Klf10), also known as Tieg1 for TGFβ (Inducible Early Gene-1) is known to control numerous genes in many cell types that are involved in various key biological processes , including cell metabolism and human disease. In skeletal muscle, particularly in the soleus, deletion of the Klf10 gene (Klf10 KO) resulted in ultrastructure fiber disorganization and mitochondrial metabolism deficiencies, characterized by muscular hypertrophy. To determine the metabolic profile related to loss of Klf10 expression, we analyzed blood and soleus tissue using UHPLC-Mass Spectrometry. Metabolomics analyses on both serum and soleus revealed profound differences between wild-type (WT) and KO animals. Klf10 deficient mice exhibited alterations in metabolites associated with energetic metabolism. Additionally, chemical classes of aromatic and amino-acid compounds were disrupted, together with Krebs cycle intermediates, lipids and phospholipids. From variable importance in projection (VIP) analyses, the Warburg effect, citric acid cycle, gluconeogenesis and transfer of acetyl groups into mitochondria appeared to be possible pathways involved in the metabolic alterations observed in Klf10 KO mice. These studies have revealed essential roles for Klf10 in regulating multiple metabolic pathways whose alterations may underlie the observed skeletal muscle defects as well as other diseases.The transcription factor Krüppel-like factor 10 ( Klf10), also named TGF-β (Trans-forming Growth Factor) inducible early gene 1 (Tieg1), is part of a complex and specific regulatory transcription system that regulates numerous genes in a variety of cells and tissue types [Klf10 have implications on multiple biological processes  [Klf10 is ubiquitously expressed in mammals and exhibits a higher expression in metabolic organs such as the liver, pancreas, muscle, and adipose tissue. Klf10 controls a variety of biological processes in the liver and skeletal muscle. It plays a crucial role in the regulation of the circadian clock, driving the expression of genes involved in lipogenesis, gluconeogenesis (with a higher fasting glucose), and glycolysis in the liver, influencing hepatic metabolism [Klf10 is downregulated in type 2 diabetes and cancer [Klf10 expression is diminished during breast cancer evolution [Klf10 is described as a tumor suppressor gene through TGF-β-induced growth inhibition; its functions are anti-proliferative and pro-apoptotic in cancer cells [Klf10 has been described to be protective against oral cancer [The transcription factor Krüppel-like factor 10  , relevanmmation) ,6. Klf10tabolism ,8. Moreod cancer . For exaer cells ,12,13,14l cancer  and has l cancer . Data inl cancer ,18.Klf10 knockout (KO) mice [Klf10 gene deletion results in hyperplasia and structural changes such as hypertrophy and ultrastructure disorganization in the soleus (slow-twitch muscle) and extensor digitorum longus (fast-twitch muscle). It has been shown that Klf10 deletion induces changes in passive [Klf10 deficient mice have contributed to our understanding of skeletal muscle metabolism, which is central to overall energetic metabolism in the animal. Klf10 is known to be responsible for important mitochondria regulating functions, affecting succinate dehydrogenase (SDH), cytochrome C oxidase (COX), and the citrate synthase (CS) enzyme activities in the soleus of deficient mice [The generation of KO) mice  has demoKO) mice ,21, hypeKO) mice ,23,24,25 passive  and acti passive  behaviorent mice .The metabolome refers to the representation of small chemical molecules (or metabolites) contained in a given biological sample at a specific time. In a biological sense, the metabolome represents a report of multiple cellular processes, including the regulation of the genome, transcriptome, and proteome cascades, and ultimately supports phenotypes and functions. Despite the limit point being the lack of insights between the production and consumption of metabolites, metabolomics has become a powerful tool to better understand the physiology, pathophysiology, or dysregulation of many biological systems ,30,31. BKlf10 on metabolism remains unclear and must be further investigated. This study is a continuation of our work demonstrating a role for Klf10 in muscle metabolism [Klf10 KO mice at the organismal level (serum) and the tissue level (muscle). This work has led to a better understanding of the role of Klf10 in metabolism, the identification of specific metabolic pathways regulated by Klf10, and the identification of potential biomarkers relevant for skeletal muscle defects/diseases.The impact of tabolism . Here, wKlf10 KO versus wild-type mice. A total of 224 and 246 metabolites were detected in serum and soleus, respectively. The PLS-DA score plot analysis of Klf10 KO and WT samples of serum and soleus are shown in 2Y = 0.974, Q2 = 0.87, CV-ANOVA = 4.42 × 10−6, from which 78 variables showed a p-value < 0.05. In soleus, the best PLS-DA score plot used 88 VIP with R2Y = 0.93, Q2 = 0.86, CV-ANOVA = 5.46 × 10−7, from which 27 variables have a p-value < 0.05. The VIP represent metabolites with a greater contribution to the separation between groups (Klf10 KO versus WT). The segregation into two distinguishable groups indicates that Klf10 gene deletion caused significant changes in the overall metabolome in mice. Of these, all metabolites showing a significant change are listed in We aimed to characterize differential metabolomics profiles in the serum and soleus of Klf10 KO and WT mice, we performed a volcano plot analysis . For instance, cholic acid and glycocholic acid in serum and caprylic acid and cortisol in soleus were highly upregulated in Klf10 KO compared to WT mice. Interestingly, hippuric acid was highly downregulated in both samples of Klf10 KO mice. These data demonstrated profound differences in the metabolic profiles of serum , nucleotides–nucleosides derivatives (6% in serum vs. 14% in soleus), and vitamins—cofactors—hormones (2% in serum vs. 7% in soleus). With the exception of sugar, phospholipids, nucleotides–nucleosides derivatives, and vitamins—cofactors–hormones