Clinical M2 macrophages-related genes to aid therapy in pancreatic ductal adenocarcinoma

Qianhui Xu,Shaohuai Chen,Wen Huang,Yuanbo Hu

doi:10.1186/s12935-021-02289-w

Abstract

BackgroundIncreasing evidence supports that infiltration M2 Macrophages act as pivotal player in tumor progression of pancreatic ductal adenocarcinoma (PDAC). Nonetheless, comprehensive analysis of M2 Macrophage infiltration and biological roles of hub genes (FAM53B) in clinical outcome and immunotherapy was lack.MethodThe multiomic data of PDAC samples were downloaded from distinct datasets. CIBERSORT algorithm was performed to uncover the landscape of TIME. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate module and significant genes associated with M2 Macrophages. Kaplan-Meier curve and receiver operating characteristic (ROC) curves were applied for prognosis value validation. Mutation data was analyzed by using “maftools” R package. Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual sample. Immunophenoscore (IPS) was implemented to estimate immunotherapeutic significance of risk score. The half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs was predicted by using the pRRophetic algorithm. Finally, quantitative real-time polymerase chain reaction was used to determine FAM53B mRNA expression and TIMER database was utilized to uncover its possible role in immune infiltration of PDAC.ResultsHerein, 17,932 genes in 234 samples (214 tumor and 20 normal) were extracted from three platforms. Taking advantage of WGCNA, significant module (royalblue) and 135 candidate genes were considered as M2 Macrophages-related genes. Subsequently, risk signature including 5 hub genes was developed by multiple analysis, which exhibited excellent prognostic performance. Besides, comprehensive prognostic nomogram was constructed to quantitatively estimate risk. Then, intrinsic link between risk score with tumor mutation burden (TMB) was explored. Additionally, risk score significantly correlated with diversity of tumor immune microenvironment (TIME). PDAC samples within different risk presented diverse signaling pathways activity and experienced significantly distinct sensitivity to administering chemotherapeutic or immunotherapeutic agents. Finally, the biological roles of FAM53B were revealed in PDAC.ConclusionsTaken together, comprehensive analyses of M2 Macrophages profiling will facilitate prognostic prediction, delineating complexity of TIME, and contribute insight into precision therapy for PDAC.

Highlights

Pancreatic ductal adenocarcinoma (PDAC) as the seventh leading cause of cancer associated death was one of the most common human malignancies globally [1, 2]
Given the batch effect from different platforms, PDAC samples were gathered by batches based on the top two principal components (PCs) of unnormalized mRNA expression levels (Fig. 1A)
After removal of batch effect, the scatter-plot based on principal component analysis (PCA) of normalized sequencing presented that the batch effect was successfully removed by cross-platform normalization (Fig. 1B)

Summary

Introduction

Pancreatic ductal adenocarcinoma (PDAC) as the seventh leading cause of cancer associated death was one of the most common human malignancies globally [1, 2]. Given the difficulty of early precision diagnosis and rapid tumor progression, a large number of PDAC cases presented advanced clinical stage or distant metastatic disease at diagnosis [2]. It is of great importance, to develop novel and reliable indicators for prognostic estimation and therapeutic efficacy prediction, further advance tailored therapy. The most reliable and promising strategy for comprehensive evaluation of tumor sensitivity to clinical treatment may be one derived from immune profiles, identifying PDAC cases according to specific risk signatures correlated with M2 Macrophages profiling, generating individualized program to improve efficacy . Comprehensive analysis of M2 Macrophage infiltration and biological roles of hub genes (FAM53B) in clinical outcome and immunotherapy was lack

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Results

Conclusion