higher sensitivity to Cisplatin, Erlotinib, Paclitaxel, and Lapatinib. The prognostic signature was generated using 5 mRNAsi-related genes, which could predict prognosis for THCA. qRT-PCR results showed that the expression of 5 genes were various in Hth7 and KTC-1 cells, and inhibition CELSR3 expression increased percentage of apoptosis in Hth7 and KTC-1 cells. mRNAsi related DNA methylation sites were mainly enriched in tumor related pathways. Good performance of this model was validated in Test dataset, pan-cancer and immunotherapy datasets. Conclusion: This study identified three subtypes for classification and developed a prognostic model with mRNAsi-related genes, which provided great potential for prognosis and immunotherapy prediction.

Objective: Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. Methods: LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. Results: A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. Conclusion: Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.

Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown. Methods: Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC. Results: We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients. Conclusions: These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.

Background: Cancer stem cells (CSCs) induces tumor metastasis and recurrence. However, the role of CSCs in molding the tumor immune microenvironment (TIME) is largely inexplicit. This study aimed to comprehensively characterize the stemness of esophageal cancer (EC) and correlate the stemness patterns with TIME., Methods: A trained stemness index model was used to score EC patients based on the one-class logistic regression (OCLR) machine-learning algorithm. Gene expression-based stemness index (mRNAsi) and DNA methylation-based stemness index (mDNAsi) were calculated for integrative analyses of EC stemness in the training cohort (n = 182) and validation cohort (n = 179). Intrinsic stemness patterns were estimated to determine its association with clinical features, biological pathways, prognosis, and potential inhibitors. Additionally, the dynamic interplay between EC stemness and TIME was integrally characterized., Results: Analyses of EC stemness and clinical characteristics indicated that higher-stage and metastatic tumors featured more dedifferentiated phenotypically. Univariate and multivariate Cox regression analyses revealed that mRNAsi was significantly associated with overall survival (OS) of EC patients, whereas no relationship was observed between mDNAsi and OS. Notably, prolonged OS was observed with esophageal squamous cell carcinoma (ESCC) in low versus high mRNAsi groups, whereas the OS was equivalent between the two groups for esophageal adenocarcinoma (ESAD). The mRNAsi may thus recapitulate prognostic molecular subgroups of EC. The prognostic model comprising 14 stemness signatures was constructed using combined Cox and Lasso regression analyses which effectively distinguished individual survival of ESCC in two cohorts. Nevertheless, no significant differences in OS was observed when the same prognostic model of ESCC was applied to ESAD. Gene Set Enrichment Analysis (GSEA) of selected stemness signatures indicated that ESCC stemness is involved in immune-related pathways. Furthermore, ESCC stemness and stemness-related signatures were associated with tumor-infiltrating immune cells, immunoscore, and PD-L1 expression. Compounds specific to the selected stemness signatures were detected using the CMap database., Conclusion: This study determined integrated characteristics of EC stemness. The identified mRNAsi-based signatures conferred with the predictive ability of personalized ESCC prognosis and highlighted the potential targets for CSC-mediated immunotherapy. Analyses of the interface between ESCC stemness and TIME may help in predicting the efficacy of CSC-specific immunotherapy and provide insight into combinatorial therapy by targeting ESCC stem cells and TIME., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Aims: Cancer Stem Cells (CSCs) refers to heterogeneous tumor cells retaining the abilities of self-renewal and differentiation. This study used mRNAsi, which is an index to describe the similarity between tumor cells and CSCs, to define genes involved in endometrial carcinoma., Materials and Methods: The mRNA expression profiles of 552 tumor samples and 23 non-tumor samples were calculated for differentially expressed genes. WGCNA was utilized to construct gene co-expression networks and classify screened genes into different modules. Univariate and multivariate Cox regression models were performed to identify and construct the prognostic model. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, and nomogram were used to assess the prognostic capacity of the six-gene signature. The screened genes were further validated by GEO (GSE17025) and qRT-PCR in EC tissues., Key Findings: 2573 upregulated and 1890 downregulated genes were identified. A total of 35 genes in the turquoise module were identified as key genes. With multivariate analysis, six genes (DEPDC1, FAM83D, NCAPH, SPC25, TPX2, and TTK) up-regulated in endometrial carcinoma were identified, and their higher expression was associated with a higher stage/age/grade. Moreover, ROC and Kaplan-Meier plots indicated these genes had a high prognostic value for EC. A nomogram was constructed for clinical use. In addition, we explored the pathogenesis involving six genes. The results showed that these genes may become pathogenic as their copy numbers changes and methylation level reduces. Finally, GSEA revealed these genes had a close association with cell cycle, etc. SIGNIFICANCE: These findings may provide new insights into the treatment of diseases., Competing Interests: Declaration of competing interest The authors confirm that there are no conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)