Screening of common key immune genes and prediction of potential drugs in chronic hepatic injury

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Differentially expressed genes (DEGs) between CHI including Alcoholic hepatitis (AH), Nonalcoholic fatty liver disease (NAFLD), Hepatitis C (HC), and Hepatitis B (HB) and related Control samples were detected by differential analysis. Then, 77 latent genes of CHI were intersected with IRGs to obtain DEGs for generating a Protein-protein interaction (PPI) network to screen out 5 key genes consisting of secreted phosphor protein 1 (SPP1), Chemokine (C-X-C motif) ligand (CXCL10), Chemokine (C-C motif) ligand 20 (CCL20), Annexin A2 (ANXA2), and lectin galactoside-binding soluble 3 (LGALS3). Besides, we found that CXCL10 was regulated by a natural compound named quercetin, and there were 187 herbs with it as the main component. TFs-mRNA network identified that Forkhead box C1 (FOXC1) could regulate 4 key genes including CCL20, SSP1, ANXA2, and LGALS3. Therefore, this could provide references for CHI treatments and further studies. Immune related genes Chronic hepatic injury Protein-protein interaction network TFs-mRNA network Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Chronic hepatic injury (CHI) refers to the pathological process of liver function damage under the influence of alcohol, virus, drugs and other reasons for a long time (Tang et al. 2012 ).The main features include oxidative stress, apoptosis, inflammatory response and so on (Ramana et al. 2010; Srivastava et al. 2005 ; Maccari et al. 2015). In the early stage of CHI, the liver function is slightly damaged, the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and bilirubin in serum are obvious increased, and the clinical symptoms are not obvious. If there is no treat in time, CHI would gradually aggravate, resulting in severe damage to liver cells, irreversible formation of fibrosis, characteristic pseudolobule lesions, development of cirrhosis, and even formation of liver malignant tumors, endangering life and health (Pierantonelli et al. 2015). The most common type of liver disease in China is chronic hepatitis B (HB), with 86 million people infected with chronic HB virus (HBV) (Deng et al. 2011 ). Accordingly, the prevalence of Nonalcoholic Fatty Liver Disease (NAFLD) in Shanghai, Chengdu, and Guangdong was 15.0%, 12.5%, and 17.0% respectively (Masand et al. 2019 ). In a retrospective study, the annual incidence of drug-induced liver injury in the general population in mainland China was estimated to be 23.8 / 100,000 (Thakur et al. 2021 ). In 2015, about 160,000 people died of alcoholic liver disease in China, and the prevalence of alcoholic liver disease in some areas of China was about 0.5% -8.55% (Thayumanavan et al. 2018 ). CHI usually causes liver fibrosis. The prognosis of patients with advanced liver fibrosis is usually poor, since it often develops into decompensated cirrhosis and liver cancer (Tsochatzis et al. 2014 ). NAFLD is usually caused by obesity. When fat accumulates around the liver tissue, it will cause hepatic steatosis and cytotoxicity, leading to hepatocyte reaction, then hematopoietic stem cell (HSC) transformation into fibroblasts, and extracellular matrix (ECM) proliferation leading to nonalcoholic liver fibrosis (Kumar et al. 2021 ). Viral hepatitis is mainly caused by HBV and hepatitis C virus (HCV). HB is transported to the liver cells through the receptor, and the nucleocapsid of the viral deoxyribonucleic acid (DNA) is encapsulated into the liver nucleus. Then the viral DNA is released and integrated with the host DNA, and the viral protein is expressed after transcription to complete the HBV infection (Tsai. 2021). Persistent viral infections cause liver cell damage, leading to liver fibrosis (Gerold et al. 2020 ; Biliotti et al. 2021 ). In the long run, heavy drinking will lead to the accumulation of acetaldehyde, which is the decomposition product of ethanol and it can cause metabolic disorders of hepatocytes and liver inflammation (Patel et al. 2021 ). However, in the process of liver fibrosis caused by different factors such as viral hepatitis, autoimmune Hepatitis (AH) and NAFLD, whether they have common associated genes has not been reported. At present, many immune cells have been reported to be related to cellular liver injury. Kupffer cells respond to lipopolysaccharide (LPS) through TLR4 to produce various inflammatory cytokines, including TNF-α, IL-1β, IL-6, IL-12, IL-18, as well as granulomatous liver disease, ischemia reperfusion liver injury, NAFLD and alcoholic chemokine liver disease (Seki et al. 2001 ). A study has shown that a group of macrophages recruited from peritoneal fluid, its F4 / 80 hi GATA6 + and rapidly penetrate through the mesothelium into the injured liver (Wang et al. 2016). The hyaluronic acid expressed by sinusoidal endothelial cells interacts with CD44 on the surface of neutrophils, which plays a key role in the recruitment of neutrophils to the liver (McDonald et al. 2008 ). After liver injury, dendritic cell (DCs) acquired the ability to induce inflammation mediated by hepatic stellate cells, NK cells and T cells (Tokita et al. 2008 ). DCs also participate in the regression of fibrosis after liver injury by producing MMP9 (Jiao et al. 2012 ). Th17 cells are pro-inflammatory cells that produce IL-17 cytokines, which are associated with promoting liver inflammation and fibrosis (Lemmers et al. 2009 ; Ge et al. 2010 ; Meng et al. 2012 ). However, immune-related genes have not been reported as diagnostic features of liver injury. In this study, we screened the differentially expressed genes (DEGs) in viral hepatitis (HB and HC), AH, and NAFLD, and intersected with Immune related genes (IRGs). The Protein-protein interaction (PPI) network was used to screen the key genes based on four liver injury diseases, and the drugs for liver injury were predicted eventually, providing a new idea and theoretical basis for the treatment of CHI. 2. Materials and methods 2.1 Collection of CHI datasets CHI related RNA sequencing data (RNA-seq) were sourced from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE28619 microarray of AH included 7 Control and 15 AH samples, GSE126848 microarray of NAFLD contained 14 Control and 15 NAFLD samples, GSE6764 microarray of HC had 10 Control and 13 HC samples, and GSE84044 microarray of HB consisted of 63 mild fibrosis (F0-F1, used as Control) and 28 severe fibrosis samples (F3-F4). The four datasets above were applied as training sets, and we downloaded 4 datasets to be used as validation sets as follows. GSE142530 dataset of AH contained 12 Control and 10 AH samples, GSE49541 microarray of NAFLD had 40 mild fibrosis (F0-F1, used as Control) and 32 severe fibrosis samples (F3-F4), GSE14323 microarray of HC had 9 Control and 41 HC samples, and GSE83148 included 6 Control and 122 HB samples. Inaddition, 2519 IRGs were obtained from ImmPort and InnateDB databases ( https://www.immport.org/shared/home , https://www.innatedb.ca/ ). 2.2 Differential analyses between Control and CHI samples We applied the “limma” package (version 3.50.1) to sift out DEGs between Control and CHI samples, the CHI samples contained AH, NAFLD, HC, and HB samples (Ritchie et al. 2015 ). The top 10 DEGs (AH, NAFLD, HC), and HB with the highest positive or negative regulation degree were visualized by the “ggplot2” package (version 3.3.5) in volcano plots and heatmaps eventually (Ito et al. 2013). Then, the RRA algorithm was applied to intersect the DEGs (AH, NAFLD, HC, and HB) and sift out genes with a higher regulation degree according to p 3. Those genes screened out were defined as latent genes of CHI. In addition, we conducted enrichment analysis of latent genes by the “clusterProfiler” package (version 4.2.2) with p < 0.05 based on the GO and KEGG (Wu et al. 2021 ). 2.3 PPI network was applied to screen key genes Latent genes were intersected with IRGs to obtain Differentially expressed IRGs (DE-IRGs). Those DE-IRGs were applied to generate a PPI network via the STRING database with medium confidence > 0.4 ( https://string-db.org/ ). After eliminating outlier genes, we sifted out genes with a higher degree of more than 2 to define them as key genes. On the basis of the four validation sets, we conducted Receiver operating characteristic (ROC) analysis to evaluate the prognostic prediction performance of key genes and contrasted their expression in different types of CHI samples. 