classes, a very similar perturbation was observed on chemical repartition for serum and soleus, indicating that the same classes of chemical compounds were impacted in both biological samples in Klf10 KO mice providing strong evidence for direct regulation of these metabolites by Klf10.VIP were classified according to their chemical classes . The peror serum A and foror serum B. This rKlf10 KO mice. We found 23% amino acid change, including L-threonine, L-lysine, L-valine, L-serine, L-arginine, L-asparagine, L-methionine, L-ornithine, and derivatives such as N-acetyltryptophan, indicating that amino acid metabolism was clearly impacted. Metabolic characterization in serum identified compounds from glycolysis and the Krebs cycle, including a slight decrease in D-glucose and L-lactic acid and a significant increase in citric acid, isocitric acid, oxoglutaric acid, fumaric acid, and L-malic acid . Together, changes in chemical classes of sugars and organic acids represented 3% and 10% of the overall changes, respectively , phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphocholine (LPC), and platelet-activating factor (PAF) or PC (O-) A. There ectively A, suggesKlf10 KO versus WT mice . In the Krebs cycle, citric acid, isocitric acid, succinic acid, fumaric acid, and L-malic acid were detected and demonstrated fluctuations, together with amino acids . Glucose 1-phosphate (G1P) and glucose 6-phosphate (G6P) were differentially discriminated as VIP and fed the glycogen storage metabolism. Interestingly, nucleotides–nucleosides, derivatives such as guanosine, guanine, inosine, and uric acid that are involved in purine metabolism, exhibited a significant decrease in Klf10 deficient mice. In this regard, lipids, phospholipids, sugar, amino acids, nucleotides, and organic acids were determined to be altered in the soleus of Klf10 KO mice , L-arginine, deoxycarnitine, thiamine monophosphate, platelet-activating factor, PC(O-16:1/2:2), and N-methyltryptamine were oppositely impacted  and allowed for the identification of potential biomarkers that are reflective of decreased Klf10 expression and/or function. To highlight potential biomarkers of interest in serum and soleus, we applied a strict cut-off of two-fold change and p-value < 0.05  described here may serve as potential robust biomarker predictors for lack of Klf10 expression and/or function.In serum, we found a set of 17 metabolites highly regulated, which mostly belong to lipids, phospholipids, and amino acids—derivatives and aromatic compounds—amines classes. In a global picture, we observed that metabolites from lipids and phospholipid classes (PE(22:6/18:1), PE(20:4/18:1), PE(16:0/20:4), tetradecanoylcarnitine, glycocholic, acid, and cholic acid were upregulated whereas metabolites from aromatic compounds-amines class  and amino acids-derivative compounds  were downregulated. Some others, such as indoleacetic acid and biotin, were upregulated. In serum, guanidoacetic acid and N-acetyltryptophan from amino acid-derivative groups were also upregulated. All dysregulated metabolites  and amino acids–peptides derivatives .In the soleus, we identified nine metabolites exhibiting a two-fold change but only three with a Klf10 KO mice. Indeed, the analysis of both serum and soleus in Klf10 KO mice succeeded in detecting a total of 224 and 246 altered metabolites, respectively. The PLS-DA model for serum revealed 96 variables with importance in projection (VIP) and 88 VIP for the soleus, which contributed to establishing the models with robust values. We found a drastic change in metabolites that clearly separated Klf10 KO from wild-type mice in each biological sample. We noticed an increased number of metabolites significantly regulated in serum when compared to the soleus, and the overall data demonstrated a profound metabolome alteration in Klf10 KO compared to WT mice.In this study, using UHPLC-Mass Spectrometry, we conducted a serum and soleus metabolic analysis to establish a specific metabolic signature of Klf10 KO mice affected different categories of compounds that belong to various chemical classes, such as lipids, phospholipids, amino acids, aromatic compounds, vitamins, hormones, and others. The proportion diagram of compound class repartition is mostly similar between serum and soleus, except for sugar, phospholipids, nucleotides-nucleosides derivatives, and vitamins–cofactors–hormones classes. This suggests that Klf10 deficiency impacts carbohydrate, nucleic acid, and lipid metabolism differently in respect of these two samples. In this context, results indicate a metabolic change in (1) fatty acid metabolism driven by phospholipids, lipids, and acylcarnitine modifications, (2) amino acid metabolism by glucogenic amino acid fluctuations, and (3) energetic metabolism related to glycolysis and Krebs cycle metabolite alterations. Changes in serum fatty acid and acylcarnitine levels could have a high predictive value for segregating Klf10 KO and WT mice, as shown by Houtlooper et al. in plasma [Klf10 KO mice  and succinate dehydrogenase  enzymes [Klf10 KO mice in Klf10 KO mice reproduces Warburg metabolism that is related to the functions of KLF10 described in the pathogenesis of various cancers [Our previous study demonstrated that  enzymes . Based o cancers . FurtherKlf10 deletion in mice induced alterations in a large panel of molecules implicated in the regulation of cell activities at different levels . We proceeded to compare the metabolic signatures of Klf10 KO mice to specific metabolic profiles characterized in aged mice, where amino acids, glucose, and lipid metabolism are affected in both liver and muscle [Klf10 KO serum . A similar situation is recalled in muscle of aging mice, where some metabolic changes implicate glycogen metabolism and anaerobic glycolysis, leading to age-related insulin resistance profiles [Klf10 KO mice. These observations provide strong evidence that Klf10 KO mice share features with known aging phenotypes in skeletal muscle, particularly with regard to impaired mitochondrial function, metabolome footprint, and insulin (and mTOR) signal