Protective Prognostic Biomarkers Negatively Correlated with Macrophage M2 Infiltration in Low-Grade Glioma

Abstract

Tumor-associated Macrophages (TAMs) play a vital role in the progression of glioma. Macrophage M2 has been confirmed to promote immunosuppression and proliferation of low-grade glioma (LGG). Here, we searched for genes negatively correlated with Macrophages M2 by bioinformatical methods and investigated their protective ability for prognosis. LGG and adjacent normal samples were screened out in TCGA and three GEO datasets. 326 overlapped differentially expressed genes were calculated, and their biological functions were investigated by Go and KEGG analyses. Macrophage M2 accounted for the highest proportion among all 22 immune cells by CIBERSORT deconvolution algorithm. The proportion of Macrophage M2 in LGG was also higher than that in normal tissue according to several deconvolution algorithms. 43 genes in the blue module negatively correlated with Macrophage M2 infiltration were identified by weighted gene coexpression network analysis (WGCNA). Through immune infiltration and correlation analysis, FGFBP3, VAX2, and SHD were selected and they were enriched in G protein-coupled receptors’ signaling regulation and cytokine receptor interaction. They could prolong the overall and disease-free survival time. Univariate and multivariate Cox regression analyses were applied to evaluate prognosis prediction ability. Interestingly, FGFBP3 and AHD were independent prognostic predictors. A nomogram was drawn, and its 1-year, 3-year, and 5-year survival prognostic value was verified by ROC curves and calibration plots. In conclusion, FGFBP3, VAX2, and SHD were protective prognostic biomarkers against Macrophage M2 infiltration in low-grade glioma. The FGFBP3 and SHD were independent factors to effectively predict long-term survival probability.

Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer

“…The results of numerous epidemiological and bioinformatic studies convincingly show the connection of the STON1 gene (its expression) with BC (on a model of BC-specific cell lines (MCF-7)) [97][98][99] as well as the same with other various oncological diseases: pancreatic duct adenocarcinoma [100], lung cancer [101][102][103], colorectal cancer [104][105][106], papillary renal cell carcinoma [107], colon carcinoma (liver metastases) [108], basal cell skin cancer [109], clear cell kidney carcinoma [96], bladder carcinoma [110,111], and glioma [112], including in the GWAS data (lung cancer [103]). It is important to emphasize that, according to our in silico data (obtained from the regBase-CAN database), the BC-associated locus rs201414717 PPP1R21 considered in this work and the seven SNPs strongly linked to them are the most likely drivers of the occurrence of tumors ("likely cancer driver").…”

A Sexual Bias in mitochondrial protein-coding gene expression across different tissues and the prognostic value in multiple cancers

“…Similarly, in DLBC the 13-component gene signature show a positive correlation for six cells including neutrophils. In agreement, M2 macrophage has been confirmed to promote immunosuppression and proliferation of LGG; on the other hand, T cells and natural killer (NK) cells can be associated with poorer OS and DSS in LGG, both in nonmitochondrial gene signature (Ye et al, 2021;Zhu et al, 2022). Concerning DLBC prognosis, the tumor-associated neutrophils show a poor prognostic, in the same way, Manfroi and colleagues demonstrated that in DLBC patients with a higher ratio of neutrophils had a worse prognosis, both in non-mitochondrial gene signature (Manfroi et al, 2018;Mu et al, 2018).…”

A novel, effective machine learning-based RNA editing profile for predicting the prognosis of lower-grade gliomas

“…LGGs are slow-growing tumors with an ineluctable propensity for malignancy [ 19 ]. The biological characteristics of LGG are notably distinct from those of GBM, and it is regarded as a therapeutic window before the malignant change.…”