Immuno-Genomic Landscape of Cutaneous Melanoma Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature

2021 ◽  
Author(s):  
Ronghua Yang ◽  
Yidan Sun ◽  
Tianqi Chen ◽  
Jiehua Li ◽  
Xiaobing Pi ◽  
...  
Keyword(s):  
Risk Score ◽  
Cutaneous Melanoma ◽  
Immune Cell ◽  
Gene Signature ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Gene Set ◽  
Immune Infiltration

Abstract BackgroundThe tumorigenesis of Skin cutaneous melanoma (SKCM) is still a mystery. Our study conducted a comprehensive analysis of the immune cell infiltration in the TME of SKCM. Based on the differential expression genes in the cluster grouped by the immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration were explored.MethodsWe analyzed the immune cell infiltration in two independent cohorts, and then assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways and gene mutations. We further divided the three clusters of differential genes into two groups with different unique biological processes. The Signature gene-A gene set was mainly manifested as exon skipping (ES) in SKCM patients, while the Signature gene-B gene set has no obvious alternative splicing form. Subsequently, we not only analyzed the genetic variation of the two signatures, but also constructed a ceRNA regulatory network..LASSO Cox regression was utilized to find the immune infiltration signature and the risk score of SKCM. ResultWe finally obtained 13 Hub genes, and calculated the risk score based on the coefficient of each gene to further explore the impact of the high and low-risk score on the biologically related functions and prognosis of SKCM patients.The correlation between the risk score and the clinicopathological characteristics of SKCM patients indicated that the low risk score was associated with TMECluster-A classification (P <0.001) and metastatic SKCM (P <0.001). We finally obtained 13 Hub genes which showed different prognostic effects in pan-cancers. The IHC staining results showed that Ube2L6, SRPX2, IFIT2 were higher expression while CLEC4E, END3, KIR2DL4 were lower expression in 25 melanoma specimens.ConclusionWe performed a comprehensive assessment of SKCM's immune environment and constructed a set of unprecedented immune signatures related to the immune landscape (EDN3、CLEC4E、SRPX2、KIR2DL4、UBE2L6、IFIT2), which are correlated with the different prognosis and drug response of SKCM. The immune gene signature we constructed can be used as a robust prognostic biomarker of SKCM and a predictor of immunotherapy effect.

scholarly journals The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

2021 ◽  
Vol 9 ◽  
Author(s):  
Sitong Zhou ◽  
Yidan Sun ◽  
Tianqi Chen ◽  
Jingru Wang ◽  
Jia He ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Signature ◽  
Clinicopathological Characteristics ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Cluster A

The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored.Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p &lt; 0.001) and metastatic SKCM (p &lt; 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens.Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

scholarly journals A novel pyroptosis-associated gene signature for immune status and prognosis of cutaneous melanoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12304
Author(s):  
Zhengyuan Wu ◽  
Leilei Chen ◽  
Chaojie Jin ◽  
Jing Xu ◽  
Xingqun Zhang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Immune Status ◽  
Immune Cell ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tumor Cell Death ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Background Cutaneous melanoma (CM) is a life-threatening destructive malignancy. Pyroptosis significantly correlates with programmed tumor cell death and its microenvironment through active host-tumor crosstalk. However, the prognostic value of pyroptosis-associated gene signatures in CM remains unclear. Methods Gene profiles and clinical data of patients with CM were downloaded from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes associated with pyroptosis and overall survival (OS). We constructed a prognostic gene signature using LASSO analysis, then applied immune cell infiltration scores and Kaplan-Meier, Cox, and pathway enrichment analyses to determine the roles of the gene signature in CM. A validation cohort was collected from the Gene Expression Omnibus (GEO) database. Results Four pyroptosis-associated genes were identified and incorporated into a prognostic gene signature. Integrated bioinformatics findings showed that the signature correlated with patient survival and was associated with tumor growth and metastasis. The results of Gene Set Enrichment Analysis of a risk signature indicated that several enriched pathways are associated with cancer and immunity. The risk signature for immune status significantly correlated with tumor stem cells, the immune microenvironment, immune cell infiltration and immune subtypes. The expression of four pyroptosis genes significantly correlated with the OS of patients with CM and was related to the sensitivity of cancer cells to several antitumor drugs. A signature comprising four genes associated with pyroptosis offers a novel approach to the prognosis and survival of patients with CM and will facilitate the development of individualized therapy.

scholarly journals A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals Alternative Splicing Events in Tumor Immune Infiltration in Colorectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jian-yu Shi ◽  
Yan-yan Bi ◽  
Bian-fang Yu ◽  
Qing-feng Wang ◽  
Dan Teng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Alternative Splicing ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Cell Infiltration ◽  
Immune Infiltration