pathway regulation. In summary, Klf10 KO mice refer to the deterioration of skeletal muscle cell structure and organization that coincide with biological functions altered with age.Aging is a late-life state characterized by a general degradation of cell function leading to declines in health. Increasing evidence shows that mitochondrial dysfunctions contribute to the aging process ,39, incld muscle , as welld muscle . In a mod muscle . We alsoKO serum . Similarunctions ,47. Impoprofiles ,48. CompKlf10 deletion. Extensive analysis of the metabolome in serum and soleus allowed us to identify metabolite groups associated with Klf10 deficiency. At a high level, lipids and amino acids were highly disrupted classes of metabolites altered in both the serum and soleus of Klf10 KO mice. Some metabolites appeared highly discriminatory and, as such, may represent ideal biomarkers. Present in both matrices, hippuric acid, a derived amino acid, was highly decreased in serum and soleus. Tracking this metabolite in real-time could provide additional information about compartment exchanges and pathophysiology. Additionally, some metabolites were sample-specific such as cholic acid and glycocholic acid that were altered in serum, and caprylic acid and urocanic acid that were altered in the soleus. Among these sets of interesting candidate biomarkers  with a light/dark cycle of 12 h. Animals had free access to water and were fed with standard laboratory chow ad libitum. The protocol was approved by the French ministry of higher education, research and innovation (Permit Number: DUO-4776) and the local ethics committee Comité Régional d’Ethique en Matière d’Expérimentation Animale de Picardie .The generation of escribed . To be cKlf10 and the 4-fold higher Klf10 expression in soleus compared to EDL (28). Thus, the soleus of 3-month-old female mice were isolated from Klf10 KO (n = 10) and WT (n = 9) mice, immediately frozen, and subsequently stored at −80 °C until metabolomics analysis. For metabolite extraction, 1 mL of MeOH:H2O (50:50) was added to frozen muscle samples and agitated for 30 min at 4 °C followed by centrifugation at 5000× g for 15 min at 4 °C. In total, 900 µL of supernatants were collected for solvent evaporation in a SpeedVac  at 35 °C for 2 h. The remaining supernatants were pooled for quality control (QC) samples and evaporated.Slow twitch-muscle (soleus) was utilized given the defects observed in this glycolytic muscle following the deletion of Klf10 KO (n = 9) and WT (n = 10) mice were prepared as previously described [Serum samples from escribed . Approxi2O (1:9) for RP-LC columns and 70 µL of ACN:H2O (9:1) for HILIC columns.For UHPLC-MS, evaporated samples were solubilized with 70 µL of solvent MeOH:HAs previously described , UHPLC-M® XB-C18  and a HILIC (Hydrophilic Interaction LIquid Chromatography) Waters Cortecs® .The chromatography system was equipped separately with two columns: an RP-LC (Reverse Phase Liquid Chromatography) Phenomenex KinetexThe autosampler temperature  was set at 4 °C.A HESI (Head ElectroSpray Ionization) source was used for both chromatography systems operated in positive (ESI+) and negative (ESI−) electrospray ionization modes (one run for each mode).m/z = 200), which ranged from 58.0 to 870.0 m/z, with an automatic gain control target of 105 charges and a maximum injection time (IT) of 250 ms. Xcalibur 2.2 software  controlled the system.Detection was performed with a full-scan acquisition at 70,000 resolution  by integrating selected product ion chromatographic peak areas, which were exported to an excel file containing the areas of each metabolite and finally normalized to the total sum of integrated metabolites. Detected peaks were identified using accurate m/z and retention time (RT) compared to the internal database made by the Mass Spectrometry Metabolite Library of Standards . According to Rathato-Paris et al. [The acquired data were processed using Xcaliburs et al. , our annhttps://www.metaboanalyst.ca, accessed on 20 September 2021) [Once the RP+/− (positive or negative ionization)-LC and HILIC+/−-LC lists were generated, the web interface Metaboanalyst (er 2021)  was usedhttp://jvenn.toulouse.inra.fr/app/example.html, accessed on 20 September 2021) [A Venn diagram (er 2021)  was used®15 software . The model qualities were evaluated by R2Y (goodness of fit), Q2 (goodness of prediction), and CV-ANOVA . The PLS-DA models were improved according to the regression coefficients of each metabolite that expressed how strongly the Y (WT or Klf10 KO) is related to the X-variables (metabolites). According to these criteria, metabolites (variable with importance in projection = VIP) with a greater contribution to the separation of the groups were identified.In the first intention, a principal component data analysis (PCA) was performed in order to observe and exclude outlier samples. Once these outliers were excluded, a supervised multivariate statistical data analysis based on PLS-DA  was then performed on the metabolomics data generated by RP and HILIC-LC using SIMCAhttps://www.metaboanalyst.ca, accessed on 20 September 2021). The results were considered to be significantly different when the p-value was ≤ 0.05. The fold changes were calculated.Univariate analysis was performed on metabolites identified by the PLS-DA using a t-test on Metaboanalyst  as previously described by Beauclercq S. et al. . A cut-oIn conclusion, the results of the studies presented here support that KLF10is highly related to the dynamic processes of metabolism regulation that manage energy (glucose and lipids) production, which are essential to cellular physiology. Considering KLF10′s involvement in gene expression, signal transduction, and metabolism , it has


