Prognostic biomarker SGSM1 and its correlation with immune infiltration in gliomas

Li, Junsheng; Jia, Wang; Ding, Yaowei; Zhao, Jizong; Wang, Wen

doi:10.1186/s12885-022-09548-7

Cited by 4 publications

(5 citation statements)

References 41 publications

(36 reference statements)

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“…Nomograms can graphically describe biological information, characteristics, and clinical variables as a statistical prediction model and estimate the individualized risk according to the characteristics of patients and diseases. It is a simple, easy-to-understand, and user-friendly clinical decision-making tool ( 41 ) and was widely used in individualized prognostic evaluation of breast cancer ( 42 ), rectal cancer ( 43 ), prostate cancer ( 44 ), glioma ( 45 ), and lung adenocarcinoma ( 46 ).…”

Section: Discussionmentioning

confidence: 99%

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

Song

et al. 2022

Front. Oncol.

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ObjectivesIn radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT).Materials and methodsWe reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normalization. Spearman correlation coefficient, principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) were used to screen features. The pretreatment traditional radiomics combined with clinical characteristics (before_rad_cil) model and pretreatment deep learning characteristics (before_dl) model were constructed according to the data collected before treatment. The data collected after NAT created the after_rad_cil model and after_dl model. The entire model was jointly constructed by all clinical features, conventional radiomics features, and deep learning features before and after neoadjuvant treatment. Finally, according to the data obtained before and after treatment, the before_nomogram and after_nomogram were constructed.ResultsIn the before_rad_cil model, four traditional radiomics features (“original_shape_flatness,” “wavelet hhl_firer_skewness,” “wavelet hlh_firer_skewness,” and “wavelet lll_glcm_correlation”) and two clinical features (“gender” and “N stage”) were screened out to predict a GPR. The average prediction accuracy (ACC) after modeling with k-nearest neighbor (KNN) was 0.707. In the after_rad_cil model, nine features predictive of GPR were obtained after feature screening, among which seven were traditional radiomics features: “exponential_firer_skewness,” “exponential_glrlm_runentropy,” “log- sigma-5-0-mm-3d_firer_kurtosis,” “logarithm_skewness,” “original_shape_elongation,” “original_shape_brilliance,” and “wavelet llh_glcm_clustershade”; two were clinical features: “after_CRP” and “after lymphocyte percentage.” The ACC after modeling with support vector machine (SVM) was 0.682. The before_dl model and after_dl model were modeled by SVM, and the ACC was 0.629 and 0.603, respectively. After feature screening, the entire model was constructed by multilayer perceptron (MLP), and the ACC of the GPR was the highest, 0.805. The calibration curve showed that the predictions of the GPR by the before_nomogram and after_nomogram were in consensus with the actual GPR.ConclusionCT-based radiomics has a good predictive ability for a GPR in NSCLC patients receiving immunotherapy-based NAT. Among the radiomics features combined with the clinicopathological information model, deep learning feature model, and the entire model, the entire model had the highest prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

Song

et al. 2022

Front. Oncol.

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“…2f, left). The most differentially expressed gene EMP3 (Epithelial membrane protein 3), which has been identified as an tumor suppressor in breast cancer [34], glioma [35] and remains to be elucidated in colon cancer. The characteristic genes in malignant epithelial cell populations were found to be involved in biological processes such as matrix remodeling, cell proliferation and apoptosis (Fig.…”

Section: Transcriptional and Functional Features Of Malignant Cells R...mentioning

confidence: 99%

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer

Xiao,

Yu,

Meng

et al. 2024

Cell Death Dis

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Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

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“…Furthermore, the expression of small G protein signaling modulator 1 (SGSM1) tended to be relatively diminished in low-grade glioma tissues. The downregulation of SGSM1 expression was found to be associated with poor survival time in patients with either low-grade glioma or its subtypes [ 5 ]. Numerous studies have confirmed a significant correlation between sulfatases (SULF) and cancer [ 7 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies have revealed correlations between changes in the expression of specific long non-coding RNAs/genes and the growth/metastasis of cancer [2][3][4][5][6]. For example, Ge et al, found an association between the overexpression of tumor protein p53 inducible protein 13 (TP53I13) in glioma tissues and unfavorable survival outcomes.…”

Section: Introductionmentioning

confidence: 99%

SUMF1 overexpression promotes tumorous cell growth and migration and is correlated with the immune status of patients with glioma

Zhang,

Liu,

Wang

et al. 2024

Aging

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Background: Glioma is a prevalent type of malignant tumor. To date, there is a lack of literature reports that have examined the association between sulfatase modifying factor 1 (SUMF1) and glioma. Methods: The levels of SUMF1 were examined, and their relationships with the diagnosis, prognosis, and immune microenvironment of patients with glioma were investigated. Cox and Lasso regression analysis were employed to construct nomograms and risk models associated with SUMF1. The functions and mechanisms of SUMF1 were explored and verified using gene ontology, cell counting kit-8, wound healing, western blotting, and transwell experiments. Results: SUMF1 expression tended to increase in glioma tissues. SUMF1 overexpression was linked to the diagnosis of cancer, survival events, isocitrate dehydrogenase status, age, and histological subtype and was positively correlated with poor prognosis in patients with glioma. SUMF1 overexpression was an independent risk factor for poor prognosis. SUMF1-related nomograms and high-risk scores could predict the outcome of patients with glioma. SUMF1 co-expressed genes were involved in cytokine, T-cell activation, and lymphocyte proliferation. Inhibiting the expression of SUMF1 could deter the proliferation, migration, and invasion of glioma cells through epithelial mesenchymal transition. SUMF1 overexpression was significantly associated with the stromal score, immune cells (such as macrophages, neutrophils, activated dendritic cells), estimate score, immune score, and the expression of the programmed cell death 1, cytotoxic T-lymphocyte associated protein 4, CD79A and other immune cell marker. Conclusion: SUMF1 overexpression was found to be correlated with adverse prognosis, cancer detection, and immune status in patients with glioma. Inhibiting the expression of SUMF1 was observed to deter the proliferation, migration, and invasion of cancer cells. The nomograms and risk models associated with SUMF1 could predict the prognosis of patients with glioma.

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Prognostic biomarker SGSM1 and its correlation with immune infiltration in gliomas

Cited by 4 publications

References 41 publications

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer

SUMF1 overexpression promotes tumorous cell growth and migration and is correlated with the immune status of patients with glioma

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