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Macrophages play a crucial role in tumor initiation and progression, while macrophage-associated gene signature in colorectal cancer (CRC) patients has not been investigated. Our study aimed to identify macrophage-related molecular subgroups and develop a macrophage-related risk model to predict CRC prognosis. the mRNA expression profile and clinical information of CRC patients were obtained from TCGA and GEO databases. CRC patients from TCGA were divided into high and low macrophage subgroups based on the median macrophage score. The ESTIMATE and CIBERSORT algorithms were used to assess immune cell infiltration between subgroups. GSVA and GSEA analyses were performed to investigate differences in enriched pathways between subgroups. Univariate and LASSO Cox regression were used to build a prognostic risk model, which was further validated in the GSE39582 dataset. a high macrophage score subgroup was associated with poor prognosis, highly activated immune-related pathways and an immune-active microenvironment. a total of 547 differentially expressed macrophage-related genes (DEMRGs) were identified, among which seven genes (including RIMKLB, UST, PCOLCE2, ZNF829, TMEM59L, CILP2, DTNA) were identified by COX regression analyses and used to build a risk score model. the risk model shows good predictive and diagnostic values for CRC patients in both TCGA and GSE39852 datasets. Furthermore, multivariate cox regression analysis showed that the risk score was an independent risk factor for overall survival in CRC patients. Our findings provided a novel insight into macrophage heterogeneity and its immunological role in CRC. this risk score model may serve as an effective prognostic tool and contribute to personalised clinical management of CRC patients. [ABSTRACT FROM AUTHOR]

As the most abundant infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) are pivotal in tumor development and treatment. The present investigation endeavors to explore the potential of M1 macrophage-related genes (MRGs) as biomarkers for assessing risk in individuals with osteosarcoma. RNA-sequence data and clinical data were derived from TCGA and GEO databases. The CIBERSORT method was utilized to discern subtypes of tumor-infiltrating immune cells. Identification of MRGs was achieved through Pearson correlation analysis. A prognostic risk model for MRGs was developed using Cox and LASSO regression analyses. A tripartite gene signature comprising CD37, GABRD, and ARHGAP25 was an independent prognostic indicator and was employed to develop a risk score model. The internal and external validation cohort confirmed the results. The area under the ROC curve (AUC) was determined for survival periods of 1 year, three years, and five years, yielding values of 0.746, 0.839, and 0.850, respectively. The C-index of the risk score was found to be superior to clinicopathological factors. GO/KEGG enrichment showed that the differences between high- and low-risk groups were predominantly associated with immune response pathways. Immune-related analysis related to proportions of immune cells, immune function, and expression levels of immune checkpoint genes all showed differences between the high- and low-risk groups. The qRT-PCR and Western blotting results indicate that CD37 expression was markedly higher in MG63 and U2OS cell lines when compared to normal osteoblast hFOB1.19. In U2OS cell line, GABRD expression levels were significantly upregulated. ARHGAP25 expression levels were elevated in both 143B and U2OS cell lines. In summary, utilizing a macrophage genes signature demonstrates efficacy in predicting both the prognosis and therapy response of OS. Additionally, immune analysis confirms a correlation between the risk score and the tumor microenvironment. Our findings, therefore, provide a cogent account for the disparate prognoses observed among patients and furnish a justification for further inquiry into biomarkers and anti-tumor treatment strategies. [ABSTRACT FROM AUTHOR]

Abstract Background Recent scientific research has enabled the identification of macrophages related-genes (MaRG), which play a key role in the control of the immune microenvironment in many human cancers. However, the functional role of MaRGs in human tumors is ill-defined. Herein, we aimed at bioinformatically exploring the molecular signatures of MaRGs in colorectal cancer. Methods A list of MaRGs was generated and their differential expression was analyzed across multiple datasets downloaded from the publicly available functional genomics database Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) was also applied to identify the partner genes of these MaRGs in colorectal cancer. Results After integration of the results from analyses of different datasets, we found that 29 differentially expressed MaRGs (DE-MaRGs) could be considered as CRC-related genes as obtained from the WGCNA analysis. These genes were functionally involved in positive regulation of DNA biosynthetic process and glutathione metabolism. Protein–protein interaction network analysis indicated that PDIA6, PSMA1, PRC1, RRM2, HSP90AB1, CDK4, MCM7, RFC4, and CCT5 were the hub MaRGs. The LASSO approach was used for validating the 29 MaRGs in TCGA-COAD and TCGA-READ data and the results showed that ten among the 29 genes could be considered as MaRGs significantly involved in CRC. The maftools analysis showed that MaRGs were mutated at varying degrees. The nomogram analysis indicated the correlation of these MaRGs with diverse clinical features of CRC patients. Conclusions Conclusively, the present disclosed a signature of MaRGs as potential key regulators involved in CRC pathogenesis and progression. These findings contribute not only to the understanding of the molecular mechanism of CRC pathogenesis but also to the development of adequate immunotherapies for CRC patients.

