Transcriptomics Analysis of Primordium Formation in Pleurotus eryngii

Primordium formation is an important stage preceding the growth and development of the Pleurotus eryngii fruiting body. However, the molecular mechanisms underlying primordium formation remain unclear. In the present study, comparative transcriptomics was performed between mature mycelia and primordium to analyze the transcriptional properties during primordium formation in P. eryngii. A total of 19,655 differentially expressed genes (10,718 upregulated genes and 8937 downregulated genes) were identified. These differentially expressed genes were involved in cell wall degradation, carbohydrate hydrolysis, light perception, and cAMP signal transduction. These results aid further understanding of the transcriptional changes and the molecular processes underlying primordium formation and differentiation, which may lay the foundation for improving the cultivation and quality control of P. eryngii.

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Molecular mechanisms detected in yak lung tissue via transcriptome-wide analysis provide insights into adaptation to high altitudes

Scientific Reports ◽

10.1038/s41598-021-87420-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Qianyun Ge ◽

Yongbo Guo ◽

Wangshan Zheng ◽

Shengguo Zhao ◽

Yuan Cai ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Lung Tissue ◽

Molecular Mechanisms ◽

Differentially Expressed ◽

P Value ◽

High Altitudes ◽

Transcriptional Changes ◽

Nonlinear Regulation ◽

Different Altitudes

AbstractDue to their long-term colonization of and widespread distribution in plateau environments, yaks can serve as an ideal natural animal model for the adaptive evolution of other plateau species, including humans. Some studies reported that the lung and heart are two key organs that show adaptive transcriptional changes in response to high altitudes, and most of the genes that show differential expression in lung tissue across different altitudes display nonlinear regulation. To explore the molecular mechanisms that are activated in yak lung tissue in response to hypoxia, the mRNAs, lncRNAs and miRNAs of lung tissue from 9 yaks living at three different altitudes (3400 m, 4200 m and 5000 m), with three repetitions per altitude, were sequenced. Two Zaosheng cattle from 1500 m were selected as low-altitude control. A total of 21,764 mRNAs, 14,168 lncRNAs and 1209 miRNAs (305 known and 904 novel miRNAs) were identified. In a comparison of yaks and cattle, 4975 mRNAs, 3326 lncRNAs and 75 miRNAs were differentially expressed. A total of 756 mRNAs, 346 lncRNAs and 83 miRNAs were found to be differentially expressed among yaks living at three different altitudes (fold change ≥ 2 and P-value < 0.05). The differentially expressed genes between yaks and cattle were functionally enriched in long-chain fatty acid metabolic process and protein processing, while the differentially expressed genes among yaks living at three different altitudes were enriched in immune response and the cell cycle. Furthermore, competing endogenous RNA (ceRNA) networks were investigated to illustrate the roles of ceRNAs in this process, the result was also support the GO and KEGG analysis. The present research provides important genomic insights for discovering the mechanisms that are activated in response to hypoxia in yak lung tissue.

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Blood Transcriptional Profiling in Patients with Leukemic Cutaneous T-Cell Lymphoma on Extracorporeal Photopheresis Reveals the Integrin Signaling As the Top Pathway Associated with Clinical Response

Blood ◽

10.1182/blood.v126.23.3981.3981 ◽

2015 ◽

Vol 126 (23) ◽

pp. 3981-3981

Author(s):

Zuolin Ying ◽

Madeleine Duvic ◽

Lisa Shiue ◽

Timothy Langridge ◽

Meghali Goswami ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Cell Lymphoma ◽

Transcriptional Profiling ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Integrin Signaling ◽

