---
title: "Macrophage Subtypes and a Candidate Prognostic Signature in Colorectal Cancer Identified by Integr"
id: "frontiers-in-immunology-13-integrated-single-cell-and-bulk-tissue-analyses-reveal-distinct-macrophage"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-13-integrated-single-cell-and-bulk-tissue-analyses-reveal-distinct-macrophage"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1902264"
published_at: "2026-09-18T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Macrophage Subtypes and a Candidate Prognostic Signature in Colorectal Cancer Identified by Integr
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-13-integrated-single-cell-and-bulk-tissue-analyses-reveal-distinct-macrophage
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1902264)
- **Published At:** 2026-09-18T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The source article title reports an integrated analysis combining **single-cell** and **bulk tissue** data that identifies distinct **macrophage subtypes** in **colorectal cancer** and proposes a candidate **prognostic signature**. - The full article text and detailed methods, cohort characteristics, specific macrophage subtypes, gene markers, statistical outcomes, and validation results were not present in the supplied source material. - Because only the article metadata and title were available from the supplied source, all methodological specifics, numerical results, cohort sizes, and conclusions beyond the title were not reported and cannot be summarized or confirmed. - The title implies relevance for tumor immune characterization and potential prognostic stratification in colorectal cancer, but concrete evidence, performance metrics, and clinical recommendations were not provided in the source text. - Users should consult the complete Frontiers in Immunology article or its supplementary material for full methods, results, and validated conclusions; those details were not included in the provided source content.
## Clinical Analysis & Structured Key Points
Frontiers | Integrated single-cell and bulk tissue analyses reveal distinct macrophage subtypes and a candidate prognostic signature in colorectal cancer: implications for tumor immune characterization ORIGINAL RESEARCH article Front. Immunol. , 18 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1902264 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Part of a Research Topic Precision Medicine in Solid Tumors: Advances, Challenges and Future Outlook Submission open 3305 views 3 articles Editor & Reviewers Edited by P W Priya Wadgaonkar Reviewed by D H DAVID H. MAUKI H K Hayat Khizar Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Table 1 Patient Characteristics of CRC patients in the TCGA-COADREAD datasets. View in article Table 2 Clinicopathological characteristics of the clinical validation cohort (n = 7 for qRT-PCR; n = 3 for Western blot). View in article Table 3 qRT-PCR primer sequences. View in article Table 4 Results of GSVA between macrophage cell subtype. View in article Table 5 Results of GO and KEGG enrichment analysis for MRDEGs. View in article Table 6 Results of univariate and multivariable cox regression analysis. View in article ORIGINAL RESEARCH article Front. Immunol. , 18 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1902264 Integrated single-cell and bulk tissue analyses reveal distinct macrophage subtypes and a candidate prognostic signature in colorectal cancer: implications for tumor immune characterization H C Hui Chen 1,2 Z L Zhipeng Li 1,3 Z L Zhen Lin 4 L W Lijun Wan 1 Y G Yuhang Gong 1 Z L Zhibin Lv 1 J H Jinfeng Hu 4 * D P Dun Pan 1,2 * 1. Department of Gastrointestinal Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, China 2. Fujian Research Institute of Abdominal Surgery, Fuzhou, China 3. First Affiliated Hospital of Fujian Medical University Binhai Campus, Fuzhou, China 4. Fujian Medical University School of Basic Medical Sciences, Fuzhou, China See more Article metrics View details Abstract Introduction: Colorectal cancer (CRC) represents a major global health burden, marked by high morbidity and mortality rates that place a considerable strain on healthcare systems. Methods: This study leveraged integrated bioinformatic analyses, single-cell RNA sequencing, and clinical sample validation to investigate the role of macrophage-related genes (MRGs) in CRC, with the goal of deepening our understanding of the complex interplay within the tumor microenvironment. Results: Differential expression analysis comparing