Macrophages have been reported to exert a crucial role in hepatocellular carcinoma (HCC). This study aimed to explore the macrophage-related genes and establish a macrophage-related signature (MRS) model to predict the overall survival (OS) of patients with HCC based on these genes' expression. We screened the macrophage-related gene module by weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized for further selection, and the selected genes were entered into stepwise regression to develop the MRS model, which was further validated in the Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. We analyzed the biological phenotypes associated with macrophages in terms of functional enrichment, tumor immune signature, and tumor mutational signature. The patient's response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score, the immunophenotype score (IPS), and the IMvigor210 dataset. A novel MRS model was established based on the LASSO regression coefficients of the genes PON1 , IL15RA , NEIL3 , HILPDA , PFN2 , HAVCR1 , ANXA10 , CDCA8 , EPO , S100A9 , TTK , KLRB1 , SPP1 , STC2 , CYP26B1 , GPC1 , G6PD , and CBX2. In either dataset, MRS was identified as an independent risk factor for OS in HCC patients. Additionally, our research indicated that a high-risk score in the MRS model was significantly correlated with tumor staging, pathological grade, tumor–node–metastasis (TNM) stage, and survival. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were highly expressed in the high-risk group. In addition, the frequency of tumor mutations was also higher in the high-risk group. According to our analyses, a higher risk score in the MRS model may predict a better response to immunotherapy. [ABSTRACT FROM AUTHOR]

Macrophages have been reported to exert a crucial role in hepatocellular carcinoma (HCC). This study aimed to explore the macrophage-related genes and establish a macrophage-related signature (MRS) model to predict the overall survival (OS) of patients with HCC based on these genes’ expression. We screened the macrophage-related gene module by weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized for further selection, and the selected genes were entered into stepwise regression to develop the MRS model, which was further validated in the Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. We analyzed the biological phenotypes associated with macrophages in terms of functional enrichment, tumor immune signature, and tumor mutational signature. The patient’s response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score, the immunophenotype score (IPS), and the IMvigor210 dataset. A novel MRS model was established based on the LASSO regression coefficients of the genes PON1, IL15RA, NEIL3, HILPDA, PFN2, HAVCR1, ANXA10, CDCA8, EPO, S100A9, TTK, KLRB1, SPP1, STC2, CYP26B1, GPC1, G6PD, and CBX2. In either dataset, MRS was identified as an independent risk factor for OS in HCC patients. Additionally, our research indicated that a high-risk score in the MRS model was significantly correlated with tumor staging, pathological grade, tumor–node–metastasis (TNM) stage, and survival. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were highly expressed in the high-risk group. In addition, the frequency of tumor mutations was also higher in the high-risk group. According to our analyses, a higher risk score in the MRS model may predict a better response to immunotherapy.

Macrophages play a crucial role in tumor initiation and progression, while macrophage-associated gene signature in colorectal cancer (CRC) patients has not been investigated. Our study aimed to identify macrophage-related molecular subgroups and develop a macrophage-related risk model to predict CRC prognosis. The mRNA expression profile and clinical information of CRC patients were obtained from TCGA and GEO databases. CRC patients from TCGA were divided into high and low macrophage subgroups based on the median macrophage score. The ESTIMATE and CIBERSORT algorithms were used to assess immune cell infiltration between subgroups. GSVA and GSEA analyses were performed to investigate differences in enriched pathways between subgroups. Univariate and LASSO Cox regression were used to build a prognostic risk model, which was further validated in the GSE39582 dataset. A high macrophage score subgroup was associated with poor prognosis, highly activated immune-related pathways and an immune-active microenvironment. A total of 547 differentially expressed macrophage-related genes (DEMRGs) were identified, among which seven genes (including RIMKLB, UST, PCOLCE2, ZNF829, TMEM59L, CILP2, DTNA) were identified by COX regression analyses and used to build a risk score model. The risk model shows good predictive and diagnostic values for CRC patients in both TCGA and GSE39852 datasets. Furthermore, multivariate Cox regression analysis showed that the risk score was an independent risk factor for overall survival in CRC patients. Our findings provided a novel insight into macrophage heterogeneity and its immunological role in CRC. This risk score model may serve as an effective prognostic tool and contribute to personalised clinical management of CRC patients.