Carbohydrate antigen 19-9 (CA 19-9) is a tumor marker used clinically to support the diagnosis and recurrence of pancreatic adenocarcinoma. CA 19-9 levels may be found to be elevated too in benign conditions, including hepatobiliary diseases such as choledocholithiasis, cholangitis, or cholestasis of any etiology. In this case, we are presenting a case of a 66-year-old man who has presented with diffuse abdominal pain and has been then found to have choledocholithiasis and obstructive jaundice treated with biliary sphincterotomy and balloon extraction. In the setting of extensive unclear gastroenterological surgeries, tumor markers were obtained, and the results revealed markedly elevated CA 19-9 with negative other tumor markers and a negative workup for malignancy. After reviewing the literature, we found that although CA 19-9 has been studied in diagnosing pancreatic and biliary malignancies, the serum level may also be elevated in many other benign conditions. As a result, the results must be individualized according to the clinical scenario of each patient. Carbohydrate antigen 19-9 (CA 19-9) is a tumor marker, a sialylated lewis antigen, which is used clinically as a diagnostic indicator in supporting the diagnosis and recurrence of pancreatic adenocarcinoma, first identified in cultured human colonic carcinoma cells by Koprowski in 1979 . CA 19-92), exploratory laparotomy with bowel resection: Hartmann’s procedure (2013), reversal of Hartmann’s procedure with massive lysis of adhesions status post small bowel obstruction (SBO), followed by two admissions for SBOs (2017 and 2018) that were treated nonoperatively, presented to the emergency department with diffuse abdominal pain of three days duration. The patient described the pain as constant, non-radiating, 8/10 on a pain scale, worsened by sitting upright or lying on the right side and with consumption of food, and associated with nausea and non-bloody, non-bilious vomiting. The last bowel movement was the day before the presentation. Home medications included ramipril 10 mg, sitagliptin 50-1000 mg, and sodium polystyrene sulfonate 15 g/60 ml suspension. He had no known drug allergies. He denied any personal or family history of gastrointestinal cancers or cancers of other organ systems. He denied any fever, chills, or night sweats. He reported a 60-70 lbs weight loss over the past year with an otherwise unremarkable review of systems. He last consumed alcohol a year ago but denied a history of tobacco or illicit drug use. On initial management, his vitals showed a temperature of 36.7 Celsius, a heart rate of 75 beats/minute, blood pressure of 150/90, a respiratory rate of 18 breaths/minute, and oxygen saturation of 99%. His abdominal examination was remarkable for generalized abdominal tenderness that was more prominent in the periumbilical region without guarding, rigidity, or rebound tenderness.A 66-year-old man with a past medical history of hypertension, hyperlipidemia, diabetes mellitus, obesity , cardiovascular, pulmonary, and musculoskeletal examinations were unremarkable. Laboratory data demonstrated a white blood cell count of 9.11 K/mcl (baseline: 6.39-7.74 K/mcl), alkaline phosphatase 406 U/L, lipase 91 U/L (baseline: 34-39 U/L), total bilirubin 6.4 mg/dl, aspartate aminotransferase (AST) 112 U/L, alanine transaminase (ALT) 371 U/L. In the emergency department, he received an intravenous push of famotidine 20 mg, Zofran 4 mg, and 1 liter of normal saline. The computed tomography (CT) abdomen/pelvis without contrast shown in Figure The patient was scheduled for magnetic resonance cholangiopancreatography (MRCP), shown in Figures  Following ERCP, the patient had rising liver function tests and complained of right upper quadrant pain, possibly due to ampullary edema secondary to sphincterotomy and instrumentation. He was started on intravenous antibiotics with ciprofloxacin and Flagyl. On day 3, laboratory data demonstrated total bilirubin of 7.2 mg/dl, direct bilirubin of 6.2 mg/dl, alkaline phosphatase 411 U/L, AST 103 U/L, and ALT 312 U/L.Further laboratory investigations were unremarkable for hepatitis B virus, hepatitis C virus, human immunodeficiency virus, alpha-fetoprotein tumor marker, and carcinoembryonic antigen (CEA). However, the CA 19-9 was markedly elevated at 2192 U/ml. His hospital stay was extended to monitor liver function tests. He continued receiving intravenous antibiotics (ciprofloxacin and Flagyl) and intravenous Zofran for pain management. Throughout the hospital stay, he received gastrointestinal prophylaxis with pantoprazole 40 mg orally daily and prophylaxis for deep vein thrombosis with low molecular weight heparin. Sitagliptin was discontinued in the setting of pancreatitis, and he has been prescribed metformin in light of well-controlled type 2 diabetes mellitus (H1ABc 5.8). He did not receive any blood transfusions during this hospitalization. On day 6, liver function tests trended down, and he was able to tolerate diet and reported only mild right upper quadrant abdominal pain. He was discharged on a six-day prescription of oral metronidazole and ciprofloxacin with a scheduled outpatient follow-up.CA 19-9 is made mainly by the pancreatic ductal cells and the biliary tract. It can also be made by epithelial cells of the stomach and salivary glands. As mentioned above, CA 19-9 is mainly associated with pancreatic cancer, specifically pancreatic adenocarcinoma. However, it is also elevated in various benign, malignant, gastrointestinal, and extra-gastrointestinal diseases -5. CA 19Benign diseases with highly elevated CA 19-9 are relatively rare. This literature review addresses some cases with elevated CA 19-9 without malignancy. In 2002, Katsanos et al.  reportedAdditionally, Sanchez et al.  reportedThe clinical characteristics and underlying reasons for CA 19-9 elevation in the absence of malignant or pancreatobiliary disease were examined in a retrospective study by Kim et al. . FollowiIn conclusion, CA 19-9 is a tumor marker that has received substantial research and widespread use for the diagnosis and prognosis of pancreaticobiliary cancers. Clinicians should exercise caution when interpreting CA 19-9 results because levels may be significantly influenced and elevated in many other benign diseases, making it difficult to distinguish between benign and malignant causes of elevated CA 19-9. These results must be viewed in the context of the clinical scenario of each patient.