2.4 Prediction of potential drugs and transcription factors (TFs) of key genes Potential drugs of CHI were predicted using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database ( https://old.tcmsp-e.com/index.php ), and the herbs with them as the main component were obtained. Then, we downloaded the PDB format document of key genes and conducted a molecular docking process between drugs and key genes. The result was visualized by the “PyMol” tool. Next, TFs correlated with key genes were detected via the networkanalys database ( https://www.networkanalyst.ca/ ) and applied to generate a TFs-mRNA network by the software “cytoscape” (version 3.8.2) (Shannon et al. 2003 ). 3. Results 3.1 77 latent genes were screened out by differential analyses Based on the four training sets, 2718 DEGs between Control and AH samples ( Fig. 1 A, B ) (Table S1) , 3744 DEGs between Control and NAFLD samples ( Fig. 1 C, D ) (Table S2) , 1733 DEGs between Control and HC samples ( Fig. 1 E, F ) (Table S3) , and 548 DEGs between Control and HB samples ( Fig. 1 G, H ) (Table S4) were sifted out by differential analysis. After an intersection process among those four kinds of DEGs, we obtained 77 latent genes of CHI and visualized their regulation degree in a heatmap ( Fig. 2 A ) . Enrichment analysis identified that latent genes were enriched in 561 GO-Biological process (GO-BP) functions including collagen fibril organization, cellular hormone metabolic process, etc., 44 GO-Cellular components (GO-CC) functions containing complex of collagen trimers, endoplasmic reticulum lumen, etc., 69 GO-Molecular functions (GO-MF) functions consisting of cadherin binding involved in cell-cell adhesion, chemokine receptor binding, etc. ( Fig. 2 B ) (Table S5) , and 15 KEGG pathways including Fructose and mannose metabolism, Pentose and glucuronate interconversions, etc. ( Fig. 2 C ) (Table S6) . 3.2 5 key genes were obtained via the PPI network Latent genes were intersected with IRGs to procure 25 DE-IRGs, and those were applied to generate a PPI network ( Fig. 3 A, B ) . We eliminated the outlier genes and picked 5 genes (SPP1, CXCL10, CCL20, ANXA2, and LGALS3) with a higher degree of more than 2 and defined them as key genes of CHI ( Fig. 3 C, D ) . It could be found in the ROC curves that all of the Area under the curve (AUC) values in GSE142530, GSE14323, and GSE83148 were higher than 0.8, and those in GSE49541 were higher than 0.7 ( Fig. 3 E ) . Besides, the expression of key genes in AH, NAFLD, HC, and HB was remarkably higher than that in Control samples ( Fig. 3 F ) . 3.3 Quercetin could regulate the expression of key genes The potential drugs prediction analysis identified that the key gene CXCL10 was regulated by a natural compound named quercetin, and then we found that there were 187 herbs with it as the main component, such as Polygoni Avicularis Herba, Alpinia Katsumadai Hayat, Phellodendri Amurensis Cortex, etc. ( Fig. 4 A ) . The minimum binding energy between CXCL10 and quercetin was − 6.7 kcal/mol, the value of it was lower than − 5 kcal/mol, which demonstrated that the combined affinity between CXCL10 and quercetin was preferably strong ( Fig. 4 B, C ) . In addition, we generate a TFs-mRNA network and found that FOXC1 could regulate 4 key genes including CCL20, SSP1, ANXA2, and LGALS3 ( Fig. 4 D ) . 4. Discussion The liver is an important metabolic organ that regulates the metabolic homeostasis of the human body. It can remove many harmful chemicals and drugs from the body. Based on these important functions, the liver is also vulnerable to these substances (Liu et al. 2018 ). Liver injury is not obvious in the early stage, but often leads to higher mortality in severe cases. Studies have shown that the type of liver injury, such as chemical liver injury, viral liver injury, alcoholic liver injury, and drug-induced liver injury (DILI). The main pathological changes include liver fibrosis, steatosis, cirrhosis, and liver cancer (Knight et al. 2007 ). Clinically, CHI includes chronic HB, chronic HC, AH, NAFLD, DILI, etc. (Younossi et al. 2018 ). There is still great difficulty in the treatment and prognosis of CHI, and even it has deteriorated into liver cancer when the disease is found. Obviously, it is particularly important to find out the key genes of CHI and predict the therapeutic drugs. In this study, bioinformatics methods were used to find out the key genes affecting the prognosis of CHI, which provided important guiding significance for improving the diagnosis and prognosis of CHI. In this study, five key genes of CHI (SPP1, CXCL10, CCL20, ANXA2, and LGALS3) were screened and verified by ROC. SPP1, also known as osteopontin, is a secreted glycoprotein that can affect the adhesion, proliferation, differentiation, migration and survival of many types of tumor cells (Wang et al. 2020 ). SPP1 is highly expressed in human hepatocellular carcinoma and adjacent tissues, which is a risk factor and potential marker for the prognosis of patients with hepatocellular carcinoma, and the prognosis of patients with high SPP1 expression is relatively poor (Rong et al. 2019 ; Chen et al. 2019 ). Chemokines are basic proteins that play a role through a subset of G protein-coupled receptors and cytokine families, and are mainly involved in leukocyte migration in inflammatory responses (Vassilatis et al. 2003 ). CXCL10, also known as γ-interferon-inducible protein, can activate CXCR3 + immune NK cells, mononuclear macrophages, etc., and plays an important role in antiviral and anti-tumor processes (Van et al. 2015). Studies have shown that CXCL10, as an anti-inflammatory regulator, is significantly up-regulated in liver injury tissues caused by NAFLD and HB, and plays a key role in its pathological process and can be used as a biomarker for these two types of CHI (Zhang et al. 2014 ; Rot et al. 2004). CCL20 is a small molecule protein that is physiologically expressed in the liver and it can participate in the inflammatory response as well as tissue homeostasis of liver tissue (Hromas et al. 1997 ; Hieshima et al. 1991). In addition, in lipopolysaccharide (LPS) -induced liver injury tissues, the up-regulation of CCL20 is closely related to LPS, which is a biomarker for predicting the prognosis of patients with AH and an important medium for linking AH inflammation, injury and fibrosis (Affò et al. 2014 ). ANXA2 is a member of the connexin family, which is involved in cell proliferation and apoptosis, and is closely related to liver injury-related diseases (Dong et al. 2014 ). Meanwhile, ANXA2 is a potential biomarker for liver injury or liver fibrosis, which is significantly up-regulated in the serum of patients with chronic HB and AH (Kolgelier et al. 2015 ; Zhang et al. 2010 ). And the overexpression of ANXA2 in the liver can inhibit the expansion of liver injury and reduce the progression of liver fibrosis (Yang et al. 2017 ; Dadhania et al. 2016 ). LGALS3 belongs to the carbohydrate binding protein of the non-integrin β-galactoside binding lectin family, and its up-regulation is related to NAFLD (Barondes et al. 1994 ; Chalasani et al. 2019 ; Azevedo et al. 2020; Ochieng et al. 1994 ). Studies have shown that LGALS3 is significantly increased in liver inflammation, fibrosis and cancer, and is involved in its apoptosis, migration, adhesion, angiogenesis and inflammatory response (Ochieng et al. 1994 ; Yang et al. 1996 ). Therefore, we speculate that in liver injury tissues, the body can regulate CHI by up-regulating the expression of these five key genes, thereby regulating liver inflammation, apoptosis, differentiation and other mechanisms. In our study, quercetin, a targeted drug for the key gene CXCL10, and 187 herbs with active ingredients were predicted. Quercetin has the effects of anti-inflammatory, anti-oxidation and scavenging free radicals, and it is a tyrosine protein kinase inhibitor and is one of the most common flavonoids, too (Manach et al. 1995 ). Studies have shown that quercetin can effectively down-regulate the M1 macrophage immune-related factor CXCL10 related to the liver Th1 immune response, thereby regulating the inflammatory response (Oo et al. 2010 ; Tsai et al. 2023). In addition, quercetin can also inhibit hepatic stellate cells (HSC) activation by reducing the levels of inflammatory factors (CXCL10 and heparin-binding cytokines), inhibit the proliferation of aHSCs and heptotocytes, and down-regulate protein molecules which related to metabolism, survival, cytokinesis and protein folding, inhibit liver cell growth, thereby controlling the progression of liver fibrosis (Wu