Integrated microenvironment-associated genomic profiles identify LRRC15 mediating recurrent glioblastoma-associated macrophages infiltration

Glioblastoma (GBM) is the most common malignant intracranial tumour with intrinsic infiltrative characteristics, which could lead to most patients eventually relapse. The prognosis of recurrent GBM patients remains unsatisfactory. Cancer cell infiltration and their interaction with the tumour microenvironment (TME) could promote tumour recurrence and treatment resistance. In our study, we aimed to identify potential tumour target correlated with rGBM microenvironment based on the gene expression profiles and clinical information of rGBM patients from The Cancer Genome Atlas (TCGA) database. LRRC15 gene with prognostic value was screened by univariate and multivariate analysis, and the correlation between macrophages and LRRC15 was identified as well. Furthermore, the prognosis correlation and immune characteristics of LRRC15 were validated using the Chinese Glioma Genome Atlas (CGGA) database and our clinical tissues by immunochemistry assay. Additionally, we utilized the transwell assay and carboxy fluorescein succinimidyl ester (CFSE) tracking to further confirm the effects of LRRC15 on attracting microglia/macrophages and tumour cell proliferation in the TME. Gene profiles-based rGBM microenvironment identified that LRRC15 could act in collusion with microglia/macrophages in the rGBM microenvironment to promote the poor prognosis, especially in mesenchymal subtype, indicating the strategies of targeting LRRC15 to improve macrophages-based immunosuppressive effects could be promising for rGBM treatments.     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.     

Transforming growth factor-β signalling in tumour resistance to the anti-PD-(L)1 therapy: Updated

Low frequency of durable responses in patients treated with immune checkpoint inhibitors (ICIs) demands for taking complementary strategies in order to boost immune responses against cancer. Transforming growth factor-β (TGF-β) is a multi-tasking cytokine that is frequently expressed in tumours and acts as a critical promoter of tumour hallmarks. TGF-β promotes an immunosuppressive tumour microenvironment (TME) and defines a bypass mechanism to the ICI therapy. A number of cells within the stroma of tumour are influenced from TGF-β activity. There is also evidence of a relation between TGF-β with programmed death-ligand 1 (PD-L1) expression within TME, and it influences the efficacy of anti-programmed death-1 receptor (PD-1) or anti-PD-L1 therapy. Combination of TGF-β inhibitors with anti-PD(L)1 has come to the promising outcomes, and clinical trials are under way in order to use agents with bifunctional capacity and fusion proteins for bonding TGF-β traps with anti-PD-L1 antibodies aiming at reinvigorating immune responses and promoting persistent responses against advanced stage cancers, especially tumours with immunologically cold ecosystem.     

GNG4, as a potential predictor of prognosis,is correlated with immune infiltrates in colon adenocarcinoma

The tumour microenvironment (TME) and immunosuppression play an important role in colon cancer (CC) metastasis, which seriously affects the prognosis of CC. G protein subunit gamma 4 (GNG4) has been shown to participate in tumour progression and the tumour mutation burden (TMB) in colorectal cancer. However, the effect of GNG4 on the CC TME and immunology remains elusive. Weighted gene coexpression network analysis (WGCNA) was employed for screening aberrantly expressed genes associated with the immune score, and GNG4 was then selected through prognostic and immune correlation analysis. Based on RNA sequencing data obtained from the TCGA and GEO databases, the expression pattern and immune characteristics of GNG4 were comprehensively examined using a pan-cancer analysis. Upregulation of GNG4 was linked to an adverse prognosis and immune inhibitory phenotype in CC. Pan-cancer analysis demonstrated higher GNG4 expression in tumours than in paired normal tissue in human cancers. GNG4 expression was closely related to prognosis, TMB, immune checkpoints (ICPs), microsatellite instability (MSI) and neoantigens. GNG4 promoted CC cell proliferation, migration and invasion and participated in immune regulation in the TME. Significantly, GNG4 expression was found to negatively correlate with tumour-infiltrating immune cells, ICP, TMB and MSI in CC. GNG4 expression predicted the immunotherapy response in the IMvigor210 cohort, suggesting that GNG4 could be used as a potential biomarker in CC for prognostication and immunology. Moreover, the expression of GNG4 predicted the immunotherapy response of ICB in CC.     