Toll-like receptors (TLRs) are important components of the innate and adaptive immune systems, and abnormal TLR expression has been linked to a variety of cancers. However, there was a lack of clarity on the association of TLR stimulation with the carcinogenesis of cancer. The study's goal was to analyse the clinical importance of TLRs expression at the mRNA level in pan-cancer datasets, as well as the link between TLR expression and carcinogenesis, progression, and clinical prognosis.The expression profile of TLRs derived from UCSC pan-cancer data was analysed in multiple dimensions, including clinical analysis, immunological subtype analysis, tumour microenvironment (TME) analysis, tumour stem cell correlation analysis, and drug sensitivity analysis. Additionally, we analyse protein-protein interactions, functional enrichment, and chromatin accessibility, as well as TLR expression in single-cell sequencing data.Our multi-omics analysis results imply that TLRs may operate as a biological marker for carcinogenesis and progression, a potential target for anti-tumour therapy, and a prognostic biomarker, laying the theoretical groundwork for future translational medicine research.Toll-like receptors (TLRs) are key factors in the process of the innate and adaptive immune response, and their aberrant expression of TLRs have been widely reported in various cancer. However, the association between TLRs stimulation and tumorigenesis of cancer has not been well clarified.In this study, in the pan-cancer data, integrated TLR family gene expression analysis, clinical correlation analysis, immune subtype correlation analysis, tumour microenvironment correlation analysis, tumour stem cell correlation analysis, and drug sensitivity correlation analysis were performed.TLRs play an important role in the development of tumours and can be studied in depth as potential prognostic markers.TLRs are involved in the formation of malignancies and can be explored in further detail as potential prognostic indicators. They are best known for their pathogen defense function, which involves the recognition of damage-associated molecular patterns (PAMPs) such as bacterial lipopolysaccharide and flagellin, as well as RNA produced during virus replication. TLRs, on the other hand, are involved in the identification of several endogenous ligands, including damage-associated molecular patterns (DAMPs) generated by dying or wounded cells . The humTLRs are broadly dispersed in numerous immune cells, including macrophages, dendritic cells (DC), neutrophils, B cells, epithelial and endothelial cells , as wellGiven that genes in the TLR family have distinct ligands and downstream mechanisms of action in various malignancies, the precise activities of several of these genes remain unknown . Conside2.2.1.http://xena.ucsc.edu/). Among the above, the mRNA level expression was quantified using Fragments per Kilobase of transcript per Million mapped reads (FPKM), and survival data were downloaded from TCGA cancer datasets collected by Genomic Data Commons (GDC) , whereas the phenotype information was obtained from GDC-collected TCGA cancer datasets . Additionally, we downloaded immunological subtypes and stemness scores assessed by DNA methylation and RNA expression from the TCGA Pan-Cancer (PANCAN) datasets . After translating the ensemble ID to the gene symbol  in R, the expression profiles of TLR1, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, TLR10, TRL12P, and TLR8-AS1 were retrieved for further study.Our analysis is based entirely on data from existing databases. The mRNA sequencing data, clinical data , immunological subtype, and stemness score information for 33 types of tumours  and pairhttps://gdc.cancer.gov/about-data/publications/ATACseq-AWG), which included 23 TCGA cancer types (Table S2), 410 tumour samples, 796 chromatin accessibility atlases, and 562,709 transposase-accessible DNA elements [Additionally, we downloaded all ATAC-seq count matrices for cancer types from the GDC data portal . Besides, FC (fold change) values of TLR expression level between paired samples were obtained for further detection. Then, we utilised boxplots and a heatmap to illustrate the differences in TLR expression between cancer and surrounding tissues: The colour in the heatmap denotes log2FC value. Colour red means that the gene is up-regulated in the tumour, Colour blue shows that the gene is down-regulated, and colour white indicates that there is no variation in expression levels. In general, a TLR expression level with a log2FC value > 1 or < −1 was considered differentially expressed. In view of the putative regulatory relationship between genes of the same family, we ran liner correlation analysis with 2.3.p < .05). The COX proportional hazard model with χ2 test was carried out to perform multi-factor analysis and to evaluate effected TLR genes on the prognosis using hazard ratio. Furthermore, liner correlation analyses of TLR expression with clinical outcomes such as tumour stage, grade, and recurrence were conducted. When the P value on both sides was less than .05, the difference was declared statistically significant.The Kaplan-Meier method was utilised to generate survival curves of patients with above and below the median TLR expression, respectively, using follow-up time as the horizontal axis and survival rate as the vertical axis in a study of 33 cancer patients. We also used the log-rank test to look for any significant changes in the factor of survival time , IFN-gamma Dominant (Immune C2), Inflammatory (Immune C3), Lymphocyte Depleted (Immune C4), Immunologically Quiet (Immune C5), and TGF-beta Dominant (Immune C6) . We used2.5.The microenvironment scores were calculated using the ESTIMATE  algorithm . Three s2.6.Cancer stem cells (CSCs) are tumour cells that have the ability to self-renew and differentiate into different types of tumour cells. Typically, these cells are identified as having the potential to promote tumour formation, progression, recurrence, and resistance to treatment. Stemness scores for tumour samples are generated using DNA methylation (DNAss) and mRNA expression (RNAss) data. They range from 0 to 1 and are calculated using the one-class logistic regression (OCLR) technique . In gene2.7.https://discover.nci.nih.gov/cellminer/) offered processed datasets of matched mRNA sequencing and compound activity data  for drug sensitivity study [Cell Miner database  is defined as the total number of non-synonymous mutations inside the exon coding region per megabase of the genome studied. Currently, it is considered that the greater the TMB value, the more types and numbers of neoantigens created by the tumour, and thus the greater the probability of being recognised by the immune system. As a result, tumour patients with a high TMB (TMB: >20 mutations/mb) are more susceptible to immune therapy, such as PD-1/PD-L1 inhibition . A thorohttp://www.cbioportal.org) [Moreover, we used the cBioportal browser   to ident2.9.https://www.proteinatlas.org/) [www.linkedomics.org/) [The first