Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

Shu, Long; Tang, Jun; Liu, Shuang; Tao, Yongguang

doi:10.1007/s13402-023-00883-w

Cited by 8 publications

(1 citation statement)

References 84 publications

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“…26,27 NUCB2 functions as a pivotal regulatory gene in tumorinfiltrating plasma cells and exhibits a bidirectional role in tumor progression, underscoring its complex impact in oncogenesis. 28 In human immunodeficiency diseases and tumors, the overactivation of Rac2 leads to an increase in PI3K activity, and affects the development and function of NK cells. 29 LYL1, functioning as a transcription factor, mediates the critical transition from lymphoid progenitor cells to NK progenitor cells, playing a key role in NK cell development.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Mei,

Gong,

Wang

et al. 2024

BLCTT

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Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Mei,

Gong,

Wang

et al. 2024

BLCTT

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Unraveling the role of low‐density lipoprotein‐related genes in lung adenocarcinoma: Insights into tumor microenvironment and clinical prognosis

Zhang,

Wu,

Wang

et al. 2024

Environmental Toxicology

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BackgroundThe hypothesized link between low‐density lipoprotein (LDL) and oncogenesis has garnered significant interest, yet its explicit impact on lung adenocarcinoma (LUAD) remains to be elucidated. This investigation aims to demystify the function of LDL‐related genes (LRGs) within LUAD, endeavoring to shed light on the complex interplay between LDL and carcinogenesis.MethodsLeveraging single‐cell transcriptomics, we examined the role of LRGs within the tumor microenvironment (TME). The expression patterns of LRGs across diverse cellular phenotypes were delineated using an array of computational methodologies, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore. CellChat facilitated the exploration of distinct cellular interactions within LDL_low and LDL_high groups. The findmarker utility, coupled with Pearson correlation analysis, facilitated the identification of pivotal genes correlated with LDL indices. An integrative approach to transcriptomic data analysis was adopted, utilizing a machine learning framework to devise an LDL‐associated signature (LAS). This enabled the delineation of genomic disparities, pathway enrichments, immune cell dynamics, and pharmacological sensitivities between LAS stratifications.ResultsEnhanced cellular crosstalk was observed in the LDL_high group, with the CoxBoost+Ridge algorithm achieving the apex c‐index for LAS formulation. Benchmarking against 144 extant LUAD models underscored the superior prognostic acuity of LAS. Elevated LAS indices were synonymous with adverse outcomes, diminished immune surveillance, and an upsurge in pathways conducive to neoplastic proliferation. Notably, a pronounced susceptibility to paclitaxel and gemcitabine was discerned within the high‐LAS cohort, delineating prospective therapeutic corridors.ConclusionThis study elucidates the significance of LRGs within the TME and introduces an LAS for prognostication in LUAD patients. Our findings accentuate putative therapeutic targets and elucidate the clinical ramifications of LAS deployment.

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The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

Li,

Sun,

et al. 2024

J Gastrointest Canc

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Background The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. Methods We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared. Results The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. Conclusions This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

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Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

Cited by 8 publications

References 84 publications

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Unraveling the role of low‐density lipoprotein‐related genes in lung adenocarcinoma: Insights into tumor microenvironment and clinical prognosis

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

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