et al. 2011 ). At the same time, it is a potential effective drug for the treatment of NAFLD by activating the farnesoid X receptor 1 / Takeda G-protein-coupled receptor 5 signaling pathway (FXR1 / TGR5 signaling pathways) to participate in the regulation of NAFLD-induced lipid metabolism, oxidative stress and inflammatory response, and reduce liver lipid accumulation (Yang et al. 2019 ). Meanwhile, by activating nuclear NF-E2-related factor 2 (Nrf2) and inducing antioxidant response element (ARE) genes, liver detoxification enzymes are significantly activated to alleviate alcohol-induced oxidative stress, glutathione depletion and pro-inflammatory cytokines in HepG2 cells (Lee et al. 2019 ). And it has a good protective effect against alcohol-induced liver injury in vitro and can be used to improve liver diseases such as AH. For viral hepatitis, quercetin can inhibit the replication of viral cell genomic DNA, inhibit the production of reactive oxygen species and nitrogen free radicals ( ROS / RNS ) and lipid peroxidation, thereby inhibiting the replication of liver injury cells (Cheng et al. 2015 ; Khachatoorian et al. 2012 ; Pisonero-Vaquero et al. 2014 ). Therefore, we speculate that quercetin can affect the proliferation, differentiation and metabolism of liver injury cells by affecting immune-related factors and inflammatory factor CXCL10, affecting lipid metabolism, oxidative stress and inflammatory response mechanisms, providing a new predictive target for CHI treatment. Based on these key genes, this paper predicts a large number of TFs that regulate key genes. In HB, specificity protein 1 (Sp1) has been shown to bind to several sites in the HB genome, and the interaction between Sp1 and covalently closed circular DNA (cccDNA) shows a direct effect on HB replication (Turton et al. 2020 ). Sp1 is also associated with a series of cellular processes such as angiogenesis, apoptosis and cell cycle (Chu et al. 2005 ). As a regulator of HB gene expression, Sp1 can participate in the regulation of HB gene expression through nuclear factor-κB (NF-κB) signaling pathway (Li et al. 2001). NF-κB is a regulator of innate and adaptive cellular immune response, which is involved in angiogenesis, apoptosis, cell proliferation, migration and other cellular processes (Taniguchi et al. 2018). Meanwhile, NF-κB can inhibit the transcription of HB genome and activate oxidative stress in liver cells (Wilson et al. 2011 ; Waris et al. 2001 ). In addition, HB infects hepatocytes and persists in the form of covalently closed cccDNA. Protein spliceosome associated factor 1 (SART1) limits the transcription and replication of HB covalently closed cccDNA by inhibiting the key TF hepatocyte nuclear factor 4 alpha (HNF4α) in hepatocytes (Teng et al. 2021 ). Members of the tripartite motif (TRIM) protein family are antiviral components of the innate immune system and are strongly induced by interferons (IFNs) (Yap et al. 2012). TRIM proteins are involved in a variety of diseases and regulate cell signal transduction, protein quality control, transcription, cell cycle, apoptosis and development (Watanabe et al. 2017). TRIM56 is a key antiviral immune effector molecule that exerts anti-HB activity through the NF-κB signaling pathway, which is essential for inhibiting the transcription of HB covalently closed cccDNA (Tian et al. 2022 ). TRIM22 has also been shown to be a direct target gene of miR-215 and a natural antiviral effector (Gao et al. 2009 ). MiR-215 targets TRIM22 to block the NF-κB pathway and positively regulates HC replication, providing a new potential target for HC infection (Tian et al. 2018). HC infection affects the binding of hepatocyte TF Forkhead box A1 (FOXA1), Forkhead box A2 (FOXA2) and HNF4α to homologous sites in the genome, thereby inhibiting their DNA methylation and regulating the expression of HC-infected hepatocytes (Wijetunga et al. 2017 ). By regulating the TFs Forkhead box C1 (FOXO1) and FOXA2, HC reduces lipid accumulation in liver cells, increases β-oxidation of liver cells, and reduces the replication of HCV genome. It is one of the effective methods for the treatment of chronic hepatitis infected with HC (Bose et al. 2014 ). At the same time, FOXA2 inhibits the induction of late adipocyte differentiation markers such as Peroxisome proliferator-activated receptor gamma (PPARγ) and APETALA2 (AP2) by blocking the adipogenic differentiation of preadipocytes (Wolfrum et al. 2003 ). The absence of FOXA2 leads to a significant decrease in the expression of liver endoderm transcription products such as Albumin (ALB) and TFs HNF4α and Hematopoietically expressed homeobox (HHEX) involved in liver development, which reduces the occurrence and development of NAFLD lipids (Genga et al. 2019 ). The patatin-like phospholipase domain-containing 3 (PNPLA3) is the main determinant of liver fat content and its development. It can increase the accumulation of mutant proteins, reduce the lipase activity on hepatocyte lipid droplets, stimulate the accumulation of triglycerides in the liver, and increase the susceptibility to NAFLD (Romeo et al. 2008 ). The activation of TF (NACHT, LRR, and PYD domains-containing protein 3) NLRP3 is related to the pathogenesis of liver disease. Down-regulation of NLRP3 / NF-κB signaling pathway can inhibit liver inflammation and fibrosis, reducing the occurrence and development of NAFLD inflammation (Szabo et al. 2015; Mridha et al. 2017 ; Wang et al. 2020 ). Activating transcription factor 4 ( ATF4 ) is a member of the cAMP response element binding protein family of basic zipper proteins, which is involved in the regulation of physiological processes of many genes, including apoptosis, lipid metabolism and obesity (Seo et al. 2009 ). ATF4 binds to the promoter region of mitochondrial transcription factor A (TFAM), a gene related to hepatic steatosis, inflammation, oxidative stress and apoptosis, inhibits the transcriptional activity of key regulatory factor nuclear respiratory factor 1 (NRF1), stops the NRF1/TFAM signaling pathway, improves mitochondrial biosynthesis and respiratory function under alcohol induction, regulates hepatic steatosis, inflammatory response and cell death, and is a key TF regulating AH (Hao et al. 2021 ). Therefore, we speculate that key genes interact with TFs in liver cells to regulate the disease process of CHI. In these four CHI-related diseases, liver cells can act on NF-κB and other related signaling pathways by regulating transcription factors such as HNF4α, PPARα, FOXA1, FOXA2, NLRP3, and SP1, affecting the proliferation, differentiation, transcription and apoptosis of liver cells, regulating DNA transcription and replication of liver injury cells, anti-inflammatory, anti-oxidation, and glucose and lipid metabolism, providing a new basis for diagnosis and prognosis for CHI prevention and treatment. In this study, the GEO database, a high-throughput gene expression database was commonly used for network data collection, and was used to collect four different CHI data sets of viral hepatitis (HB and HC), AH and NAFLD, and to screen the differentially expressed genes of these four diseases. PPI protein interaction network was used to screen the key genes based on four kinds of liver injury diseases, and the relationships between potential liver injury therapeutic drugs, characteristic genes and CHI diseases were screened by TCMSP database, which provided a new idea and theoretical basis for the treatment of liver injury. But there are still some shortcomings in this experiment. Firstly, only four kinds of data related to CHI were collected and analyzed. The sample size obtained from the GEO database was not enough, and the clinical verification data was not complete enough. Secondly, some key genes such as FOXC1 can regulate four key genes related to CHI (CCL20, SSP1, ANXA2 and LGALS3), but the correlation with CXCL10 has not been reported or the specific regulatory mechanism is still unknown, which needs to be verified in a large-scale multicenter clinical cohort. In the future, we will continue to pay attention to the role of these genes and the mechanism of treating liver injury. Abbreviations CHI: Chronic hepatic injury IRGs: Immune related genes DEGs: Differentially expressed genes AH: Alcoholic hepatitis GO: Gene ontology GO-BP: GO-Biological process GO-CC: GO-Cellular components GO-MF: GO-Molecular functions KEGG: Kyoto Encyclopedia of Genes and Genomes PPI: Protein-protein interaction RRA: Robust rank aggregation ALT: alanine aminotransferase AST: aspartate