step was to determine the overall protein level expression of TLR family members in cancer and neighbouring normal tissues using immunohistochemistry (IHC) results from the Human Protein Atlas database  . To detecs.org/) .2.10.http://geneontology.org/) [https://www.genome.jp/kegg/) function enrichment analysis [We began by annotating TLR genes using GO   and KEGGanalysis  to deterhttps://string-db.org/) [Then, using the methodology described by Sanchez-Vega et al., we analysed the prevalence of somatic mutations in canonical pathways to validate the pathway modification linked with the TLR family across different tumour types . The 33 db.org/) .2.11.Because high-throughput mRNA sequencing employs tissue samples made up of a mixture of millions of cells, the results represent an average of gene expression, reflecting only the expression pattern in the numerically dominant cell population. In comparison to conventional mRNA sequencing, single-cell RNA sequencing (scRNA-Seq) generates an individual genetic profile for each cell and identifies uncommon cells from diverse tumour samples, allowing for the exploration of features specific to a single cell. During this process, individual cells are isolated, their transcripts are captured, and sequence libraries are generated, as well as individual cells are mapped to the transcripts.https://www.ebi.ac.uk/gxa/sc/release-notes.html) [In our study, we used scRNA-Seq data from various common tumour tissues, including colorectal cancer (Accession no. E-MTAB-8410)  (Accessies.html) . Results2.12.The Assay for Targeting Accessible-Chromatin with High-Throughput Sequencing (ATAC-Seq) technique  is a rel2.13.https://www.perl.org), R software , and Bioconductor packages (http://www.bioconductor.org/packages/release/bioc/html/impute.html) to further process and analyse raw data. And a P value less than .05 for the two tails was considered significant.We used Perl , LUAD (P0.001), PRAD (P0.001), BRCA (P0.001), and LIHC (P0.001), and TLR8-AS1 expression was specifically decreased in LUSC (P0.001) and PRAD (P0.001). TLR1, TLR3, TLR4, TLR5, and TLR12P were all down-regulated in the majority of malignancies. TLR3 was not up-regulated in any of the cancer tissues tested, whereas TLR1 was inversely up-regulated only in KIRC (P0.01), CHOL (P0.05), and GBM (P0.001). Only KIRC (P0.001) and GBM (P0.05) demonstrated increased TLR4 expression. TLR5 expression was significantly increased in LIHC (P0.001), GBM (P0.01), and CHOL (P0.001). TLR12P transcriptional expression was significantly increased in THCA (P0.01) and KIRC (P0.001). Additionally, TLR6, TLR7, TLR8, TLR9, and TLR10 expression was up- or down-regulated in different cancer species .R = 0.79), TLR1 and TLR7 (R = 0.72), TLR1 and TLR8 (R = 0.72), TLR7 and TLR8 (R = 0.69), and TLR1 and TLR2 (R = 0.66), showing that those genes share some type of expression or functional relationship. Given that TLR4 and TLR6 have been shown to form heterodimers, our co-expression data corroborated the relationship [Additionally, there is a generally positive connection between the co-expression of TLR genes, demonstrating that TLR genes have a universal co-expression relationship . Notablytionship .3.2.Kaplan-Meier and COX regression analysis demonstrated that all TLR genes, at the mRNA level, were capable of classifying patients into groups with a good or bad prognosis in at least three forms of cancer. The 15 survival analysis results with the lowest P values are depicted in p < .001) indicated a longer survival duration for patients, whereas low TLR2 expression in LGG (p < .001) suggested a shorter survival period for patients. Additionally, increased TLR3 expression was related to a favourable prognosis in KIRC (p < .001) and SKCM (p < .001) samples, but not in LGG (p < .001).For example, high TLR2 expression in SKCM , GBM , KIRC , ACC , THCA , LGG , and KICH . This occurrence may be related to the lncRNA's regulatory functions. TLR7 expression was found to be a significant risk factor for six different forms of cancer: BLCA , KIRC , LGG , KIRP , and THYM . Additionally, TLR2 and TLR6 expression were found to be poor predictive markers for five distinct forms of cancer, respectively.Cox proportional hazards regression was used to further investigate the relationship between TLR and clinical outcomes . Among tFigure S1), implying the involvement of the TLR family in tumour progression mechanisms, which may have implications for diagnosis and clinical risk prediction.Following that, we conducted investigations of the connection between TLR expression and clinical events in order to further establish the clinical relevance of TLR family expression levels. In terms of clinical stages, we discovered that the expression levels of multiple TLR family members, including TLR3, TLR4, TLR8, TLR10, and TLR8-AS1, were significantly different between stage I and IV of BRCA, COAD, HNSC, and STAD (3.3.p < .001). TLR1, TLR2, TLR3, TLR4, and TLR5 expression levels were high across the six immune subtypes in the pan-cancer data; TLR6, TLR7, TLR8, and TLR10 expression levels were moderate; and TLR9, TLR12P, and TLR8-AS1 expression levels were extremely low. In general, the levels of expression of various genes were relatively high in C6. Figure S2A).To a certain extent, the immunological subtype of cancer is strongly associated with its incidence, progression, and clinical prognosis. We discovered that TLR gene expression levels varied considerably between the six immunological subtypes of pan-cancer (p < .001), and the overall expression level of TLR8-AS1 also increased (p < .05). TLR gene integral expression levels were lower in LIHC, SARK, and SKCM, indicating that TLR genes were substantially expressed in C2 or C6 but were absent in C1 . Given that the TLR family is associated with immune-related genes, we hypothesised that detecting TLR expression fingerprints across different cancer types or samples could aid in developing more accurate and effective immunotherapies.TLR gene expression varied significantly among the six immunological subtypes found in various cancer types. For instance, whereas the expression trend in LUAD was generally identical to that in pan-cancer, TLR9 was not differentially expressed amongst any of the six immunological subtypes. Additionally, TLR2 gene expression was significantly higher in C3 and C4 , consistent with the PRRs function of the TLR family.In addition to tumour cells, there are immune and stromal cells in tumour tissue, which form the micro-environment, and constant communications between tumour and non-tumour cells jointly regulate the growth of neoplasm. The association between TLR gene expression levels and the stromal score, immunological score, and estimation score was examined in 33 TCGA tumour samples. It may allow us to better understand their involvement in the tumour microenvironment and purity, which were found to be substantially connected with clinical, genetic, and biological aspects of individuals with malignancies. Almost all malignancies had a substantial