Canonical Pathways ◽

Rho Family ◽

Upregulated Genes

Abstract Extracorporeal photopheresis (ECP) is an effective frontline therapy for patients with leukemic cutaneous T-cell lymphoma (L-CTCL), but the mechanisms of action are not fully understood. To elucidate molecular mechanisms underlying the efficacy of ECP, we used Agilent Whole Human Genome Microarrays to examine blood transcriptional profiles in L-CTCL patients after ECP therapy. Ten L-CTCL patients including 5 clinical responders and 5 non-responders were studied. Their peripheral blood was collected before ECP (baseline), at Day 2, and one month post-ECP. Total RNA extracted from peripheral blood mononuclear cells was assayed with Whole Human Genome Oligo Microarrays (4 × 44 K) (Agilent, Santa Clara, CA). The differentially expressed gene analysis (DGA) was done using the paired t-test with Benjamini- Hochberg correction (P value < 0.05) between post-ECP and baseline. The fold change of gene expression between post-ECP and baseline were calculated from the normalized values. Hierarchical clustering of differentially expressed genes was performed with the Pearson correlation. The DGA between responders and non-responders were cross-compared. Canonical biological pathways were identified using Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Redwood City, CA). Differentially expressed gene profiles were different in responders from non-responders. As indicated in Figure 1, there were more genes differentially regulated in responders than in non-responders post-ECP at both Day 2 (549 genes in responders versus 66 genes in non-responders) and at one month (472 genes in responders versus 95 genes in non-responders). Among 472 differentially expressed genes in responders at one month post-ECP, almost twice as many genes (313) were down-regulated compared to up-regulated genes (159). The top down-regulated genes were IL-1β, EGR1, CCL3, CCL3L3, and CXCL2. The down-regulated genes were mainly related to functions of platelets, immune and/or stress responses, and chromatin remodeling. The upregulated genes were mainly related to functions of the nucleolus and included USP34, POLR3F, ZNF529, C22orf35, and BAT2D1. The ingenuity pathway analysis revealed that the top 5 pathways affected by ECP at one-month in responders were 1) integrin signaling; 2) granulocyte adhesion and diapedesis; 3) signaling by Rho Family GPTases; 4) agranulocytes (lymphocyte, monocyte and macrophage) adhesion and diapedesis; and 5) triggering receptor expressed on myeloid cells 1 (TREM1) signaling (Table 1). In contrast, these pathways and genes were less affected in non-responders. Of note, a comparison of all DGA results indicated that the responder group overlapped in the differentially expressed genes between Day 2 group (RD2) and one month group (RM1), but had few genes in common to the non-responder group (NM1). There were 94 genes consistently downregulated among RD2 and RM1 while only 6 genes were found in common between the RM1 and NM1 group. Similarly, 61 genes were consistently upregulated in group RD2 and RM1 while only 3 genes were found in common between the RM1 and NM1 group. In summary, the blood transcriptional profiling by this study identifies a signature of genes and pathways relevant to clinical response to ECP in L-CTCL patients. These findings expand our understanding of molecular mechanisms of ECP. Further validation of these genes and pathways is warranted in the future studies. Table 1. Top canonical pathways affected by ECP in L-CTCL patients responded to ECP at one-month Canonical Pathways Downregulated genes Upregulated genes Integrin Signaling 15/201 (7%) ITGA2B, MAP3K11, ITGA5, MYLK, ITGB3, MYL9, PARVB, AKT1, RHOB, CAPN1, ACTN4, CTTN, ARPC4, ACTN1, ITGB5 2/201 (1%) ITGB1, PPP1R12A Granulocyte Adhesion and Diapedesis 14/179 (8%) CSF3R, ICAM1, PPBP,ITGA5, CXCL5, SDC4, CCL3, ITGB3, GNAI2, CLDN5, CCL3L3, IL1B, CXCL1, CXCL2 1/179 (1%) ITGB1 Signaling by Rho Family GTPases 13/236 (6%) SEPT5, MAP3K11, ITGA5, MYLK, GNAZ, CDC42EP2, GNAI2, MYL9, GNG11, GNA15, RHOB, GNB2, ARPC4 3/236 (1%) ITGB1, DIAPH3, PPP1R12A Agranulocyte Adhesion and Diapedesis 13/190 (7%) ICAM1, PPBP, ITGA5, CXCL5, SDC4, CCL3, GNAI2, MYL9, CLDN5, CCL3L3, IL1B, CXCL1, CXCL2 1/190 (1%) ITGB1 TREM1 Signaling 7/76 (9%) ICAM1, AKT1, NLRP12, ITGA5, IL1B, CD83, CCL3 2/76 (3%) ITGB1, NLRC3 Figure 1. Differentially expressed genes post-ECP between responders and non-responders Figure 1. Differentially expressed genes post-ECP between responders and non-responders Disclosures Duvic: Therakos: Research Funding. Ni:Therakos: Research Funding.