CRC tumor and normal tissues in the TCGA-COADREAD cohort identified 1,962 differentially expressed genes (DEGs). In parallel, a predefined set of 1,719 MRGs was curated from public databases and the literature to define the macrophage-related biological context. By integrating the bulk transcriptomic DEGs, single-cell macrophage subtype-specific genes, and the predefined MRG set, we identified eight hub macrophage-related DEGs (MRDEGs) implicated in CRC. Functional enrichment analysis of these eight MRDEGs revealed significant roles in immune-regulatory processes, including leukocyte chemotaxis, eosinophil chemotaxis, chemokine receptor binding, and the chemokine signaling pathway. From these eight hub MRDEGs, we selected CCL24 and MMP12 via LASSO-Cox regression to construct a prognostic risk model. Immune infiltration analysis using CIBERSORT revealed significant differences ( p 65 368 (57.1%) Patient Characteristics of CRC patients in the TCGA-COADREAD datasets. TCGA, The Cancer Genome Atlas; CRC, Colorectal Cancer. 2.2 Single-cell data processing and identification of key genes The scRNA-seq dataset (GSE166555) was processed using the Seurat R package (version 4.0). A Seurat object was first constructed, and quality control (QC) was performed to retain cells meeting the following criteria: number of detected genes (nFeature_RNA) > 200, total UMI count (nCount_RNA) ≥ 200, and mitochondrial gene percentage (percent.mt) 3, adj. p -value 1 and an adjusted p -value < 0.05. To identify robust candidate genes, MRDEGs that were consistently dysregulated in at least three out of five bulk cohorts (TCGA-COADREAD, GSE20916, GSE73360, GSE44861, and GSE74602) were defined as hub genes. The differential expression profiles of all 56 MRDEGs across these five cohorts, including logFC, P value, adjusted P value, direction, and significance status, are provided in Supplementary Table 2 . The clusterProfiler R package was applied to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the final set of MRDEGs. Additionally, the patterns of somatic mutations and copy number variations within the TCGA-CRC cohort were evaluated by maftools and subsequently visualized through ggplot2. 2.4 Construction and validation of a prognostic signature To construct a prognostic risk score, we subjected the expression profiles of the final MRDEGs in the TCGA cohort to LASSO Cox regression analysis, employing 10-fold cross-validation, as implemented in the glmnet R package. The lambda.min value (the lambda that minimizes the cross-validated deviance) was used to build the final model, as lambda.1se (the largest lambda within one standard error of the minimum) resulted in no retained variables. Patients were then categorized into high-risk and low-risk groups based on the median risk score of the TCGA cohort: patients with a risk score higher than the median were defined as the high-risk group, and those with a risk score equal to or lower than the median were defined as the low-risk group. The continuous risk score was used for model construction and individual risk calculation, while the dichotomized high-/low-risk groups were used for group comparisons in risk distribution plots, immune infiltration analysis, drug sensitivity assessment, and immune checkpoint expression analysis. The independence and prognostic power of the signature were evaluated using univariate and multivariate Cox regression analyses, and a nomogram was developed (rms package). The model’s clinical utility was assessed using decision curve analysis (DCA) ( 24 ) and calibration curves. 2.5 Immune landscape and therapeutic response analysis The immune cell infiltration profile of the TCGA cohort was quantified using single-sample gene-set enrichment analysis (ssGSEA) ( 25 ) and CIBERSORT ( 26 ). Consensus clustering (ConsensusClusterPlus) was applied to the ssGSEA matrix to define immune subtypes. Differences in Tumor Immune Dysfunction and Exclusion (TIDE) scores, tumor mutational burden (TMB), and microsatellite instability (MSI) among subtypes and risk groups were evaluated. Drug sensitivity (IC 50 ) to common chemotherapeutic agents was predicted using the pRRophetic algorithm, and the expression profiles of immune checkpoint genes were compared between risk groups. 