aminotransferase HB: Hepatitis B HBV: Hepatitis B virus HC: Hepatitis C HCV: Hepatitis C virus NAFLD: Nonalcoholic fatty liver disease DNA: deoxyribonucleic acid cccDNA: covalently closed circular DNA RNA-seq: RNA sequencing data GEO: Gene Expression Omnibus TFs: transcription factors TCMSP: Traditional Chinese Medicine Systems Pharmacology ROC: Receiver operating characteristic AUC: Area under the curve SPP1:secreted phosphor protein CXCL10: Chemokine (C-X-C motif) ligand CCL20: Chemokine (C-C motif) ligand 20 ANXA2: Annexin A2 LGALS3: lectin galactoside‐binding soluble 3 DILI: drug-induced liver injury FOXC1: Forkhead box C1 FOXA1: Forkhead box A1 LPS: lipopolysaccharide HSC: hepatic stellate cells FXR1: farnesoid X receptor 1 TGR5: Takeda G-protein-coupled receptor 5 Nrf2: NF-E2-related factor 2 ARE: antioxidant response element NF-κB: nuclear factor-κB Sp1: specificity protein TRIM: tripartite motif SART1: Spliceosome associated factor 1 HNF4α: hepatocyte nuclear factor 4 alpha PPARγ: Peroxisome proliferator-activated receptor gamma AP2: APETALA2 ALB: Albumin HHEX: Hematopoietically expressed homeobox PNPLA3: patatin-like phospholipase domain-containing 3 ATF4: Activating transcription factor 4 NLRP3: NACHT, LRR, and PYD domains-containing protein 3 NRF1: nuclear respiratory factor 1 TFAM: mitochondrial transcription factor A Declarations Author contributions TP: Writing–Original draft preparation, Conceptualization. JF: Data curation. JL: Methodology. YC: Supervision, Funding acquisition. HH: Project administration. CL: Editing. QT: Writing-Reviewing, Editing. NH: Software. JM: Visualization, Investigation. MZ: Formal analysis. QQ: Validation. JW: Resources. Funding This work was supported by grants from the National Natural Science Foundation of China (81360524, 81260673) and Guangxi Municipal and County Scientific Research Project (XKJ2346, Z2023118, GZKJ2309, 2022A008, YKJ2129). Conflict of interest No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. References Affò S, Morales-Ibanez O, Rodrigo-Torres D, Altamirano J, Blaya D, Dapito DH, Millán C, Coll M, Caviglia JM, Arroyo V, Caballería J, Schwabe RF, Ginès P, Bataller R, Sancho-Bru P (2014) CCL20 mediates lipopolysaccharide induced liver injury and is a potential driver of inflammation and fibrosis in alcoholic hepatitis. 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Li","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Junxuan","middleName":"","lastName":"Li","suffix":""},{"id":267644762,"identity":"802c6202-354e-4f0c-b021-255838fca961","order_by":3,"name":"Yong Chen","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Chen","suffix":""},{"id":267644763,"identity":"95bb5f60-a5ef-4bd9-ae8c-35368f45ab47","order_by":4,"name":"Huan He","email":"","orcid":"","institution":"Guangxi Vocational University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"He","suffix":""},{"id":267644764,"identity":"6c120b66-9937-437a-947c-5f69e121df21","order_by":5,"name":"Jiabao Ma","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiabao","middleName":"","lastName":"Ma","suffix":""},{"id":267644765,"identity":"2f8f061f-02e9-4281-90ca-ff0f7d396a8d","order_by":6,"name":"Cao Liang","email":"","orcid":"","institution":"Guangxi International Zhuang Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cao","middleName":"","lastName":"Liang","suffix":""},{"id":267644766,"identity":"776c8427-1e2e-4184-afc9-d8b1d7dc5ea1","order_by":7,"name":"Qiulian Tang","email":"","orcid":"","institution":"Guangxi International Zhuang Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiulian","middleName":"","lastName":"Tang","suffix":""},{"id":267644767,"identity":"4dd6e9c8-2320-4d45-9505-72610756bb89","order_by":8,"name":"Naiqiang Hu","email":"","orcid":"","institution":"Guangxi International Zhuang Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Naiqiang","middleName":"","lastName":"Hu","suffix":""},{"id":267644768,"identity":"2cb67d74-3373-4f0e-aee2-fbd3343f5c88","order_by":9,"name":"Meirong Zhao","email":"","orcid":"","institution":"Guangxi Vocational University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Meirong","middleName":"","lastName":"Zhao","suffix":""},{"id":267644769,"identity":"2112ec6e-9fc1-4f97-9162-9ef22170920c","order_by":10,"name":"Qingxia Qin","email":"","orcid":"","institution":"Guangxi Vocational University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Qingxia","middleName":"","lastName":"Qin","suffix":""},{"id":267644770,"identity":"2c866ff6-4c13-4efc-b780-2153a6d0588e","order_by":11,"name":"Jiangcun Wei","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiangcun","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-01-17 11:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872787/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49853186,"identity":"9dd204bd-212a-4b2c-a3c1-bd42ca1755cf","added_by":"auto","created_at":"2024-01-19 06:30:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential analyses results of Case and Control based on CHI datasets: \u003c/strong\u003e(A) Volcano Map of Gene Distribution between Case and Control (GSE28619); (B) Heat map of differentially expressed genes between Case and Control (GSE28619); (C) Volcano Map of Gene Distribution between Case and Control (GSE126848); (D)Heat map of differentially expressed genes between Case and Control (GSE126848); (E) Volcano Map of Gene Distribution between Case and Control (GSE6764); (F) Heat map of differentially expressed genes between Case and Control (GSE6764); (G) Volcano Map of Gene Distribution between Case and Control (GSE84044); (H) Heat map of differentially expressed genes between Case and Control (GSE84044).\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872787/v1/e131b2c25687105ee8285454.jpg"},{"id":49852734,"identity":"69ca7a4d-6747-4d73-a29d-d29f0d466858","added_by":"auto","created_at":"2024-01-19 06:22:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":752006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of latent genes in CHI：\u003c/strong\u003e(A)The logFC size of latent genes in 4 datasets; (B) GO histogram of the top 10 biological function with the most significant latent genes; (C) KEGG bubble diagram of the top 10 signaling pathways with the most significant latent genes.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872787/v1/2d025047f5cafca464cf190e.jpg"},{"id":49852733,"identity":"f4348ebb-e1ff-449c-a64c-824b5326c1d1","added_by":"auto","created_at":"2024-01-19 06:22:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5 key genes were obtained via the PPI network: \u003c/strong\u003e(A) Venn diagram of differential immune genes; (B) PPI network of differential immune genes; (C) 5 key genes screened via PPI network; (D) PPI protein interaction sub-network of 5 key genes; (E) ROC curves of 5 key genes in 4 validation sets; (F) Mean values of key genes expression in groups.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872787/v1/d6ecd8f0423076fe058ff5fb.jpg"},{"id":49852730,"identity":"88a1f793-21b6-4fc8-90d9-2d716c30efe9","added_by":"auto","created_at":"2024-01-19 06:22:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":384724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuercetin could regulate the expression of key genes:\u003c/strong\u003e (A) Gene-compound-herb regulation network；(B)Molecular docking of CXCL10 and quercetin (-6.9 kcal / mol)；(C) Molecular docking details of CXCL10 and quercetin (-6.9kcal / mol); (D) Gene-TFs regulatory network.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3872787/v1/ac8f2c5a83d995f642107907.jpg"},{"id":49929454,"identity":"4352ed5f-1185-479c-b2f2-ad52d1331cf0","added_by":"auto","created_at":"2024-01-21 12:52:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":968477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872787/v1/41fa6885-9b6d-408e-8038-312dc7e29033.