NOD2 deficiency confers a pro-tumorigenic macrophage phenotype to promote lung adenocarcinoma progression

Nucleotide-binding and oligomerization domain-containing protein 2 (NOD2) was a member of the NOD-like receptor family and played an important role in the innate immune response. Dysregulated NOD2 had been reported to contribute to tumorigenesis and progression. Here, we investigated that decreased NOD2 expressions could affect the phenotypic polarization of tumour-associated macrophages and thus lead to the poor prognosis of lung adenocarcinoma patients. We clustered the patients by the single-sample gene set enrichment analysis of tumour microenvironment and 13 prognostic differentially expressed immune-related genes (PDEIRGs) were obtained based on prognostic analyses. After multiple assessments on the 13 PDEIRGs, NOD2 was considered to be the central immune gene and had a strong effect on suppressing tumour progression. Decreased NOD2 expression could be induced by cancer cells and lead to the phenotypic polarization of macrophages from protective M1 phenotype to pro-tumorigenic M2 subtype which might be attributed to the down-regulating of NF-κB signalling pathway. This study draw attention to the role of inhibited innate immune function mediated by depletion of NOD2 in the TME. Our work also points to a potential strategy of NOD2-mediated TAM-targeted immunotherapy.     

Modifying the tumour microenvironment and reverting tumour cells: New strategies for treating malignant tumours

The tumour microenvironment (TME) plays a pivotal role in tumour fate determination. The TME acts together with the genetic material of tumour cells to determine their initiation, metastasis and drug resistance. Stromal cells in the TME promote the growth and metastasis of tumour cells by secreting soluble molecules or exosomes. The abnormal microenvironment reduces immune surveillance and tumour killing. The TME causes low anti‐tumour drug penetration and reactivity and high drug resistance. Tumour angiogenesis and microenvironmental hypoxia limit the drug concentration within the TME and enhance the stemness of tumour cells. Therefore, modifying the TME to effectively attack tumour cells could represent a comprehensive and effective anti‐tumour strategy. Normal cells, such as stem cells and immune cells, can penetrate and disrupt the abnormal TME. Reconstruction of the TME with healthy cells is an exciting new direction for tumour treatment. We will elaborate on the mechanism of the TME to support tumours and the current cell therapies for targeting tumours and the TME—such as immune cell therapies, haematopoietic stem cell (HSC) transplantation therapies, mesenchymal stem cell (MSC) transfer and embryonic stem cell‐based microenvironment therapies—to provide novel ideas for producing breakthroughs in tumour therapy strategies.     

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.     

Transforming growth factor‐β signalling in tumour resistance to the anti‐PD‐(L)1 therapy: Updated

Low frequency of durable responses in patients treated with immune checkpoint inhibitors (ICIs) demands for taking complementary strategies in order to boost immune responses against cancer. Transforming growth factor‐β (TGF‐β) is a multi‐tasking cytokine that is frequently expressed in tumours and acts as a critical promoter of tumour hallmarks. TGF‐β promotes an immunosuppressive tumour microenvironment (TME) and defines a bypass mechanism to the ICI therapy. A number of cells within the stroma of tumour are influenced from TGF‐β activity. There is also evidence of a relation between TGF‐β with programmed death‐ligand 1 (PD‐L1) expression within TME, and it influences the efficacy of anti‐programmed death‐1 receptor (PD‐1) or anti‐PD‐L1 therapy. Combination of TGF‐β inhibitors with anti‐PD(L)1 has come to the promising outcomes, and clinical trials are under way in order to use agents with bifunctional capacity and fusion proteins for bonding TGF‐β traps with anti‐PD‐L1 antibodies aiming at reinvigorating immune responses and promoting persistent responses against advanced stage cancers, especially tumours with immunologically cold ecosystem.     

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.     

Pyroptosis in glioblastoma: A crucial regulator of the tumour immune microenvironment and a predictor of prognosis

Recent studies have shown that pyroptosis, an inflammatory form of cell death, has a dual role in tumorigenesis and tumour progression and affects the prognosis of patients; however, the role of pyroptosis in glioblastoma (GBM) is still unclear. In this study, based on GBM patients'' data from two independent cohorts, we performed a comprehensive analysis of the expression and prognostic value of 33 pyroptosis‐associated genes (PAGs) in GBM, as well as their role in the tumour immune microenvironment (TIME) of GBM. We identified 29 PAGs that were differentially expressed between GBM and normal brain tissue, 18 of which were upregulated in GBM tissue. Most of the 33 PAGs were strongly correlated with the levels of immune cell infiltration. Based on the 33 PAGs, the GBM samples can be divided into two clusters (C1‐C2), with C1 having a ‘hot’ but immunosuppressive TIME and C2 having a ‘cold’ TIME, suggesting different immunotherapeutic responses in the two clusters. In addition, we identified four PAGs that were strongly associated with GBM prognosis and constructed a risk model based on these four PAGs. This risk model is an independent prognostic factor for GBM patients, and there is a different immune status between high‐ and low‐risk groups. In conclusion, this study demonstrates that pyroptosis is closely associated with the prognosis and TIME of GBM and provides an important basis for further studies on the relationship between pyroptosis and GBM.     

Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer

Tumour microenvironment (TME) is crucial to tumorigenesis. This study aimed to uncover the differences in immune phenotypes of TME in endometrial cancer (EC) using Uterine Corpus Endometrial Carcinoma (UCEC) cohort and explore the prognostic significance. We employed GVSA enrichment analysis to cluster The Cancer Genome Atlas (TCGA) EC samples into immune signature cluster modelling, evaluated immune cell profiling in UCEC cohort (n = 538) and defined four immune subtypes of EC. Next, we analysed the correlation between immune subtypes and clinical data including patient prognosis. Furthermore, we analysed the expression of immunomodulators and DNA methylation modification. The profiles of immune infiltration in TCGA UCEC cohort showed significant difference among four immune subtypes of EC. Among each immune subtype, natural killer T cells (NKT), dendritic cells (DCs) and CD8⁺T cells were significantly associated with EC patients survival. Each immune subtype exhibited specific molecular classification, immune cell characterization and immunomodulators expression. Moreover, the expression immunomodulators were significantly related to DNA methylation level. In conclusion, the identification of immune subtypes in EC tissues could reveal unique immune microenvironments in EC and predict the prognosis of EC patients.     

Aberrant lipid metabolism in hepatocellular carcinoma cells as well as immune microenvironment: A review

Hepatocellular carcinoma (HCC) is a primary malignancy of the liver with a high worldwide prevalence and poor prognosis. Researches are urgently needed on its molecular pathogenesis and biological characteristics. Metabolic reprogramming for adaptation to the tumour microenvironment (TME) has been recognized as a hallmark of cancer. Dysregulation of lipid metabolism especially fatty acid (FA) metabolism, which involved in the alternations of the expression and activity of lipid‐metabolizing enzymes, is a hotspot in recent study, and it may be involved in HCC development and progression. Meanwhile, immune cells are also known as key players in the HCC microenvironment and show complicated crosstalk with cancer cells. Emerging evidence has shown that the functions of immune cells in TME are closely related to abnormal lipid metabolism. In this review, we summarize the recent findings of lipid metabolic reprogramming in TME and relate these findings to HCC progression. Our understanding of dysregulated lipid metabolism and associated signalling pathways may suggest a novel strategy to treat HCC by reprogramming cell lipid metabolism or modulating TME.     

Oncolytic virotherapy for pancreatic ductal adenocarcinoma: A glimmer of hope after years of disappointment?

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and highly lethal malignancies. Existing therapeutic interventions have so far been unsuccessful in improving prognosis, and survival remains very poor. Oncolytic virotherapy represents a promising, yet not fully explored, alternative strategy for the treatment of PDAC. Oncolytic viruses (OVs) infect, replicate within and lyse tumor cells specifically and stimulate antitumor immune responses. Multiple challenges have hampered the efficacy of oncolytic virotherapy for PDAC, the most significant being the desmoplastic and immunosuppressive pancreatic tumor microenvironment (TME). The TME limits the access of therapeutic drugs and the infiltration of effector T cells and natural killer (NK) cells into the tumor mass. Additionally, cancer cells promote the secretion of immunosuppressive factors and develop mechanisms to evade the host immune system. Because of their oncolytic and immune-stimulating properties, OVs are the ideal candidates for counteracting the pancreatic immunosuppressive TME and for designing combination therapies that can be clinically exploited in clinical trials that seek to improve the prognosis of PDAC.     

Potent immunogenicity in BRCA1‐mutated patients with high‐grade serous ovarian carcinoma

High‐grade serous ovarian carcinomas (HGSOCs) were among the tumours with an unsatisfactory outcome of immune checkpoint inhibitors (ICIs). It is imperative to develop feasible biomarker for identifying responsive candidates and guiding precise immunotherapy for HGSOC patients. Here, we analysed genomic data of patients with HGSOCs to depict their immunological phenotype of tumour microenvironment (TME) and figure out the major determinants of immunogenicity. In comparison with other solid tumours, we observed the lowest levels of PD‐L1, total mutation burden (TMB) and cytolytic molecules in HGSOCs. Surprisingly, TMB is not certainly positively related to tumour immune response as it failed to predict the response to ICIs in a considerable portion of patients in previous clinical trials. By a machine learning approach in search of biomarkers for immunotherapy implications for HGSOCs, we identified the ten most dominant factors determining the immunogenicity of HGSOCs. Interestingly, we found that BRCA1 mutated tumours presented a potent immunogenic phenotype, independent of TMB, meeting the criteria of both our dominant factors and the determinants of immunogenicity established before. Our findings provide evidence that BRCA1‐mutation may be served as a predictive biomarker in guiding ICI therapies for the patients with HGSOCs.     