2.6 Clinical sample validation Approval for this study was granted by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Approval No.: MRCTA, ECFAH of FMU [2021]423). A total of pathologically confirmed CRC patients were enrolled. Paired CRC tumor tissues and adjacent normal tissues (at least 5 cm from tumor margin) were collected. Inclusion criteria were: (i) histopathologically confirmed CRC after surgical resection; (ii) no prior radiotherapy, chemotherapy, immunotherapy, or other antitumor treatment before surgery; (iii) tissue samples of adequate quality for molecular analyses. Exclusion criteria were: (i) presence of other concurrent malignancies; (ii) receipt of neoadjuvant therapy; (iii) incomplete clinical data; (iv) poor tissue preservation or sample quality. Demographic and clinicopathological characteristics of the cohort (age, sex, tumor location, TNM stage, pathological stage, differentiation, lymph node metastasis) are summarized in Table 2 . This validation cohort was independently collected at our center and had no sample overlap with the TCGA-COADREAD or GEO discovery datasets. Table 2 Patient ID Age (years) Sex Tumor location TNM stage (T/N/M) Pathological stage Differentiation Lymph node metastasis Protein validation (western blot) CRC-01 62 Male Right colon T3/N1/M0 IIIA Poor Yes (1/17) Yes CRC-02 55 Female Rectum T3/N0/M0 IIA Moderate No (0/15) Yes CRC-03 70 Male Left colon T4a/N2/M1 IV Poor Yes (11/28) Yes CRC-04 48 Female Right colon T3/N0/M0 IIA Moderate No (0/16) No CRC-05 65 Male Sigmoid colon T3/N1/M0 IIIB Moderate Yes (2/14) No CRC-06 73 Female Rectum T4b/N2/M0 IIIB Poor Yes (5/20) No CRC-07 59 Male Right colon T4a/N0/M0 IIB Well No (0/18) No Clinicopathological characteristics of the clinical validation cohort (n = 7 for qRT-PCR; n = 3 for Western blot). The mRNA expression levels of the eight macrophage-related hub MRDEGs ( CCL24 , ADAMDEC1 , CHI3L1 , GZMB , CCL23 , CD163L1 , MMP12 , and CDKN3 ) were assessed by quantitative real-time PCR, and the protein expression levels of CCL24 and MMP12 (the two genes constituting the final prognostic signature) were detected by immunoblotting. Both procedures were carried out according to previously reported methods ( 27 ). The primer sequences employed are summarized in Table 3 . Table 3 Primers Primer sequence(5′-3′) CCL24 F: ACATCATCCCTACGGGCTCT R: CTTGGGGTCGCCACAGAAC ADAMDEC1 F:GGCCTTGGTAGGTATGGAAATC R:CCCCAGGTTAGAACTGTGCC CHI3L1 F:AAGCAACGATCACATCGACAC R:TCAGGTTGGGGTTCCTGTTCT GZMB F:CCCTGGGAAAACACTCACACA R:CCCTGGGAAAACACTCACACA CCL23 F:CATCTCCTACACCCCACGAAG R:GGGTTGGCACAGAAACGTC CD163L1 F:GCTGTGGTAACTTGCATCCTG R:GCAGTAGTGTTCCACCCATCA MMP12 F:CATGAACCGTGAGGATGTTGA R:GCATGGGCTAGGATTCCACC CDKN3 F:TCCGGGGCAATACAGACCAT R:GCAGCTAATTTGTCCCGAAACTC qRT-PCR primer sequences. All primers were synthesized by Shanghai Shenggong Bioengineering Co., Ltd. (F, forward; R, reverse). 2.7 Statistical analysis All statistical analyses were performed using R software (version 4.3.2). Comparisons between groups were made using Student’s t-test, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the underlying assumptions. Correlation analyses were conducted based on Spearman’s method. A two-tailed p -value below 0.05 was regarded as indicating statistical significance. For multiple comparisons involving multiple immune cell types, immune checkpoints, and drug sensitivity analyses, the Benjamini-Hochberg (BH) method was applied to adjust P values, and an adjusted P value or false discovery rate (FDR) < 0.05 was used as the primary threshold for statistical significance. P < 0.05 was otherwise considered statistically significant for single comparisons. 3 Result 3.1 Single-cell profiling reveals macrophage heterogeneity and core candidate genes in colorectal cancer To obtain a high-resolution view of the CRC tumor microenvironment and identify macrophage subtype-related genes as a starting point for subsequent bulk cohort screening and prognostic modeling, we analyzed the scRNA-seq dataset GSE166555. After quality control, we identified seven major cell populations, including epithelial cells, T cells, B cells, macrophages, and mast cells ( Figure 1A ). Notably, two spatially separated clusters in the UMAP embedding were annotated as B cells. Both clusters exhibited B-cell lineage marker expression (e.g., CD79A and MS4A1) and were therefore assigned to the B-cell lineage; however, they represented distinct transcriptional states. To reflect this, they are labeled as “B cell cluster 1” and “B cell cluster 2” in Figure 1A . To further support the major cell type annotation, we visualized the expression of representative marker genes for each major cell type on the UMAP embedding; the resulting feature plots are provided in Supplementary Figu
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