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening of common key immune genes and prediction of potential drugs in chronic hepatic injury","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic hepatic injury (CHI) refers to the pathological process of liver function damage under the influence of alcohol, virus, drugs and other reasons for a long time (Tang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).The main features include oxidative stress, apoptosis, inflammatory response and so on (Ramana et al. 2010; Srivastava et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Maccari et al. 2015). In the early stage of CHI, the liver function is slightly damaged, the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and bilirubin in serum are obvious increased, and the clinical symptoms are not obvious. If there is no treat in time, CHI would gradually aggravate, resulting in severe damage to liver cells, irreversible formation of fibrosis, characteristic pseudolobule lesions, development of cirrhosis, and even formation of liver malignant tumors, endangering life and health (Pierantonelli et al. 2015). The most common type of liver disease in China is chronic hepatitis B (HB), with 86\u0026nbsp;million people infected with chronic HB virus (HBV) (Deng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Accordingly, the prevalence of Nonalcoholic Fatty Liver Disease (NAFLD) in Shanghai, Chengdu, and Guangdong was 15.0%, 12.5%, and 17.0% respectively (Masand et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In a retrospective study, the annual incidence of drug-induced liver injury in the general population in mainland China was estimated to be 23.8 / 100,000 (Thakur et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In 2015, about 160,000 people died of alcoholic liver disease in China, and the prevalence of alcoholic liver disease in some areas of China was about 0.5% -8.55% (Thayumanavan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCHI usually causes liver fibrosis. The prognosis of patients with advanced liver fibrosis is usually poor, since it often develops into decompensated cirrhosis and liver cancer (Tsochatzis et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). NAFLD is usually caused by obesity. When fat accumulates around the liver tissue, it will cause hepatic steatosis and cytotoxicity, leading to hepatocyte reaction, then hematopoietic stem cell (HSC) transformation into fibroblasts, and extracellular matrix (ECM) proliferation leading to nonalcoholic liver fibrosis (Kumar et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Viral hepatitis is mainly caused by HBV and hepatitis C virus (HCV). HB is transported to the liver cells through the receptor, and the nucleocapsid of the viral deoxyribonucleic acid (DNA) is encapsulated into the liver nucleus. Then the viral DNA is released and integrated with the host DNA, and the viral protein is expressed after transcription to complete the HBV infection (Tsai. 2021). Persistent viral infections cause liver cell damage, leading to liver fibrosis (Gerold et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Biliotti et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the long run, heavy drinking will lead to the accumulation of acetaldehyde, which is the decomposition product of ethanol and it can cause metabolic disorders of hepatocytes and liver inflammation (Patel et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, in the process of liver fibrosis caused by different factors such as viral hepatitis, autoimmune Hepatitis (AH) and NAFLD, whether they have common associated genes has not been reported.\u003c/p\u003e \u003cp\u003eAt present, many immune cells have been reported to be related to cellular liver injury. Kupffer cells respond to lipopolysaccharide (LPS) through TLR4 to produce various inflammatory cytokines, including TNF-α, IL-1β, IL-6, IL-12, IL-18, as well as granulomatous liver disease, ischemia reperfusion liver injury, NAFLD and alcoholic chemokine liver disease (Seki et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A study has shown that a group of macrophages recruited from peritoneal fluid, its F4 / 80 hi GATA6\u0026thinsp;+\u0026thinsp;and rapidly penetrate through the mesothelium into the injured liver (Wang et al. 2016). The hyaluronic acid expressed by sinusoidal endothelial cells interacts with CD44 on the surface of neutrophils, which plays a key role in the recruitment of neutrophils to the liver (McDonald et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). After liver injury, dendritic cell (DCs) acquired the ability to induce inflammation mediated by hepatic stellate cells, NK cells and T cells (Tokita et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). DCs also participate in the regression of fibrosis after liver injury by producing MMP9 (Jiao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Th17 cells are pro-inflammatory cells that produce IL-17 cytokines, which are associated with promoting liver inflammation and fibrosis (Lemmers et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ge et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Meng et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, immune-related genes have not been reported as diagnostic features of liver injury.\u003c/p\u003e \u003cp\u003eIn this study, we screened the differentially expressed genes (DEGs) in viral hepatitis (HB and HC), AH, and NAFLD, and intersected with Immune related genes (IRGs). The Protein-protein interaction (PPI) network was used to screen the key genes based on four liver injury diseases, and the drugs for liver injury were predicted eventually, providing a new idea and theoretical basis for the treatment of CHI.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Collection of CHI datasets\u003c/h2\u003e \u003cp\u003eCHI related RNA sequencing data (RNA-seq) were sourced from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE28619 microarray of AH included 7 Control and 15 AH samples, GSE126848 microarray of NAFLD contained 14 Control and 15 NAFLD samples, GSE6764 microarray of HC had 10 Control and 13 HC samples, and GSE84044 microarray of HB consisted of 63 mild fibrosis (F0-F1, used as Control) and 28 severe fibrosis samples (F3-F4). The four datasets above were applied as training sets, and we downloaded 4 datasets to be used as validation sets as follows. GSE142530 dataset of AH contained 12 Control and 10 AH samples, GSE49541 microarray of NAFLD had 40 mild fibrosis (F0-F1, used as Control) and 32 severe fibrosis samples (F3-F4), GSE14323 microarray of HC had 9 Control and 41 HC samples, and GSE83148 included 6 Control and 122 HB samples. Inaddition, 2519 IRGs were obtained from ImmPort and InnateDB databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/shared/home\u003c/span\u003e\u003cspan address=\"https://www.immport.org/shared/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.innatedb.ca/\u003c/span\u003e\u003cspan address=\"https://www.innatedb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential analyses between Control and CHI samples\u003c/h2\u003e \u003cp\u003eWe applied the \u0026ldquo;limma\u0026rdquo; package (version 3.50.1) to sift out DEGs between Control and CHI samples, the CHI samples contained AH, NAFLD, HC, and HB samples (Ritchie et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The top 10 DEGs (AH, NAFLD, HC), and HB with the highest positive or negative regulation degree were visualized by the \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.5) in volcano plots and heatmaps eventually (Ito et al. 2013). Then, the RRA algorithm was applied to intersect the DEGs (AH, NAFLD, HC, and HB) and sift out genes with a higher regulation degree according to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Freq\u0026thinsp;\u0026gt;\u0026thinsp;3. Those genes screened out were defined as latent genes of CHI. In addition, we conducted enrichment analysis of latent genes by the \u0026ldquo;clusterProfiler\u0026rdquo; package (version 4.2.2) with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 based on the GO and KEGG (Wu et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 PPI network was applied to screen key genes\u003c/h2\u003e \u003cp\u003eLatent genes were intersected with IRGs to obtain Differentially expressed IRGs (DE-IRGs). Those DE-IRGs were applied to generate a PPI network via the STRING database with medium confidence\u0026thinsp;\u0026gt;\u0026thinsp;0.