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Identification of Differentially Expressed Genes Associated with Papillary Thyroid Carcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200402085832 ◽

2020 ◽

Vol 23 (6) ◽

pp. 546-553

Author(s):

Hongyuan Cui ◽

Mingwei Zhu ◽

Junhua Zhang ◽

Wenqin Li ◽

Lihui Zou ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Cellular Proliferation ◽

Mrna Level ◽

Thyroid Tissue ◽

Differentially Expressed ◽

Thyroid Tumors ◽

Papillary Thyroid

Objective: Next-generation sequencing (NGS) was performed to identify genes that were differentially expressed between normal thyroid tissue and papillary thyroid carcinoma (PTC). Materials & Methods: Six candidate genes were selected and further confirmed with quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry in samples from 24 fresh thyroid tumors and adjacent normal tissues. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used to investigate signal transduction pathways of the differentially expressed genes. Results: In total, 1690 genes were differentially expressed between samples from patients with PTC and the adjacent normal tissue. Among these, SFRP4, ZNF90, and DCN were the top three upregulated genes, whereas KIRREL3, TRIM36, and GABBR2 were downregulated with the smallest p values. Several pathways were associated with the differentially expressed genes and involved in cellular proliferation, cell migration, and endocrine system tumor progression, which may contribute to the pathogenesis of PTC. Upregulation of SFRP4, ZNF90, and DCN at the mRNA level was further validated with RT-PCR, and DCN expression was further confirmed with immunostaining of PTC samples. Conclusion: These results provide new insights into the molecular mechanisms of PTC. Identification of differentially expressed genes should not only improve the tumor signature for thyroid tumors as a diagnostic biomarker but also reveal potential targets for thyroid tumor treatment.

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Analysis of Transcriptional Changes in Different Brassica napus Synthetic Allopolyploids

Genes ◽

10.3390/genes12010082 ◽

2021 ◽

Vol 12 (1) ◽

pp. 82

Author(s):

Yunxiao Wei ◽

Guoliang Li ◽

Shujiang Zhang ◽

Shifan Zhang ◽

Hui Zhang ◽

...

Keyword(s):

Gene Expression ◽

Brassica Napus ◽

Differentially Expressed Genes ◽

Expression Patterns ◽

Female Parent ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Transcriptional Changes ◽

Aa Genome ◽

High Degree

Allopolyploidy is an evolutionary and mechanistically intriguing process involving the reconciliation of two or more sets of diverged genomes and regulatory interactions, resulting in new phenotypes. In this study, we explored the gene expression patterns of eight F2 synthetic Brassica napus using RNA sequencing. We found that B. napus allopolyploid formation was accompanied by extensive changes in gene expression. A comparison between F2 and the parent shows a certain proportion of differentially expressed genes (DEG) and activation\silent gene, and the two genomes (female parent (AA)\male parent (CC) genomes) showed significant differences in response to whole-genome duplication (WGD); non-additively expressed genes represented a small portion, while Gene Ontology (GO) enrichment analysis showed that it played an important role in responding to WGD. Besides, genome-wide expression level dominance (ELD) was biased toward the AA genome, and the parental expression pattern of most genes showed a high degree of conservation. Moreover, gene expression showed differences among eight individuals and was consistent with the results of a cluster analysis of traits. Furthermore, the differential expression of waxy synthetic pathways and flowering pathway genes could explain the performance of traits. Collectively, gene expression of the newly formed allopolyploid changed dramatically, and this was different among the selfing offspring, which could be a prominent cause of the trait separation. Our data provide novel insights into the relationship between the expression of differentially expressed genes and trait segregation and provide clues into the evolution of allopolyploids.

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Identification of Differentially Expressed Genes in Porcine Ovaries at Proestrus and Estrus Stages Using RNA-Seq Technique

BioMed Research International ◽

10.1155/2018/9150723 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Songbai Yang ◽

Xiaolong Zhou ◽

Yue Pei ◽

Han Wang ◽

Ke He ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Patterns ◽

Hormone Secretion ◽

Differentially Expressed ◽

Receptor Interaction ◽

The Novel ◽

Kegg Pathways ◽

Go Enrichment ◽

Single Organism

Estrus is an important factor for the fecundity of sows, and it is involved in ovulation and hormone secretion in ovaries. To better understand the molecular mechanisms of porcine estrus, the expression patterns of ovarian mRNA at proestrus and estrus stages were analyzed using RNA sequencing technology. A total of 2,167 differentially expressed genes (DEGs) were identified (P≤0.05, log2 Ratio≥1), of which 784 were upregulated and 1,383 were downregulated in the estrus compared with the proestrus group. Gene Ontology (GO) enrichment indicated that these DEGs were mainly involved in the cellular process, single-organism process, cell and cell part, and binding and metabolic process. In addition, a pathway analysis showed that these DEGs were significantly enriched in 33 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including cell adhesion molecules, ECM-receptor interaction, and cytokine-cytokine receptor interaction. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) confirmed the differential expression of 10 selected DEGs. Many of the novel candidate genes identified in this study will be valuable for understanding the molecular mechanisms of the sow estrous cycle.

