Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer

Chen, Tingting; Hua, Wei; Xu, Bing; Chen, Hui; Xie, Minhao; Sun, Xinchen; Ge, Xiaolin

doi:10.3934/mbe.2021228

Cited by 4 publications

(2 citation statements)

References 65 publications

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“…However, for the supervised methods to be accurate in their computations, the signature matrix and the bulk data must be collected by the same expression platform, as Chen et al mention in their article [ 29 ]. In addition, CIBERSORT is one of the most widely used deconvolution methods nowadays [ 30 , 31 , 32 , 33 , 34 ], providing good results in predicting cell abundance. Nevertheless, LINSEED was more robust in the presence of noise in the data (i.e., in the presence of cancer cells).…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

Alonso-Moreda

Berral-González

Rosa

et al. 2023

IJMS

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In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

Alonso-Moreda

Berral-González

Rosa

et al. 2023

IJMS

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“…With the large quantity of publicly available bulk transcriptomic data, several recent studies have utilized computational deconvolution efforts to explore immune cell subtypes [8][9][10][11][12][13]. However, despite describing similar methodologies for deconvolution, these studies derived disparate conclusions regarding the prognostic associations of the immune infiltrate, overall [8][9][10], as well as specific immune cell subtypes [8,11,12].…”

Section: Introductionmentioning

confidence: 99%

Immune Cell Deconvolution Reveals Possible Association of γδ T Cells with Poor Survival in Head and Neck Squamous Cell Carcinoma

Parikh,

Li,

Mazul

et al. 2023

Cancers

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(1) Background: The role of rare immune cell subtypes in many solid tumors, chief among them head and neck squamous cell carcinoma (HNSCC), has not been well defined. The objective of this study was to assess the association between proportions of common and rare immune cell subtypes and survival outcomes in HNSCC. (2) Methods: In this cohort study, we utilized a deconvolution approach based on the CIBERSORT algorithm and the LM22 signature matrix to infer proportions of immune cell subtypes from 517 patients with untreated HPV-negative HNSCC from The Cancer Genome Atlas. We performed univariate and multivariable survival analysis, integrating immune cell proportions with clinical, pathologic, and genomic data. (3) Results: We reliably deconvolved 22 immune cell subtypes in most patients and found that the most common immune cell types were M0 macrophages, M2 macrophages, and memory resting CD4 T cells. In the multivariable analysis, we identified advanced N stage and the presence of γδ T cells as independently predictive of poorer survival. (4) Conclusions: We uncovered that γδ T cells in the tumor microenvironment were a negative predictor of survival among patients with untreated HNSCC. Our findings underscore the need to better understand the role of γδ T cells in HNSCC, including potential pro-tumorigenic mechanisms, and whether their presence may predict the need for alternative therapy approaches.

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Measuring robustness in rank aggregation based on the error-effectiveness curve

Xiao

Zhu

Chen

et al. 2023

Information Processing & Management

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Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer

Cited by 4 publications

References 65 publications

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

Immune Cell Deconvolution Reveals Possible Association of γδ T Cells with Poor Survival in Head and Neck Squamous Cell Carcinoma

Measuring robustness in rank aggregation based on the error-effectiveness curve

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