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After eliminating outlier genes, we sifted out genes with a higher degree of more than 2 to define them as key genes. On the basis of the four validation sets, we conducted Receiver operating characteristic (ROC) analysis to evaluate the prognostic prediction performance of key genes and contrasted their expression in different types of CHI samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Prediction of potential drugs and transcription factors (TFs) of key genes\u003c/h2\u003e \u003cp\u003ePotential drugs of CHI were predicted using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/index.php\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the herbs with them as the main component were obtained. Then, we downloaded the PDB format document of key genes and conducted a molecular docking process between drugs and key genes. The result was visualized by the \u0026ldquo;PyMol\u0026rdquo; tool. Next, TFs correlated with key genes were detected via the networkanalys database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and applied to generate a TFs-mRNA network by the software \u0026ldquo;cytoscape\u0026rdquo; (version 3.8.2) (Shannon et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 77 latent genes were screened out by differential analyses\u003c/h2\u003e \u003cp\u003eBased on the four training sets, 2718 DEGs between Control and AH samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B\u003cb\u003e) (Table S1)\u003c/b\u003e, 3744 DEGs between Control and NAFLD samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D\u003cb\u003e) (Table S2)\u003c/b\u003e, 1733 DEGs between Control and HC samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F\u003cb\u003e) (Table S3)\u003c/b\u003e, and 548 DEGs between Control and HB samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H\u003cb\u003e) (Table S4)\u003c/b\u003e were sifted out by differential analysis. After an intersection process among those four kinds of DEGs, we obtained 77 latent genes of CHI and visualized their regulation degree in a heatmap \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Enrichment analysis identified that latent genes were enriched in 561 GO-Biological process (GO-BP) functions including collagen fibril organization, cellular hormone metabolic process, etc., 44 GO-Cellular components (GO-CC) functions containing complex of collagen trimers, endoplasmic reticulum lumen, etc., 69 GO-Molecular functions (GO-MF) functions consisting of cadherin binding involved in cell-cell adhesion, chemokine receptor binding, etc. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e) (Table S5)\u003c/b\u003e, and 15 KEGG pathways including Fructose and mannose metabolism, Pentose and glucuronate interconversions, etc. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e) (Table S6)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 5 key genes were obtained via the PPI network\u003c/h2\u003e \u003cp\u003eLatent genes were intersected with IRGs to procure 25 DE-IRGs, and those were applied to generate a PPI network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. We eliminated the outlier genes and picked 5 genes (SPP1, CXCL10, CCL20, ANXA2, and LGALS3) with a higher degree of more than 2 and defined them as key genes of CHI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D\u003cb\u003e)\u003c/b\u003e. It could be found in the ROC curves that all of the Area under the curve (AUC) values in GSE142530, GSE14323, and GSE83148 were higher than 0.8, and those in GSE49541 were higher than 0.7 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Besides, the expression of key genes in AH, NAFLD, HC, and HB was remarkably higher than that in Control samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Quercetin could regulate the expression of key genes\u003c/h2\u003e \u003cp\u003eThe potential drugs prediction analysis identified that the key gene CXCL10 was regulated by a natural compound named quercetin, and then we found that there were 187 herbs with it as the main component, such as Polygoni Avicularis Herba, Alpinia Katsumadai Hayat, Phellodendri Amurensis Cortex, etc. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The minimum binding energy between CXCL10 and quercetin was \u0026minus;\u0026thinsp;6.7 kcal/mol, the value of it was lower than \u0026minus;\u0026thinsp;5 kcal/mol, which demonstrated that the combined affinity between CXCL10 and quercetin was preferably strong \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C\u003cb\u003e)\u003c/b\u003e. In addition, we generate a TFs-mRNA network and found that FOXC1 could regulate 4 key genes including CCL20, SSP1, ANXA2, and LGALS3 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe liver is an important metabolic organ that regulates the metabolic homeostasis of the human body. It can remove many harmful chemicals and drugs from the body. Based on these important functions, the liver is also vulnerable to these substances (Liu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Liver injury is not obvious in the early stage, but often leads to higher mortality in severe cases. Studies have shown that the type of liver injury, such as chemical liver injury, viral liver injury, alcoholic liver injury, and drug-induced liver injury (DILI). The main pathological changes include liver fibrosis, steatosis, cirrhosis, and liver cancer (Knight et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Clinically, CHI includes chronic HB, chronic HC, AH, NAFLD, DILI, etc. (Younossi et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There is still great difficulty in the treatment and prognosis of CHI, and even it has deteriorated into liver cancer when the disease is found. Obviously, it is particularly important to find out the key genes of CHI and predict the therapeutic drugs. In this study, bioinformatics methods were used to find out the key genes affecting the prognosis of CHI, which provided important guiding significance for improving the diagnosis and prognosis of CHI.\u003c/p\u003e \u003cp\u003eIn this study, five key genes of CHI (SPP1, CXCL10, CCL20, ANXA2, and LGALS3) were screened and verified by ROC. SPP1, also known as osteopontin, is a secreted glycoprotein that can affect the adhesion, proliferation, differentiation, migration and survival of many types of tumor cells (Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SPP1 is highly expressed in human hepatocellular carcinoma and adjacent tissues, which is a risk factor and potential marker for the prognosis of patients with hepatocellular carcinoma, and the prognosis of patients with high SPP1 expression is relatively poor (Rong et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Chemokines are basic proteins that play a role through a subset of G protein-coupled receptors and cytokine families, and are mainly involved in leukocyte migration in inflammatory responses (Vassilatis et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). CXCL10, also known as γ-interferon-inducible protein, can activate CXCR3\u0026thinsp;+\u0026thinsp;immune NK cells, mononuclear macrophages, etc., and plays an important role in antiviral and anti-tumor processes (Van et al. 2015). Studies have shown that CXCL10, as an anti-inflammatory regulator, is significantly up-regulated in liver injury tissues caused by NAFLD and HB, and plays a key role in its pathological process and can be used as a biomarker for these two types of CHI (Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rot et al. 2004). CCL20 is a small molecule protein that is physiologically expressed in the liver and it can participate in the inflammatory response as well as tissue homeostasis of liver tissue (Hromas et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hieshima et al. 1991). In addition, in lipopolysaccharide (LPS) -induced liver injury tissues, the up-regulation of CCL20 is closely related to LPS, which is a biomarker for predicting the prognosis of patients with AH and an important medium for linking AH inflammation, injury and fibrosis (Aff\u0026ograve; et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). ANXA2 is a member of the connexin family, which is involved in cell proliferation and apoptosis, and is closely related to liver injury-related diseases (Dong et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Meanwhile, ANXA2 is a potential biomarker for liver injury or liver fibrosis, which is significantly up-regulated in the serum of patients with chronic HB and AH (Kolgelier et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). And the overexpression of ANXA2 in the liver can inhibit the expansion of liver injury and reduce the progression of liver fibrosis (Yang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dadhania et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). LGALS3 belongs to the carbohydrate binding protein of the non-integrin β-galactoside binding lectin family, and its up-regulation is related to NAFLD (Barondes et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Chalasani et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Azevedo et al. 2020; Ochieng et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Studies have shown that LGALS3 is significantly increased in liver inflammation, fibrosis and cancer, and is involved in its apoptosis, migration, adhesion, angiogenesis and inflammatory response (Ochieng et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Therefore, we speculate that in liver injury tissues, the body can regulate CHI by up-regulating the expression of these five key genes, thereby regulating liver inflammation, apoptosis, differentiation and other mechanisms.