Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

Zhang, Xin; Zhuge, Jinke; Liu, Jinhui; Xia, Zhijia; Wang, Huixiong; Gao, Qiang; Jiang, Hao; Qu, Yanyu; Fan, Lei; Ma, Jun; Tan, Chunyan; Luo, Wei; Luo, Yong

doi:10.3389/fimmu.2023.1153423

Cited by 31 publications

(31 citation statements)

References 74 publications

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“…These include innovations like single‐cell sequencing 35 , 36 , 37 , 38 , 39 , 40 and machine learning applications. 41 , 42 Such advancements are critical not only in reducing the side effects associated with chemotherapy and radiotherapy but also in enhancing the precision of molecular targeting methods, thereby contributing to improved patient survival outcomes.…”

Section: Discussionmentioning

confidence: 99%

Integrating single‐cell and multi‐omic approaches reveals Euphorbiae Humifusae Herba‐dependent mitochondrial dysfunction in non‐small‐cell lung cancer

Zhang,

Zhao,

et al. 2024

J Cellular Molecular Medi

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Euphorbiae Humifusae Herba (EHH) is a pivotal therapeutic agent with diverse pharmacological effects. However, a substantial gap exists in understanding its pharmacological properties and anti‐tumour mechanisms. This study aimed to address this gap by exploring EHH's pharmacological properties, identifying NSCLC therapy‐associated protein targets, and elucidating how EHH induces mitochondrial disruption in NSCLC cells, offering insights into novel NSCLC treatment strategies. String database was utilized to explore protein–protein interactions. Subsequently, single‐cell analysis and multi‐omics further unveiled the impact of EHH‐targeted genes on the immune microenvironment of NSCLC, as well as their influence on immunotherapeutic responses. Finally, both in vivo and in vitro experiments elucidated the anti‐tumour mechanisms of EHH, specifically through the assessment of mitochondrial ROS levels and alterations in mitochondrial membrane potential. EHH exerts its influence through engagement with a cluster of 10 genes, including the apoptotic gene CASP3. This regulatory impact on the immune milieu within NSCLC holds promise as an indicator for predicting responses to immunotherapy. Besides, EHH demonstrated the capability to induce mitochondrial ROS generation and perturbations in mitochondrial membrane potential in NSCLC cells, ultimately leading to mitochondrial dysfunction and consequent apoptosis of tumour cells. EHH induces mitochondrial disruption in NSCLC cells, leading to cell apoptosis to inhibit the progress of NSCLC.

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Section: Discussionmentioning

confidence: 99%

Integrating single‐cell and multi‐omic approaches reveals Euphorbiae Humifusae Herba‐dependent mitochondrial dysfunction in non‐small‐cell lung cancer

Zhang,

Zhao,

et al. 2024

J Cellular Molecular Medi

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“…To generate LGALS3 knockdown, small interfering RNAs (siRNAs) were used ( 34 ). The siRNA sequences for LGALS3 are listed in Supplementary Table S1 .…”

Section: Methodsmentioning

confidence: 99%

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Zhang

Pei

et al. 2023

Front. Endocrinol.

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BackgroundGlutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).MethodsWe used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.ResultsWe identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model’s performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.ConclusionOur study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

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“…Next, the LASSO regression analysis was employed to minimize the overfitting risk of the prognostic model. [22] Based on the result of multivariable COX regression analysis, the signature was calculated as follows: falseRisk score=∑i=1nβi×Expi, where falseβi is the risk coefficient, and falseExpi represents the mRNA expression of each selected gene. The median risk score was considered the cutoff to classify patients into high- and low-risk groups.…”

Section: Methodsmentioning

confidence: 99%

Single-cell and bulk RNA sequencing reveal cancer-associated fibroblast heterogeneity and a prognostic signature in prostate cancer

Liu

Wang

et al. 2023

Medicine

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Cancer-associated fibroblasts (CAFs), the central players in the tumor microenvironment (TME), can promote tumor progression and metastasis via various functions. However, the properties of CAFs in prostate cancer (PCa) have not been fully assessed. Therefore, we aimed to examine the CAF characteristics in PCa and construct a CAF-derived signature to predict PCa prognosis. CAFs were identified using single-cell RNA sequencing (scRNA-seq) data from 3 studies. We performed the FindAllMarkers function to extract CAF marker genes and constructed a signature to predict the biochemical relapse-free survival (bRFS) of PCa in the Cancer Genome Atlas (TCGA) cohort. Subsequently, different algorithms were applied to reveal the differences of the TME, immune infiltration, treatment responses in the high- and low-risk groups. Additionally, the CAF heterogeneity was assessed in PCa, which were confirmed by the functional enrichment analysis, gene set enrichment analysis (GSEA), and AUCell method. The scRNA-seq analysis identified a CAF cluster with 783 cells and determined 183 CAF marker genes. Cell-cell communication revealed extensive interactions between fibroblasts and immune cells. A CAF-related prognostic model, containing 7 genes (ASPN, AEBP1, ALDH1A1, BGN, COL1A1, PAGE4 and RASD1), was developed to predict bRFS and validated by 4 independent bulk RNA-seq cohorts. Moreover, the high-risk group of the signature score connected with an immunosuppressive TME, such as a higher level of M2 macrophages and lower levels of plasma cells and CD8+ T cells, and a reduced reaction rate for immunotherapy compared with low-risk group. After re-clustering CAFs via unsupervised clustering, we revealed 3 biologically distinct CAF subsets, namely myofibroblast-like CAFs (myCAFs), immune and inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs). In conclusion, the CAF-derived signature, the first of its kind, can effectively predict PCa prognosis and serve as an indicator for immunotherapy. Furthermore, our study identified 3 CAF subpopulations with distinct functions in PCa.

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Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

Cited by 31 publications

References 74 publications

Integrating single‐cell and multi‐omic approaches reveals Euphorbiae Humifusae Herba‐dependent mitochondrial dysfunction in non‐small‐cell lung cancer

Integrating single‐cell and multi‐omic approaches reveals Euphorbiae Humifusae Herba‐dependent mitochondrial dysfunction in non‐small‐cell lung cancer

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Single-cell and bulk RNA sequencing reveal cancer-associated fibroblast heterogeneity and a prognostic signature in prostate cancer

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