Background: Recent scientific research has enabled the identification of macrophages related-genes (MaRG), which play a key role in the control of the immune microenvironment in many human cancers. However, the functional role of MaRGs in human tumors is ill-defined. Herein, we aimed at bioinformatically exploring the molecular signatures of MaRGs in colorectal cancer. Methods: A list of MaRGs was generated and their differential expression was analyzed across multiple datasets downloaded from the publicly available functional genomics database Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) was also applied to identify the partner genes of these MaRGs in colorectal cancer. Results: After integration of the results from analyses of different datasets, we found that 29 differentially expressed MaRGs (DE-MaRGs) could be considered as CRC-related genes as obtained from the WGCNA analysis. These genes were functionally involved in positive regulation of DNA biosynthetic process and glutathione metabolism. Protein–protein interaction network analysis indicated that PDIA6, PSMA1, PRC1, RRM2, HSP90AB1, CDK4, MCM7, RFC4, and CCT5 were the hub MaRGs. The LASSO approach was used for validating the 29 MaRGs in TCGA-COAD and TCGA-READ data and the results showed that ten among the 29 genes could be considered as MaRGs significantly involved in CRC. The maftools analysis showed that MaRGs were mutated at varying degrees. The nomogram analysis indicated the correlation of these MaRGs with diverse clinical features of CRC patients. Conclusions: Conclusively, the present disclosed a signature of MaRGs as potential key regulators involved in CRC pathogenesis and progression. These findings contribute not only to the understanding of the molecular mechanism of CRC pathogenesis but also to the development of adequate immunotherapies for CRC patients. [ABSTRACT FROM AUTHOR]

As the most abundant infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) are pivotal in tumor development and treatment. The present investigation endeavors to explore the potential of M1 macrophage-related genes (MRGs) as biomarkers for assessing risk in individuals with osteosarcoma. RNA-sequence data and clinical data were derived from TCGA and GEO databases. The CIBERSORT method was utilized to discern subtypes of tumor-infiltrating immune cells. Identification of MRGs was achieved through Pearson correlation analysis. A prognostic risk model for MRGs was developed using Cox and LASSO regression analyses. A tripartite gene signature comprising CD37, GABRD, and ARHGAP25 was an independent prognostic indicator and was employed to develop a risk score model. The internal and external validation cohort confirmed the results. The area under the ROC curve (AUC) was determined for survival periods of 1 year, three years, and five years, yielding values of 0.746, 0.839, and 0.850, respectively. The C-index of the risk score was found to be superior to clinicopathological factors. GO/KEGG enrichment showed that the differences between high- and low-risk groups were predominantly associated with immune response pathways. Immune-related analysis related to proportions of immune cells, immune function, and expression levels of immune checkpoint genes all showed differences between the high- and low-risk groups. The qRT-PCR and Western blotting results indicate that CD37 expression was markedly higher in MG63 and U2OS cell lines when compared to normal osteoblast hFOB1.19. In U2OS cell line, GABRD expression levels were significantly upregulated. ARHGAP25 expression levels were elevated in both 143B and U2OS cell lines. In summary, utilizing a macrophage genes signature demonstrates efficacy in predicting both the prognosis and therapy response of OS. Additionally, immune analysis confirms a correlation between the risk score and the tumor microenvironment. Our findings, therefore, provide a cogent account for the disparate prognoses observed among patients and furnish a justification for further inquiry into biomarkers and anti-tumor treatment strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mao, Song, Jin, Zou, Dai, Sun, Xu, Wang and Kang.)