\u003c/p\u003e \u003cp\u003eIn our study, quercetin, a targeted drug for the key gene CXCL10, and 187 herbs with active ingredients were predicted. Quercetin has the effects of anti-inflammatory, anti-oxidation and scavenging free radicals, and it is a tyrosine protein kinase inhibitor and is one of the most common flavonoids, too (Manach et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Studies have shown that quercetin can effectively down-regulate the M1 macrophage immune-related factor CXCL10 related to the liver Th1 immune response, thereby regulating the inflammatory response (Oo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tsai et al. 2023). In addition, quercetin can also inhibit hepatic stellate cells (HSC) activation by reducing the levels of inflammatory factors (CXCL10 and heparin-binding cytokines), inhibit the proliferation of aHSCs and heptotocytes, and down-regulate protein molecules which related to metabolism, survival, cytokinesis and protein folding, inhibit liver cell growth, thereby controlling the progression of liver fibrosis (Wu et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). At the same time, it is a potential effective drug for the treatment of NAFLD by activating the farnesoid X receptor 1 / Takeda G-protein-coupled receptor 5 signaling pathway (FXR1 / TGR5 signaling pathways) to participate in the regulation of NAFLD-induced lipid metabolism, oxidative stress and inflammatory response, and reduce liver lipid accumulation (Yang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Meanwhile, by activating nuclear NF-E2-related factor 2 (Nrf2) and inducing antioxidant response element (ARE) genes, liver detoxification enzymes are significantly activated to alleviate alcohol-induced oxidative stress, glutathione depletion and pro-inflammatory cytokines in HepG2 cells (Lee et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). And it has a good protective effect against alcohol-induced liver injury in vitro and can be used to improve liver diseases such as AH. For viral hepatitis, quercetin can inhibit the replication of viral cell genomic DNA, inhibit the production of reactive oxygen species and nitrogen free radicals ( ROS / RNS ) and lipid peroxidation, thereby inhibiting the replication of liver injury cells (Cheng et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khachatoorian et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pisonero-Vaquero et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, we speculate that quercetin can affect the proliferation, differentiation and metabolism of liver injury cells by affecting immune-related factors and inflammatory factor CXCL10, affecting lipid metabolism, oxidative stress and inflammatory response mechanisms, providing a new predictive target for CHI treatment.\u003c/p\u003e \u003cp\u003eBased on these key genes, this paper predicts a large number of TFs that regulate key genes. In HB, specificity protein 1 (Sp1) has been shown to bind to several sites in the HB genome, and the interaction between Sp1 and covalently closed circular DNA (cccDNA) shows a direct effect on HB replication (Turton et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sp1 is also associated with a series of cellular processes such as angiogenesis, apoptosis and cell cycle (Chu et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). As a regulator of HB gene expression, Sp1 can participate in the regulation of HB gene expression through nuclear factor-κB (NF-κB) signaling pathway (Li et al. 2001). NF-κB is a regulator of innate and adaptive cellular immune response, which is involved in angiogenesis, apoptosis, cell proliferation, migration and other cellular processes (Taniguchi et al. 2018). Meanwhile, NF-κB can inhibit the transcription of HB genome and activate oxidative stress in liver cells (Wilson et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Waris et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In addition, HB infects hepatocytes and persists in the form of covalently closed cccDNA. Protein spliceosome associated factor 1 (SART1) limits the transcription and replication of HB covalently closed cccDNA by inhibiting the key TF hepatocyte nuclear factor 4 alpha (HNF4α) in hepatocytes (Teng et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Members of the tripartite motif (TRIM) protein family are antiviral components of the innate immune system and are strongly induced by interferons (IFNs) (Yap et al. 2012). TRIM proteins are involved in a variety of diseases and regulate cell signal transduction, protein quality control, transcription, cell cycle, apoptosis and development (Watanabe et al. 2017). TRIM56 is a key antiviral immune effector molecule that exerts anti-HB activity through the NF-κB signaling pathway, which is essential for inhibiting the transcription of HB covalently closed cccDNA (Tian et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TRIM22 has also been shown to be a direct target gene of miR-215 and a natural antiviral effector (Gao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). MiR-215 targets TRIM22 to block the NF-κB pathway and positively regulates HC replication, providing a new potential target for HC infection (Tian et al. 2018). HC infection affects the binding of hepatocyte TF Forkhead box A1 (FOXA1), Forkhead box A2 (FOXA2) and HNF4α to homologous sites in the genome, thereby inhibiting their DNA methylation and regulating the expression of HC-infected hepatocytes (Wijetunga et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By regulating the TFs Forkhead box C1 (FOXO1) and FOXA2, HC reduces lipid accumulation in liver cells, increases β-oxidation of liver cells, and reduces the replication of HCV genome. It is one of the effective methods for the treatment of chronic hepatitis infected with HC (Bose et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). At the same time, FOXA2 inhibits the induction of late adipocyte differentiation markers such as Peroxisome proliferator-activated receptor gamma (PPARγ) and APETALA2 (AP2) by blocking the adipogenic differentiation of preadipocytes (Wolfrum et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The absence of FOXA2 leads to a significant decrease in the expression of liver endoderm transcription products such as Albumin (ALB) and TFs HNF4α and Hematopoietically expressed homeobox (HHEX) involved in liver development, which reduces the occurrence and development of NAFLD lipids (Genga et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The patatin-like phospholipase domain-containing 3 (PNPLA3) is the main determinant of liver fat content and its development. It can increase the accumulation of mutant proteins, reduce the lipase activity on hepatocyte lipid droplets, stimulate the accumulation of triglycerides in the liver, and increase the susceptibility to NAFLD (Romeo et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The activation of TF (NACHT, LRR, and PYD domains-containing protein 3) NLRP3 is related to the pathogenesis of liver disease. Down-regulation of NLRP3 / NF-κB signaling pathway can inhibit liver inflammation and fibrosis, reducing the occurrence and development of NAFLD inflammation (Szabo et al. 2015; Mridha et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Activating transcription factor 4 ( ATF4 ) is a member of the cAMP response element binding protein family of basic zipper proteins, which is involved in the regulation of physiological processes of many genes, including apoptosis, lipid metabolism and obesity (Seo et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). ATF4 binds to the promoter region of mitochondrial transcription factor A (TFAM), a gene related to hepatic steatosis, inflammation, oxidative stress and apoptosis, inhibits the transcriptional activity of key regulatory factor nuclear respiratory factor 1 (NRF1), stops the NRF1/TFAM signaling pathway, improves mitochondrial biosynthesis and respiratory function under alcohol induction, regulates hepatic steatosis, inflammatory response and cell death, and is a key TF regulating AH (Hao et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, we speculate that key genes interact with TFs in liver cells to regulate the disease process of CHI. In these four CHI-related diseases, liver cells can act on NF-κB and other related signaling pathways by regulating transcription factors such as HNF4α, PPARα, FOXA1, FOXA2, NLRP3, and SP1, affecting the proliferation, differentiation, transcription and apoptosis of liver cells, regulating DNA transcription and replication of liver injury cells, anti-inflammatory, anti-oxidation, and glucose and lipid metabolism, providing a new basis for diagnosis and prognosis for CHI prevention and treatment.