positive correlation between TLR gene expression and the number of interstitial cells, most notably in the study results for TLR1, TLR2, TLR4, TLR6, TLR7, TLR8, and TLR10 genes  and DNAss (derived from DNA methylation signature) to evaluate stemness traits that indicate self-renewal and dedifferentiation potential. Due to the fact that RNAss and DNAss use distinct input files, their findings may be inconsistent. Correlation examination of TLR expression and stemness index, on the other hand, demonstrated that both RNAss and DNAss had a high degree of connection with TLRs for the majority of malignancies.Figure S4A) in BLCA, BRAC, CESC, COAD, DLBC, ESCA, KIRP, LGG, LUAD, LUSC, READ, and STAD tumours, implying that tumours with high TLR expression exhibited a higher degree of differentiation and lacked cancer stem cell characteristics. It's worth noting that TLR1, TLR2, TLR3, TLR7, TLR8, and TLR9 were adversely connected with RNAss in the majority of cancers, but favourably correlated with RNAss in a very small number of tumours . Additionally, TLR8-AS1, a lncRNA, demonstrated a positive connection with RNAss in ACC, CHOL, SARC, TGCT, and THYM, which was inconsistent with the expression trends of other TLR genes. Given this, we hypothesised that TLR1, TLR2, TLR3, TLR7, TLR8, and TLR9 would be involved in a negative regulatory mechanism mediated by TLR8-AS1 that contributes to the maintenance of cancer stem cell features.TLR gene expression was significantly negatively associated with RNAss . DNAss analysis revealed that while high TLR gene expression was adversely connected with stem cell features in the majority of malignancies, it was positively correlated in a few tumours . As a result, the association between TLRs and DNAss cannot be established definitively.Although there was a considerable association between TLR family expression and DNAss in cancers, the positive or negative correlations between 33 tumours were quite diverse , and the correlation pattern remained consistent with the pan-cancer trend. TLR4 and TLR7 were the most strongly correlated with tumour stemness features, but TLR9 had an undefined connection with the stemness index in various malignancies, and TLR4, TLR7, and TLR8 were the most associated with the tumour microenvironment. The verification results above were consistent with the findings of the study on pan-cancer.Additionally, we examined the link between TLR family genes and the intratumor microenvironment and stemness index in single tumour data from BRCA, COAD, HNSC, and LIHC  of the various lines of cancer cells derived from the Cell Miner database and the TLR family of gene expression were employed. And 3.7.The TMB (in the form of log10 (TMB + 1)) of 33 TCGA tumours was plotted in Co-expression analysis  revealedAdditionally, as illustrated in 3.8.Figure S6).To begin, we determined the TLR family's protein expression using the Human Protein Atlas database. Due to a lack of TLR family immunohistochemical samples, only TLR3, TLR4, TLR7, and TLR8 staining results in colorectal cancer, breast cancer, prostate cancer, and normal tissue were exhibited , which w3.9.The functional enrichment analysis  was perfAs a large number of studies reported that TLR blockade altered the activity of c-MYC , a proteMYC, MYCN, MGA, MAX, MLX, MNT, and MXI1 were the genes examined in the c-MYC pathway, where mutations in MYC and MYCN were defined as oncogenes, and mutations in other genes were characterised as tumour suppressor genes. Clearly, MYC was the most frequently changed gene across many tumour types, followed by MGA. MYC and MYCN had the largest frequency of amplification, while MGA had the highest frequency of epigenetic silencing, whereas other genes were primarily mutated. MYC mutations were frequent in gynaecologic cancers , breast cancers , and HPV-negative head and neck cancers . Furthermore, MGA mutations were most frequently seen in lung cancer , prostate cancer , and HPV-negative head and neck cancer . . On the other hand, lung adenocarcinoma  exhibited the greatest cumulative c-MYC pathway change frequency among all tumours. Other malignancies with a high prevalence of c-MYC pathway mutations were HNSC HPV- (85%), TCGT seminoma (72%), and high CN UCEC (70%). This finding indicated that the interaction of TLR and its downstream components in the c-MYC pathway played a role in the incidence and progression of these malignancies. Additionally, the String database's protein-protein interaction network established that the TLR family was intimately tied to not only the well-recognised NF-κB pathway but also the c-MYC pathway, especially MYC protein .3.10.Figure S7A and S7B). In this investigation, we used the online procedure of Single Cell Expression Atlas to detect TLR gene expression, and some of the most significant results are shown in Figure S7 . It was shown that TLR2 and TLR4 were predominantly expressed in myeloid cells, including SPP1+ B myeloid cells, proliferative myeloid cells, pro-inflammatory myeloid cells, and anti-inflammatory myeloid cells, as previously described. Additionally, TLR10 expression was localised in CD19 + CD20+ B cells. TLR7 and TLR9, on the other hand, did not aggregate in any cell clusters, despite the fact that TLR7 was uniformly low expressed and TLR9 was virtually completely absent.According to Lee HO et al. , tumour Figure S11A), however, the data set did not include cell classification information. To our surprise, the link between TLR2 and TLR4 colocalization expression was still apparent in distinct subtypes of lung cancer endothelial cells . Furthermore, the results above were similar to an HNCSS investigation using the scRNA-Seq technology, in which 164,276 cells from peripheral and intratumoral immune populations isolated from patients with HPV- and HPV + HNSCC were sequenced  [In another study developed by Bassez A et al. , their sure S12) .3.11.Figure S13-S14) in several tumour cell lines to determine chromatin availability in pan-cancer. Consistent with the high level of mRNA expression, ATAC-Seq analysis revealed open regions with a dense population of peaks in all examined cell lines surrounding the TLR2 locus, near chr4:153700000 . While the TLR4 gene had open sections in all cell lines, the density of peaks was lower than that of TLR2, and various cell lines had a range of TLR4 chromatin accessibility positions . Near chr6:117900000, the NP-1 and NP-2 cell lines (2 human glioma cell lines) demonstrated more accessibility than the H1-hESC-1, H1-hESC-2, H9-hESC-1, H9-hESC-2 (4 human embryonic stem cell lines), MSiPS-1, and MSiPS-2 cell lines (2 human pluripotent stem cell lines). While LNCaP-1 and LNCaP2 (2 human prostate cancer cell lines) and H1437-1 and H1437-2 (2 human lung adenocarcinoma cell lines) demonstrated an open TLR4 gene region close to chr6:118050000. These data suggested that the differences in TLR4 expression between cancer types may be due to post-transcriptional alteration. There were no identifiable ATAC-Seq peaks for TLR7 and TLR9 in all cell lines, implying that chromatin