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Comparative Transcriptomic Analysis of Embryo Implantation in Mice and Rats

Cellular Physiology and Biochemistry ◽

10.1159/000494187 ◽

2018 ◽

Vol 50 (2) ◽

pp. 668-678 ◽

Cited By ~ 1

Author(s):

Wen-Qian Zhang ◽

Miao Zhao ◽

Ming-Yu Huang ◽

Ji-Long Liu

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Embryo Implantation ◽

Differentially Expressed ◽

Rat Uterus ◽

Mouse Uterus ◽

Pathway Network ◽

Implantation Sites ◽

High Selection ◽

Transcriptomic Changes

Background/Aims: Embryo implantation is an essential process for eutherian pregnancy, but this process varies across eutherians. The genomic mechanisms that led to the emergence and diversification of embryo implantation are largely unknown. Methods: In this study, we analyzed transcriptomic changes during embryo implantation in mice and rats by using RNA-seq. Bioinformatics and evolutionary analyses were performed to characterize implantation-associated genes in these two species. Results: We identified a total of 518 differentially expressed genes in mouse uterus during implantation, of which 253 genes were up-regulated and 265 genes were down-regulated at the implantation sites compared with the inter-implantation sites. In rat uterus, there were 374 differentially expressed genes, of which 284 genes were up-regulated and 90 genes were down-regulated. A cross-species comparison revealed that 92 up-regulated genes and 20 down-regulated genes were shared. The differences and similarities between mice and rats were investigated further at the gene ontology, pathway, network, and causal transcription factor levels. Additionally, we found that embryo implantation might have evolved through the recruitment of ancient genes into uterine expression. The evolutionary rates of the differentially expressed genes in mouse and rat uterus were significantly lower than those of the non-changed genes, indicating that implantation-related genes are evolutionary conserved due to high selection pressure. Conclusion: Our study provides insights into the molecular mechanisms involved in the evolution of embryo implantation.

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Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽

10.1186/s41065-021-00190-0 ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Haoming Li ◽

Linqing Zou ◽

Jinhong Shi ◽

Xiao Han

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differentially Expressed Genes ◽

Entorhinal Cortex ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Neurodegenerative Disorder ◽

Early Stage ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

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Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

PeerJ ◽

10.7717/peerj.8096 ◽

2019 ◽

Vol 7 ◽

pp. e8096 ◽

Cited By ~ 2

Author(s):

Haiping Zhang ◽

Jian Zou ◽

Ying Yin ◽

Bo Zhang ◽

Yaling Hu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Differentially Expressed Genes ◽

Renal Cell ◽

Molecular Mechanisms ◽

Clear Cell ◽

Clinical Stage ◽

Stage I ◽

Differentially Expressed ◽

Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

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Insight into Molecular Profile Changes after Skeletal Muscle Contusion Using Microarray and Bioinformatics Analyses

10.21203/rs.3.rs-85830/v1 ◽

2020 ◽

Author(s):

Na Li ◽

Ru-feng Bai ◽

Chun Li ◽

Li-hong Dang ◽

Qiu-xiang Du ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Healing Process ◽

Differentially Expressed ◽

Molecular Profile ◽

Wound Age ◽

Functional Modules

Abstract Background: Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. This study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process.Methods: A total of 33 rats were divided randomly into control (n = 3), mild contusion (n = 15), and severe contusion (n = 15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n = 3 per subgroup). Then full genome microarray of RNA isolated from muscle tissue was performed to access the gene expression changes during healing process.Results: A total of 2,844 and 2,298 differentially expressed genes were identified in the mild and severe contusion groups, respectively. The analysis of the overlapping differentially expressed genes showed that there are common mechanisms of transcriptomic repair of mild and severe contusion within 48 h post-contusion. This was supported by the results of principal component analysis, hierarchical clustering, and weighted gene co‐expression network analysis of the 1,620 coexpressed genes in mildly and severely contused muscle. From these analyses, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. We then performed an analysis of the functions of genes (including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation, and protein–protein interaction network analysis) in the functional modules and temporal clusters, and the hub genes in each module–cluster pair were identified. Interestingly, we found that genes downregulated within 24−48 h of the healing process were largely associated with metabolic processes, especially oxidative phosphorylation of reduced nicotinamide adenine dinucleotide phosphate, which has been rarely reported. Conclusions: These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

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Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

10.21203/rs.3.rs-123214/v1 ◽

2020 ◽

Author(s):

Xiang Zhou ◽

Keying Zhang ◽

Fa Yang ◽

Chao Xu ◽

Jianhua Jiao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Wilcoxon Test ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

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