Classification of lung adenocarcinoma based on stemness scores in bulk and single cell transcriptomes

Liu, Qian; Lei, Juan; Zhang, Xiaobo; Wang, Qianqian

doi:10.1016/j.csbj.2022.04.004

Cited by 10 publications

(7 citation statements)

References 44 publications

(70 reference statements)

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“…Although some papers acknowledged this issue for a subset of the analyzed signatures [14], there is no general recognition. We hypothesize here that this phenomenon could affect expression-based subtyping schemes in many other indications, in which the existence of mesenchymal/stemness phenotypes has been postulated [38,39].…”

Section: Discussionmentioning

confidence: 95%

Stromal signals dominate gene expression signature scores that aim to describe cancer-intrinsic stemness or mesenchymality characteristics

Kreis,

Aybey,

Geist

et al. 2023

Preprint

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PurposeEpithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory ability, a crucial aspect of tumor metastasis that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, and mesenchymality (EMT-related) characteristics of tumors based on bulk tumor expression profiling. However, recent studies have suggested that non-cancerous cells in the tumor micro- or macroenvironment heavily influence individual signature profiles.Experimental DesignWe analyzed scores of 11 published and frequently referenced gene expression signatures in bulk, single cell, and pseudo bulk expression data across multiple cancer types.ResultsOur study strengthens and extends the influence of non-cancerous cells on signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. The cell type composition, especially the amount of tumor cells, of a tumor sample frequently dominates EMT-related signature scores. Additionally, our analyses revealed that stromal cells, most often fibroblasts, are the main drivers of the EMT-related signature scores.ConclusionsWe call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of “stemness subtypes” often result from low tumor cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymality/EMT characteristics.

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Section: Discussionmentioning

confidence: 95%

Stromal signals dominate gene expression signature scores that aim to describe cancer-intrinsic stemness or mesenchymality characteristics

Kreis,

Aybey,

Geist

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…C2 also exhibited the most common TMB and CNV frequencies, indicating that the C2 genome was the least stable. According to a previous study, high stemness is associated with a tumor progression phenotype, poor prognosis, and genomic instability in LUAD ( 30 ). In this study, C2 showed the highest levels of mRNAsi and mDNAsi, which has been confirmed as the worst prognostic and genomically unstable phenotype.…”

Section: Discussionmentioning

confidence: 96%

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes

Yu¹,

Huang²,

Lin³

et al. 2023

J Thorac Dis

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Background DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup. Methods The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed. Results A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB). Conclusions In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

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“…Yang et al (2022) used a dataset of gene expression profiles from 515 tumor samples and 59 normal tissues and split the dataset into two significantly different clusters; they further showed that using age, gender, pathological stages, and risk score as predictors of LUAD increased the prediction accuracy measures [46]. Liu, Lei, Zhang, and Wang (2022) used cluster analysis on enrichment scores of 12 stemness signatures to identify three LUAD subtypes, St-H, St-M and St-L for six different datasets [47].…”

Section: Methodsmentioning

confidence: 99%

ML Classification of Cancer Types Using High Dimensional Gene Expression Microarray Data

Mukhopadhyay,

Phanord,

Dalpatadu

et al. 2024

Preprint

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Machine Learning classifiers are used to classify a very wide dataset containing gene ex-pression microarray data of patients with five types of cancer (breast cancer, kidney cancer, Colon cancer, lung cancer and prostate cancer). Since the dataset was very wide with a large number of columns, the code yielded stack overflow errors, and we resorted to Principal Components Analysis (PCA) for dimensionality reduction, and principal component scores of the raw data for classification. PCA was run using a fast algorithm which is able to compute PC scores for very large datasets. High classification accuracies are obtained using just the first two principal component scores. Machine Learning (ML) classifiers Linear Discriminant Analysis (LDA) & Random Forest (RF) methods were utilized where the latter provided with higher accuracy than the former. The results of this article should be helpful to researchers who are dealing with large number of genes in microarray data.

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Classification of lung adenocarcinoma based on stemness scores in bulk and single cell transcriptomes

Cited by 10 publications

References 44 publications

Stromal signals dominate gene expression signature scores that aim to describe cancer-intrinsic stemness or mesenchymality characteristics

Stromal signals dominate gene expression signature scores that aim to describe cancer-intrinsic stemness or mesenchymality characteristics

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes

ML Classification of Cancer Types Using High Dimensional Gene Expression Microarray Data

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