Integrated analysis of tumor-associated macrophages and M2 macrophages in CRC: unraveling molecular heterogeneity and developing a novel risk signature.

Emerging investigations have increasingly highlighted the critical role of tumor-associated macrophages (TAMs) and M2 macrophages in cancer development, progression, and metastasis, marking them as potential targets in various cancer types. The main objective of this research is to discover new biomarkers associated with TAM-M2 macrophages in colorectal cancer (CRC) and to dissect the molecular heterogeneity of CRC by combining single-cell RNA sequencing and bulk RNA-seq data. By utilizing weighted gene co-expression network analysis (WGCNA), we acquired TAM-M2-associated genes by intersecting TAM marker genes obtained from scRNA-seq data with module genes of M2 macrophages derived from bulk RNA-seq data. We employed least absolute shrinkage and selection operator (LASSO) Cox analysis to select predictive biomarkers from these TAM-M2-related genes. Quantitative polymerase chain reaction (qPCR) was employed to validate the mRNA expression levels of the genes identified in the screening. This led to the development of the TAM-M2-related signature (TAMM2RS). We also conducted functional and immune landscape analyses of different risk groups. The combination of scRNA-seq and bulk RNA-seq analyses yielded 377 TAM-M2-related genes. DAPK1, NAGK, and TRAF1 emerged as key prognostic genes in CRC, which were identified through LASSO Cox analysis. Utilizing these genes, we constructed and validated the TAMM2RS, demonstrating its effectiveness in predicting survival in CRC patients. Our research offers a thorough investigation into the molecular mechanisms associated with TAM-M2 macrophages in CRC and unveils potential therapeutic targets, offering new insights for treatment strategies in colorectal cancer.

Shi L ，Mao H ，Ma J 《BMC Medical Genomics》

M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma.

M2-like tumor-associated macrophages (M2-like TAMs) have important roles in the progression and therapeutics of cancers. We aimed to detect novel M2-like TAM-related biomarkers in hepatocellular carcinoma (HCC) via integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to construct a novel prognostic signature, reveal the "immune landscape", and screen drugs in HCC. M2-like TAM-related genes were obtained by overlapping the marker genes of TAM identified from scRNA-seq data and M2 macrophage modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were carried out to screen prognostic genes from M2-like TAM-related genes, followed by a construction of a prognostic signature, delineation of risk groups, and external validation of the prognostic signature. Analyses of immune cells, immune function, immune evasion scores, and immune-checkpoint genes between high- and low-risk groups were done to further reveal the immune landscape of HCC patients. To screen potential HCC therapeutic agents, analyses of gene-drug correlation and sensitivity to anti-cancer drugs were conducted. A total of 127 M2-like TAM-related genes were identified by integrative analysis of scRNA-seq and bulk-seq data. PDLIM3, PAM, PDLIM7, FSCN1, DPYSL2, ARID5B, LGALS3, and KLF2 were screened as prognostic genes in HCC by univariate Cox regression and LASSO regression analyses. Then, a prognostic signature was constructed and validated based on those genes for predicting the survival of HCC patients. In terms of drug screening, expression of PAM and LGALS3 was correlated positively with sensitivity to simvastatin and ARRY-162, respectively. Based on risk grouping, we predicted 10 anticancer drugs with high sensitivity in the high-risk group, with epothilone B having the lowest half-maximal inhibitory concentration among all drugs tested. Our findings enhance understanding of the M2-like TAM-related molecular mechanisms involved in HCC, reveal the immune landscape of HCC, and provide potential targets for HCC treatment.

Qu X ，Zhao X ，Lin K ，Wang N ，Li X ，Li S ，Zhang L ，Shi Y ... -  《Frontiers in Immunology》

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer.

Ou Y ，Xia C ，Ye C ，Liu M ，Jiang H ，Zhu Y ，Yang D ... -  《-》

Exploring the immune landscape and drug prediction of an M2 tumor-associated macrophage-related gene signature in EGFR-negative lung adenocarcinoma.

Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD. M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature. CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy. The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Huang Y ，Zhang Y ，Duan X ，Hou R ，Wang Q ，Shi J ... -  《-》

Integrated Analysis Construct a Tumor-Associated Macrophage Novel Signature with Promising Implications in Predicting the Prognosis and Immunotherapeutic Response of Gastric Cancer Patients.

Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide. At present, tumor-associated macrophages (TAMs) are essential in the progression, metastasis, and drug resistance of tumors. Therefore, TAMs can be a crucial target for tumor treatment. We intended to investigate the TAM characteristics in GC and develop a risk signature based on TAM to predict the prognosis of GC patients. The single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were acquired from a publicly available database. We utilized the Seurat pipeline to process the scRNA-seq data and determine TAM cell types using marker genes. Univariate Cox regression analysis was utilized to examine TAM-related prognostic genes, and then we employed Lasso-Cox regression analysis, and Multivariate Cox regression analysis established a novel risk profile to forecast the clinical value of the model with a new nomogram combining risk profiles and clinicopathological characteristics. The current study employed scRNA-seq data to identify five TAM clusters in GC, among which four were significantly associated with GC prognosis. Accordingly, we further developed a TAM-related risk signature utilizing nine genes. After evaluation, our model accurately predicted the prognosis of gastric cancer. Generally, GC patients with low TAMS scores exhibited a more favorable prognosis, greater benefits from immunotherapy, and higher levels of immune cell infiltration. The prognosis of GC can be effectively predicted by TAM-based risk signatures, and the signature may provide a new perspective for comprehensively guiding clinical diagnosis, prediction, and immunotherapy for gastric cancer.

Xin H ，Chen Y ，Niu H ，Li X ，Gai X ，Cui G ... -  《-》