Despite extensive research, the exact mechanisms involved in colorectal cancer (CRC) etiology and pathogenesis remain unclear. This study aimed to examine the correlation between tumor-associated alternative splicing (AS) events and tumor immune infiltration (TII) in CRC. We analyzed transcriptome profiling and clinical CRC data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq and Innate databases, respectively to develop and validate a risk model of differential AS events and subsequently a TII risk model. We then conducted a two-factor survival analysis to study the association between TII and AS risk and evaluated the associations between immune signatures and six types of immune cells based on the TIMER database. Subsequently, we studied the distribution of six types of TII cells in high- and low-risk groups for seven AS events and in total. We obtained the profiles of AS events/genes for 484 patients, which included 473 CRC tumor samples and 41 corresponding normal samples, and detected 22581 AS events in 8122 genes. Exon Skip (ES) (8446) and Mutually Exclusive Exons (ME) (74) exhibited the most and fewest AS events, respectively. We then classified the 433 patients with CRC into low-risk (n = 217) and high-risk (n = 216) groups based on the median risk score in different AS events. Compared with patients with low-risk scores (mortality = 11.8%), patients with high-risk scores were associated with poor overall survival (mortality = 27.6%). The risk score, cancer stage, and pathological stage (T, M, and N) were closely correlated with prognosis in patients with CRC (P &lt; 0.001). We identified 6479 differentially expressed genes from the transcriptome profiles of CRC and intersected 468 differential immune-related signatures. High-AS-risk and high-TII-risk predicted a poor prognosis in CRC. Different AS types were associated with different TII risk characteristics. Alternate Acceptor site (AA) and Alternate Promoter (AP) events directly affected the concentration of CD4T cells, and the level of CD8T cells was closely correlated with Alternate Terminator (AT) and Exon Skip (ES) events. Thus, the concentration of CD4T and CD8T cells in the CRC immune microenvironment was not specifically modulated by AS. However, B cell, dendritic cell, macrophage, and neutrophilic cell levels were strongly correlated with AS events. These results indicate adverse associations between AS event risk levels and immune cell infiltration density. Taken together, our findings show a clear association between tumor-associated alternative splicing and immune cell infiltration events and patient outcome and could form a basis for the identification of novel markers and therapeutic targets for CRC and other cancers in the future.

scholarly journals Identification of an m6A Regulators-Mediated Prognosis Signature For Survival Prediction and Its Relevance to Immune Infiltration in Melanoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Liuxing Wu ◽  
Xin Hu ◽  
Hongji Dai ◽  
Kexin Chen ◽  
Ben Liu
Keyword(s):  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Low Risk ◽  
Cell Infiltration ◽  
Survival Prediction ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Related Risk ◽  
Significant Difference

Despite robust evidence for the role of m6A in cancer development and progression, its association with immune infiltration and survival outcomes in melanoma remains obscure. Here, we aimed to develop an m6A-related risk signature to improve prognostic and immunotherapy responder prediction performance in the context of melanoma. We comprehensively analyzed the m6A cluster and immune infiltration phenotypes of public datasets. The TCGA (n = 457) and eleven independent melanoma cohorts (n = 758) were used as the training and validation datasets, respectively. We identified two m6A clusters (m6A-clusterA and m6A-clusterB) based on the expression pattern of m6A regulators via unsupervised consensus clustering. IGF2BP1 (7.49%), KIAA1429 (7.06%), and YTHDC1 (4.28%) were the three most frequently mutated genes. There was a correlation between driver genes mutation statuses and the expression of m6A regulators. A significant difference in tumor-associated immune infiltration between two m6A clusters was detected. Compared with m6A-clusterA, the m6A-clusterB was characterized by a lower immune score and immune cell infiltration but higher mRNA expression-based stemness index (mRNAsi). An m6A-related risk signature consisting of 12 genes was determined via Cox regression analysis and divided the patients into low- and high-risk groups (IL6ST, MBNL1, NXT2, EIF2A, CSGALNACT1, C11orf58, CD14, SPI1, NCCRP1, BOK, CD74, PAEP). A nomogram was developed for the prediction of the survival rate. Compared with the high-risk group, the low-risk group was characterized by high expression of immune checkpoints and immunophenoscore (IPS), activation of immune-related pathways, and more enriched in immune cell infiltrations. The low-risk group had a favorable prognosis and contained the potential beneficiaries of the immune checkpoint blockade therapy and verified by the IMvigor210 cohort (n = 298). The m6A-related signature we have determined in melanoma highlights the relationships between m6A regulators and immune cell infiltration. The established risk signature was identified as a promising clinical biomarker of melanoma.

