Identification of potential biomarkers and their correlation with immune infiltration cells in schizophrenia using combinative bioinformatics strategy

Li, Zhijun; Li, Xinwei; Jin, Mengdi; Liu, Yang; He, Yingkun; Jia, Ningning; Cui, Xueling; Liu, Yane; Hu, Guoyan; Yu, Qiong

doi:10.1016/j.psychres.2022.114658

Cited by 26 publications

(14 citation statements)

References 73 publications

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“…For example, CMC clusters associated with SCZ (Cluster 8) compared to general affective disorders (AFF) (Cluster 12) revealed distinct convergent networks with unique node genes. Of note, the central protein-coding node gene of the cluster 12 network is ABCG2— down-regulated in a subset of SCZ cases with a neuroimmune molecular subtype (SCZ Type II) 57,58 , a biomarker associated with increased negative symptoms 59 , and associated with SCZ treatment resistance 60 .…”

Section: Resultsmentioning

confidence: 99%

Convergent impact of schizophrenia risk genes

Townsley

Deans

et al. 2022

Preprint

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Schizophrenia is a highly heritable psychiatric disorder with a complex genetic risk architecture that reflects the additive impact of hundreds of risk variants. While many schizophrenia-associated risk variants are thought to regulate the expression of target genes in a cell-type-specific manner, the mechanisms by which the effect of these myriad variants combine to contribute to risk remain unclear. Here we apply a CRISPR-based approach to evaluate in parallel twelve schizophrenia eGenes (that encompass common variation) in human glutamatergic neurons. Querying the shared neuronal impacts across risk genes uncovers a convergent effect concentrated on pathways of brain development and synaptic signaling. Our analyses reveal shared and divergent downstream effects of these twelve genes, independent of their previously annotated biological roles. General convergence of gene expression increases with increasing polygenicity, while the specificity of convergence increases between functionally similar genes. Convergent networks show brain-region and developmental period-specific enrichments, as well as disorder-specific enrichments for both rare and common variant target genes across schizophrenia, bipolar disorder, autism spectrum disorder, and intellectual disability. These gene targets are drug-able and potentially represent novel points of therapeutic intervention. Convergent signatures are also resolved in the post-mortem brain. Overall, convergence suggests a model to explain how non-additive interactions arise between risk genes and may explain cross-disorder pleiotropy of genetic risk for psychiatric disorders.

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Section: Resultsmentioning

confidence: 99%

Convergent impact of schizophrenia risk genes

Townsley

Deans

et al. 2022

Preprint

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“…90 A study showed that the expression of ALDH1L1 was associated with infiltrating immune cells. 91 Through a cluster model based on ALDH1L1, prediction can be made regarding immune infiltration and drug sensitivities to immune checkpoint inhibitors in clear cell renal cell carcinoma. 92…”

Section: Aldehyde Dehydrogenase 1 Family Member L1mentioning

confidence: 99%

“…Aldehyde dehydrogenase 1 family member L1 (ALDH1L1) catalyses the decomposition of 10‐f‐THF into carbon dioxide and NADPH, which are present in the cytoplasm 90 . A study showed that the expression of ALDH1L1 was associated with infiltrating immune cells 91 . Through a cluster model based on ALDH1L1, prediction can be made regarding immune infiltration and drug sensitivities to immune checkpoint inhibitors in clear cell renal cell carcinoma 92 …”

Section: Application Of 1c Metabolism In Cancer Immunotherapymentioning

confidence: 99%

Targeting one‐carbon metabolism for cancer immunotherapy

Ren,

Wang,

Zheng

et al. 2024

Clinical & Translational Med

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BackgroundOne‐carbon (1C) metabolism is a metabolic network that plays essential roles in biological reactions. In 1C metabolism, a series of nutrients are used to fuel metabolic pathways, including nucleotide metabolism, amino acid metabolism, cellular redox defence and epigenetic maintenance. At present, 1C metabolism is considered the hallmark of cancer. The 1C units obtained from the metabolic pathways increase the proliferation rate of cancer cells. In addition, anticancer drugs, such as methotrexate, which target 1C metabolism, have long been used in the clinic. In terms of immunotherapy, 1C metabolism has been used to explore biomarkers connected with immunotherapy response and immune‐related adverse events in patients.MethodsWe collected numerous literatures to explain the roles of one‐carbon metabolism in cancer immunotherapy.ResultsIn this review, we focus on the important pathways in 1C metabolism and the function of 1C metabolism enzymes in cancer immunotherapy. Then, we summarise the inhibitors acting on 1C metabolism and their potential application on cancer immunotherapy. Finally, we provide a viewpoint and conclusion regarding the opportunities and challenges of targeting 1C metabolism for cancer immunotherapy in clinical practicability in the future.ConclusionTargeting one‐carbon metabolism is useful for cancer immunotherapy.

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“…Normalized gene expression data were used to evaluate the relative proportions of 22 types of infiltrating immune cells using the CIBERSORT algorithm [18]. Gene expression datasets were prepared using standard annotation files and data uploaded to the CIBERSORT Web portal (https://cibersort.stanford.edu), with the algorithm run using the default signature matrix at 1,000 permutations, which has been applied in many studies [19][20][21][22]. Here, we first divided SCZ patients into "high" and "low" hypoxia groups, which were determined by the cut-off value of median hypoxia score based on the expression of 12 hypoxia-related DEGs.…”

Section: Evaluation Of Scz-infiltrating Immune Cellsmentioning

confidence: 99%

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression

Sun

et al. 2023

Hum Hered

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Introduction: Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients. Methods: GSE17612, GSE21935 and GSE53987 datasets, consist of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis (ssGSEA) using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patients. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores, and patients in low-score groups if their hypoxia score were in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients. Results: In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients. Conclusion: These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.

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Identification of potential biomarkers and their correlation with immune infiltration cells in schizophrenia using combinative bioinformatics strategy

Cited by 26 publications

References 73 publications

Convergent impact of schizophrenia risk genes

Convergent impact of schizophrenia risk genes

Targeting one‐carbon metabolism for cancer immunotherapy

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression

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