Development of a tumor microenvironment-related prognostic signature in glioma to predict immune landscape and potential therapeutic drugs

The involvement of the tumor microenvironment (TME) in the biology of gliomas has expanded, while it is yet uncertain its potential of supporting diagnosis and therapy choices. According to immunological characteristics and overall survival, cohorts of glioma patients from public databases were separated into two TME-relevant clusters in this analysis. Based on differentially expressed genes between TME clusters and correlative regression analysis, a 21-gene molecular classifier of TME-related prognostic signature (TPS) was constructed. Afterward, the prognostic efficacy and effectiveness of TPS were assessed in the training and validation groups. The outcome demonstrated that TPS might be utilized alone or in conjunction with other clinical criteria to act as a superior prognostic predictor for glioma. Also, high-risk glioma patients classified by TPS were considered to associate with enhanced immune infiltration, greater tumor mutation, and worse general prognosis. Finally, possible treatment medicines specialized for different risk subgroups of TPS were evaluated in drug databases.     

The roles of mast cells in anticancer immunity

The tumor microenvironment (TME), which is composed of stromal cells such as endothelial cells, fibroblasts, and immune cells, provides a supportive niche promoting the growth and invasion of tumors. The TME also raises an immunosuppressive barrier to effective antitumor immune responses and is therefore emerging as a target for cancer immunotherapies. Mast cells (MCs) accumulate in the TME at early stages, and their presence in the TME is associated with poor prognosis in many aggressive human cancers. Some well-established roles of MCs in cancer are promoting angiogenesis and tumor invasion into surrounding tissues. Several mouse models of inducible and spontaneous cancer show that MCs are among the first immune cells to accumulate within and shape the TME. Although MCs and other suppressive myeloid cells are associated with poor prognosis in human cancers, high densities of intratumoral T effector (T(eff)) cells are associated with a favorable prognosis. The latter finding has stimulated interest in developing therapies to increase intratumoral T cell density. However, cellular and molecular mechanisms promoting high densities of intratumoral T(eff) cells within the TME are poorly understood. New evidence suggests that MCs are essential for shaping the immune-suppressive TME and impairing both antitumor T(eff) cell responses and intratumoral T cell accumulation. These roles for MCs warrant further elucidation in order to improve antitumor immunity. Here, we will summarize clinical studies of the prognostic significance of MCs within the TME in human cancers, as well as studies in mouse models of cancer that reveal how MCs are recruited to the TME and how MCs facilitate tumor growth. Also, we will summarize our recent studies indicating that MCs impair generation of protective antitumor T cell responses and accumulation of intratumoral T(eff) cells. We will also highlight some approaches to target MCs in the TME in order to unleash antitumor cytotoxicity.     

Prognostic signature and immune efficacy of m1A-, m5C- and m6A-related regulators in cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive cancer; given that initial and specific signs are lacking, diagnosis is often late and the prognosis is poor. RNA modification has been widely studied in tumour progression. Nevertheless, little progress has been made in the signature of N¹-methyladenosine (m¹A), 5-methylcytosine (m⁵C), N⁶-methyladenosine (m⁶A)-related regulators and the tumour microenvironment (TME) cell infiltration in CM. Our study identified the characteristics of m¹A-, m⁵C- and m⁶A-related regulators based on 468 CM samples from the public database. Using univariate, multivariate and LASSO Cox regression analysis, a risk model of regulators was established and validated by a nomogram on independent prognostic factors. The gene set variation analysis (GSVA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) clarified the involved functional pathways. A combined single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT approach revealed TME of regulator-related prognostic signature. The nine-gene signature stratified the patients into distinct risk subgroups for personalized prognostic assessment. Additionally, functional enrichment, immune infiltration and immunotherapy response analysis indicated that the high-risk group was correlated with T-cell suppression, while the low-risk group was more sensitive to immunotherapy. The findings presented here contribute to our understanding of the TME molecular heterogeneity in CM. Nine m¹A-, m⁵C- and m⁶A-related regulators may also be promising biomarkers for future research.     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.