scholarly journals Comprehensive Analysis of Inflammation Response Related Genes and Immune Infiltrates in Osteosarcoma

2021 ◽  
Author(s):  
Weilong Xu ◽  
Wei Niu
Keyword(s):  
High Risk ◽  
Inflammatory Response ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis ◽  
Gene Set

Abstract Background:Osteosarcoma is one of the most common bone malignant tumors in children and young adults. Inflammatory response in the microenvironment which acts as active cross-talk signals between host and tumor may play a vital role in osteosarcoma. In present study, bioinformatics algorithms were applied to establish inflammatory response-related genes (IRG) signature to improve prognosis prediction in osteosarcoma.Methods:Clinical and mRNA expression profiles data of osteosarcoma patients were collected via Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Prognostic values of IRG were evaluated via univariate and LASSO Cox regression analysis and combined to construct risk siganture. Relationship between immune cell infiltration and signature was investigated in present study. Single-sample gene set enrichment analysis was implemented to calculate immune related pathway activity and immune cell infiltration score. Results:We found 17 IRG were correlated with overall survival (OS). From LASSO Cox regression analyses, 11 IRG were identified as candidate genes to combine into risk score formulas. Patients were divided into high and low risk subgroups. patients in low-risk subgroup had a significantly better OS than patients in high-risk according to Kaplan-Meier curve result. In addition, gene set variation analysis of risk stratification may explain the different survival. Immune infiltration result demonstrated that high risk subgroups had lower levels of key antitumor infiltrating immune cells and antitumor immunity.Conclusion:The present study established IRG signature to act as a robust predictor of prognosis and a novel therapeutic target for treatment in osteosarcoma.

scholarly journals Identification of Crucial Methylation Genes Associated With Immune Cell Infiltration in Twin-to-twin Transfusion Syndrome Using Co-methylation Analysis

2021 ◽  
Author(s):  
Chuang Li ◽  
Yuan Wang ◽  
Caixia Liu ◽  
Shaowei Yin
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Interaction Network ◽  
Cell Infiltration ◽  
Future Research ◽  
Regulation Mechanism ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Neurodevelopmental Abnormalities

Abstract Background: DNA methylation (DNAm), is an important transcriptional regulation mechanism, relevant to various diseases. Twin-to-twin transfusion syndrome (TTTS) is a complication in twin pregnancies resulting from disproportionate blood circulation. Survivors of TTTS show a high risk of neurodevelopmental abnormalities, particularly in the hippocampus, which is important in learning and memory. Here, we investigate gene expression and DNAm in hippocampus tissues of TTTS specimens. Methods: DNAm and gene expression levels were compared among the three groups: 10 recipients, 10 donors, and 10 matched control, using methylation microarray. We further explored the immune infiltration of six immune cell sub-populations using EpiDISH analysis. The methylated sites related to immune cell infiltration were identified using the WGCNA package. We explored the core methylation genes in the protein-protein interaction network using the MCODE plugin in Cytoscape software. Results: There were 188 differential methylation sites among three groups. Based on WGCNA, we found that the turquoise module containing 174 CpG sites is significantly related to the immune infiltration level. And four hub genes correlated with immune infiltration level, namely, PTPRJ, FYN, LYN, and AKT1, and were identified using gene sub-network analysis. Conclusions: We identify the four hub methylation genes related to immune infiltration in the TTTS. The molecular function of hub genes is still explored in the future research.

scholarly journals Effects of Hypoxia in Intestinal Tumors on Immune Cell Behavior in the Tumor Microenvironment

2021 ◽  
Vol 12 ◽  
Author(s):  
Luping Zhang ◽  
Shaokun Wang ◽  
Yachen Wang ◽  
Weidan Zhao ◽  
Yingli Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Cell Infiltration ◽  
Cell Behavior ◽  
Immune Cell Infiltration