\u003c/p\u003e \u003cp\u003eIn this study, the GEO database, a high-throughput gene expression database was commonly used for network data collection, and was used to collect four different CHI data sets of viral hepatitis (HB and HC), AH and NAFLD, and to screen the differentially expressed genes of these four diseases. PPI protein interaction network was used to screen the key genes based on four kinds of liver injury diseases, and the relationships between potential liver injury therapeutic drugs, characteristic genes and CHI diseases were screened by TCMSP database, which provided a new idea and theoretical basis for the treatment of liver injury. But there are still some shortcomings in this experiment. Firstly, only four kinds of data related to CHI were collected and analyzed. The sample size obtained from the GEO database was not enough, and the clinical verification data was not complete enough. Secondly, some key genes such as FOXC1 can regulate four key genes related to CHI (CCL20, SSP1, ANXA2 and LGALS3), but the correlation with CXCL10 has not been reported or the specific regulatory mechanism is still unknown, which needs to be verified in a large-scale multicenter clinical cohort. In the future, we will continue to pay attention to the role of these genes and the mechanism of treating liver injury.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col\u003e\n\u003cli\u003eCHI: Chronic hepatic injury\u003c/li\u003e\n\u003cli\u003eIRGs: Immune related genes \u003c/li\u003e\n\u003cli\u003eDEGs: Differentially expressed genes \u003c/li\u003e\n\u003cli\u003eAH: Alcoholic hepatitis \u003c/li\u003e\n\u003cli\u003eGO: Gene ontology \u003c/li\u003e\n\u003cli\u003eGO-BP: GO-Biological process \u003c/li\u003e\n\u003cli\u003eGO-CC: GO-Cellular components\u003c/li\u003e\n\u003cli\u003eGO-MF: GO-Molecular functions \u003c/li\u003e\n\u003cli\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/li\u003e\n\u003cli\u003ePPI: Protein-protein interaction\u003c/li\u003e\n\u003cli\u003eRRA: Robust rank aggregation \u003c/li\u003e\n\u003cli\u003eALT: alanine aminotransferase \u003c/li\u003e\n\u003cli\u003eAST: aspartate aminotransferase \u003c/li\u003e\n\u003cli\u003eHB: Hepatitis B \u003c/li\u003e\n\u003cli\u003eHBV: Hepatitis B virus \u003c/li\u003e\n\u003cli\u003eHC: Hepatitis C \u003c/li\u003e\n\u003cli\u003eHCV: Hepatitis C virus \u003c/li\u003e\n\u003cli\u003eNAFLD: Nonalcoholic fatty liver disease \u003c/li\u003e\n\u003cli\u003eDNA: deoxyribonucleic acid\u003c/li\u003e\n\u003cli\u003ecccDNA: covalently closed circular DNA\u003c/li\u003e\n\u003cli\u003eRNA-seq: RNA sequencing data \u003c/li\u003e\n\u003cli\u003eGEO: Gene Expression Omnibus \u003c/li\u003e\n\u003cli\u003eTFs: transcription factors \u003c/li\u003e\n\u003cli\u003eTCMSP: Traditional Chinese Medicine Systems Pharmacology\u003c/li\u003e\n\u003cli\u003eROC: Receiver operating characteristic\u003c/li\u003e\n\u003cli\u003eAUC: Area under the curve \u003c/li\u003e\n\u003cli\u003eSPP1:secreted phosphor protein \u003c/li\u003e\n\u003cli\u003eCXCL10: Chemokine (C-X-C motif) ligand\u003c/li\u003e\n\u003cli\u003eCCL20: Chemokine (C-C motif) ligand 20 \u003c/li\u003e\n\u003cli\u003eANXA2: Annexin A2 \u003c/li\u003e\n\u003cli\u003eLGALS3: lectin galactoside‐binding soluble 3 \u003c/li\u003e\n\u003cli\u003eDILI: drug-induced liver injury\u003c/li\u003e\n\u003cli\u003eFOXC1: Forkhead box C1\u003c/li\u003e\n\u003cli\u003eFOXA1: Forkhead box A1 \u003c/li\u003e\n\u003cli\u003eLPS: lipopolysaccharide \u003c/li\u003e\n\u003cli\u003eHSC: hepatic stellate cells\u003c/li\u003e\n\u003cli\u003eFXR1: farnesoid X receptor 1 \u003c/li\u003e\n\u003cli\u003eTGR5: Takeda G-protein-coupled receptor 5 \u003c/li\u003e\n\u003cli\u003eNrf2: NF-E2-related factor 2\u003c/li\u003e\n\u003cli\u003eARE: antioxidant response element\u003c/li\u003e\n\u003cli\u003eNF-\u0026kappa;B: nuclear factor-\u0026kappa;B\u003c/li\u003e\n\u003cli\u003eSp1: specificity protein \u003c/li\u003e\n\u003cli\u003eTRIM: tripartite motif \u003c/li\u003e\n\u003cli\u003eSART1: Spliceosome associated factor 1\u003c/li\u003e\n\u003cli\u003eHNF4\u0026alpha;: hepatocyte nuclear factor 4 alpha\u003c/li\u003e\n\u003cli\u003ePPAR\u0026gamma;: Peroxisome proliferator-activated receptor gamma \u003c/li\u003e\n\u003cli\u003eAP2: APETALA2 \u003c/li\u003e\n\u003cli\u003eALB: Albumin \u003c/li\u003e\n\u003cli\u003eHHEX: Hematopoietically expressed homeobox\u003c/li\u003e\n\u003cli\u003ePNPLA3: patatin-like phospholipase domain-containing 3 \u003c/li\u003e\n\u003cli\u003eATF4: Activating transcription factor 4 \u003c/li\u003e\n\u003cli\u003eNLRP3: NACHT, LRR, and PYD domains-containing protein 3\u003c/li\u003e\n\u003cli\u003eNRF1: nuclear respiratory factor 1\u003c/li\u003e\n\u003cli\u003eTFAM: mitochondrial transcription factor A \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTP: Writing\u0026ndash;Original draft preparation, Conceptualization. JF: Data curation. JL: Methodology. YC: Supervision, Funding acquisition. HH: Project administration. CL: Editing. QT: Writing-Reviewing, Editing. NH: Software. JM: Visualization, Investigation. MZ: Formal analysis. QQ: Validation. JW: Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (81360524, 81260673) and Guangxi Municipal and County Scientific Research Project (XKJ2346, Z2023118, GZKJ2309, 2022A008, YKJ2129).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAff\u0026ograve; S, Morales-Ibanez O, Rodrigo-Torres D, Altamirano J, Blaya D, Dapito DH, Mill\u0026aacute;n C, Coll M, Caviglia JM, Arroyo V, Caballer\u0026iacute;a J, Schwabe RF, Gin\u0026egrave;s P, Bataller R, Sancho-Bru P (2014) CCL20 mediates lipopolysaccharide induced liver injury and is a potential driver of inflammation and fibrosis in alcoholic hepatitis. 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J Hepatol 61(6): 1365-1375. https://doi.org/10.1016/j.jhep.2014.07.006.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Immune related genes, Chronic hepatic injury, Protein-protein interaction network, TFs-mRNA network","lastPublishedDoi":"10.21203/rs.3.rs-3872787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3872787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudies had revealed that Immune related genes play an important role in Chronic hepatic injury (CHI), which is the main cause of liver fibrosis. Differentially expressed genes (DEGs) between CHI including Alcoholic hepatitis (AH), Nonalcoholic fatty liver disease (NAFLD), Hepatitis C (HC), and Hepatitis B (HB) and related Control samples were detected by differential analysis. Then, 77 latent genes of CHI were intersected with IRGs to obtain DEGs for generating a Protein-protein interaction (PPI) network to screen out 5 key genes consisting of secreted phosphor protein 1 (SPP1), Chemokine (C-X-C motif) ligand (CXCL10), Chemokine (C-C motif) ligand 20 (CCL20), Annexin A2 (ANXA2), and lectin galactoside-binding soluble 3 (LGALS3). Besides, we found that CXCL10 was regulated by a natural compound named quercetin, and there were 187 herbs with it as the main component. TFs-mRNA network identified that Forkhead box C1 (FOXC1) could regulate 4 key genes including CCL20, SSP1, ANXA2, and LGALS3. Therefore, this could provide references for CHI treatments and further studies.\u003c/p\u003e","manuscriptTitle":"Screening of common key immune genes and prediction of potential drugs in chronic hepatic injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 06:22:12","doi":"10.21203/rs.3.rs-3872787/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"681d929f-0725-455a-b6fe-e3ede013ea3f","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-21T12:44:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 06:22:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3872787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3872787","identity":"rs-3872787","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}