BackgroundImbalanced nutritional supply and demand in the tumor microenvironment often leads to hypoxia. The subtle interaction between hypoxia and immune cell behavior plays an important role in tumor occurrence and development. However, the functional relationship between hypoxia and the tumor microenvironment remains unclear. Therefore, we aimed to investigate the effect of hypoxia on the intestinal tumor microenvironment.MethodWe extracted the names of hypoxia-related genes from the Gene Set Enrichment Analysis (GSEA) database and screened them for those associated with colorectal cancer prognosis, with the final list including ALDOB, GPC1, ALDOC, and SLC2A3. Using the sum of the expression levels of these four genes, provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the expression coefficients, we developed a hypoxia risk score model. Using the median risk score value, we divided the patients in the two databases into high- and low-risk groups. GSEA was used to compare the enrichment differences between the two groups. We used the CIBERSORT computational method to analyze immune cell infiltration. Finally, the correlation between these five genes and hypoxia was analyzed.ResultThe prognosis of the two groups differed significantly, with a higher survival rate in the low-risk group than in the high-risk group. We found that the different risk groups were enriched by immune-related and inflammatory pathways. We identified activated M0 macrophages in TCGA and GEO databases and found that CCL2/4/5, and CSF1 contributed toward the increased infiltration rate of this immune cell type. Finally, we observed a positive correlation between the five candidate genes’ expression and the risk of hypoxia, with significant differences in the level of expression of each of these genes between patient risk groups.ConclusionOverall, our data suggest that hypoxia is associated with the prognosis and rate of immune cell infiltration in patients with colorectal cancer. This finding may improve immunotherapy for colorectal cancer.

scholarly journals RTP4 is a novel prognosis-related hub gene in cutaneous melanoma

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yiqi Li ◽  
Jue Qi ◽  
Jiankang Yang
Keyword(s):  
Cutaneous Melanoma ◽  
Gene Networks ◽  
Immune Cell ◽  
Cox Model ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Hub Gene ◽  
Rank Analysis ◽  
Prognostic Gene

Abstract Objective Melanoma accounts for 80% of skin cancer deaths. The pathogenesis of melanoma is regulated by gene networks. Thus, we aimed here to identify gene networks and hub genes associated with melanoma and to further identify their underlying mechanisms. Methods GTEx (normal skin) and TCGA (melanoma tumor) RNA-seq datasets were employed for this purpose. We conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with melanoma. Log-rank analysis and multivariate Cox model analysis were performed to identify prognosis genes, which were validated using two independent melanoma datasets. We also evaluated the correlation between prognostic gene and immune cell infiltration. Results The blue module was the most relevant for melanoma and was thus considered the key module. Intersecting genes were identified between this module and differentially expressed genes (DEGs). Finally, 72 genes were identified and verified as hub genes using the Oncomine database. Log-rank analysis and multivariate Cox model analysis identified 13 genes that were associated with the prognosis of the metastatic melanoma group, and RTP4 was validated as a prognostic gene using two independent melanoma datasets. RTP4 was not previously associated with melanoma. When we evaluated the correlation between prognostic gene and immune cell infiltration, we discovered that RTP4 was associated with immune cell infiltration. Further, RTP4 was significantly associated with genes encoding components of immune checkpoints (PDCD1, TIM-3, and LAG3). Conclusions RTP4 is a novel prognosis-related hub gene in cutaneous melanoma. The novel gene RTP4 identified here will facilitate the exploration of the molecular mechanism of the pathogenesis and progression of melanoma and the discovery of potential new target for drug therapy.

scholarly journals Identification of Hub Genes and Their Correlation With Immune Infiltration Cells in Hepatocellular Carcinoma Based on GEO and TCGA Databases

2021 ◽  
Vol 12 ◽  
Author(s):  
Rui Huang ◽  
Jinying Liu ◽  
Hui Li ◽  
Lierui Zheng ◽  
Haojun Jin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Primary Liver Cancer ◽  
Cell Infiltration ◽  
Regulation Mechanism ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Rank Analysis ◽  
Immune Infiltration ◽  
Follicular Helper

Hepatocellular carcinoma (HCC) is a primary liver cancer with extremely high mortality in worldwide. HCC is hard to diagnose and has a poor prognosis due to the less understanding of the molecular pathological mechanisms and the regulation mechanism on immune cell infiltration during hepatocarcinogenesis. Herein, by performing multiple bioinformatics analysis methods, including the RobustRankAggreg (RRA) rank analysis, weighted gene co-expression network analysis (WGCNA), and a devolution algorithm (CIBERSORT), we first identified 14 hub genes (NDC80, DLGAP5, BUB1B, KIF20A, KIF2C, KIF11, NCAPG, NUSAP1, PBK, ASPM, FOXM1, TPX2, UBE2C, and PRC1) in HCC, whose expression levels were significantly up-regulated and negatively correlated with overall survival time. Moreover, we found that the expression of these hub genes was significantly positively correlated with immune infiltration cells, including regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with immune infiltration cells including monocytes. Among these hub genes, KIF2C and UBE2C showed the most significant correlation and were associated with immune cell infiltration in HCC, which was speculated as the potential prognostic biomarker for guiding immunotherapy.

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