accessibility inhibition was the source of low TLR7 and TLR9 mRNA expression.We counted ATAC-Seq peaks in chromatin at TLR gene loci , TLR8 (R = 0.59), TLR1 (R = 0.56), TLR2 (R = 0.42), and TLR6 (R = 0.40), implying functional synergy. As demonstrated by a wide number of tests using heterodimers of TLR4 and TLR6 on the cell membrane. As for TME, in almost all malignancies except UVM, TLR4 is found to be positively linked with immune and stromal cell infiltration. Additionally, TLR4 expression is highest in C5 , out of six immunological subtypes, and is also much greater than other TLRs in C5. As previously stated, C5 has the lowest lymphocyte count and the greatest macrophage count [TLR4 is the most frequently reported oncogenesis and tumour growth gene. It is located on chromosome 9 and encodes a 95680 Da protein with 839 amino acids. Compared with other molecules in the TLR family, TLR4 is relatively highly expressed at the transcriptome level in pan-cancer studies, close behind TLR2. While the majority of 33 forms of cancer exhibit decreased TLR4 expression in comparison to normal tissues, only a few cancers  exhibit the opposite trend. Additionally, there is a correlation between TLR4 and TLR7 , TLR8 and TLR4 (R = 0.69), TLR4 and TLR10 (R = 0.53), and TLR6 and TLR6 (R = 0.52). TLR7 is capable to recognise single strand RNAs (ssRNAs). Following ligand attachment, the stimulated TLR7 initiates downstream signal transduction by activating the transcription factors NF-κB and IRF7 [TLR7, which plays a critical role in innate and adaptive immunity, is typically found as homodimers on macrophages, B lymphocytes, and mast cells. The TLR7 gene is located on chromosomes X:12,867,072–12,890,361 and X:12,885,202–12,908,499. TLR7 expression is moderate in pan-cancer, lower than that of TLR4. It is primarily up-regulated throughout all TCGA malignancies, with exception of LUAD, LUSC, COAD, and READ. Apart from TLR4, co-expression occurs between TLR7 and TLR1  motifs (CpG ODNs), a TLR9 agonist, can stimulate antitumor immunity by activating the NF-κB pathway , and incted CHK2 . Additioted CHK2 .R = 0.32). Although there are considerable differences in TLR9 expression between the six immunological subtypes, no trend in TLR9 expression can be detected due to the expression level being too low. Additionally, the TME score analysis demonstrates that the association between TLR9 and many types of cancer exhibits a range of features, including positive correlation, negative correlation, and irrelevance, but the positive correlation is the predominant finding. TLR9 is negatively connected with RNAss in the majority of malignancies, but positively correlated with RNAss in CHOL, KIRC, and THYM, and has no correlation with RNAss in ACC, LIHC, PCPG, and UVM. However, there is no link between TLR9 and DNAss. It exhibits positive and negative relationships with 13 types of cancer but is unrelated to DNAss in the other seven types of cancer. The results above show that TLR9 may play a variety of roles in carcinogenesis and progression, which warrants additional investigation.Notably, while TLR9 expression remains modest, it is up-regulated in all 33 tumours. The co-occurrence of TLR9 and TLRs is not as clear as that of other TLRs, and the most significant link is that of TLR6 co-expression , a new cytotoxic anticancer drug with DNA damaging and MAPK activating properties, can also promote tumour cell apoptosis of BRCA1 . Clearlyof BRCA1 . This crIn a word, the pan-cancer research demonstrates that the TLR family expression pattern is strongly correlated with TME , cancer stemness, and chemotherapy sensitivity, all of which vary among cancer types and subtypes. Additionally, TLR-activated signalling pathways result in the generation of cytokines, chemokines, and a variety of inducible molecules related to cell metabolism, indicating TLRs' significant function in tumour cells and the tumour microenvironment.via cell research, animal investigations, and clinical trials, as well as to validate TLR's utility as a potential clinical biomarker.Although the effect of TLR on tumour prognosis has been documented in a significant number of tumour studies, this is the first study to integrate and analyse the TLR family of genes at the pan-cancer level, and to investigate their association with tumour formation. The findings indicate that TLR, as a possible prognostic marker, merits more investigation and discussion. However, the following unavoidable limitations remain: 1) This study analyzes mRNA-seq data from a single database, and the sample size is insufficient; 2) As a pure bioinformatics study, this study lacks experimental validation at the cellular or animal level; and 3) The data we collected are predominantly from Caucasians, and ethnic heterogeneity makes expansion difficult. As a result of this study's premise, we want to further investigate the role of TLR in carcinogenesis and development In our research, we investigated the TLR family's functions at the transcriptomic, genomic, and proteomic levels in 33 TCGA malignancies and also validated them using single-cell data and chromosome accessibility studies. The results indicate that the TLR gene plays a role in tumour growth and has an effect on tumour cells' sensitivity to chemotherapy. As a consequence, we believe that TLRs, particularly TLR4, TLR7, and TLR9, may be useful as predictive biomarkers.Click here for additional data file.


Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) is associated with the prognosis of some tumors. It is possible to explore the mechanisms and functions of the DMRT3 gene in the cancer process, using bioinformatic approaches and experimental validation. We comprehensively explored the clinical and immunological characteristics of DMRT3. We identified that DMRT3 was aberrantly expressed in pan-cancer and may promote tumorigenesis and progression via different mechanisms. DMRT3 can be used as a therapeutic target to treat cancer in humans.Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) is associated with the prognosis of some tumors. It is possible to explore the role of DMRT3 in the cancer process using bioinformatic approaches and experimental validation. We comprehensively explored the clinical and immunological characteristics of DMRT3. The DMRT3 expression is abnormal in human cancers and correlates with clinical staging. A high DMRT3 expression is significantly associated with poor overall survival (OS) in KIRC, KIRP, LUAD, and UCEC. Amplification was the greatest frequency of the DMRT3 alterations in pan-cancer. The OS was significantly lower in the DMRT3 altered group than in the DMRT3 unaltered group (P = 0.0276). The DMRT3 expression was significantly associated with MSI in three cancer types and TMB in six cancer types. The DMRT3 expression was significantly correlated with the level of the immune cell infiltration and the immune checkpoint genes. The DMRT3 was involved in some pathways in pan-cancer. DMRT3 may play a role in chemotherapy and may be associated with chemoresistance. A ceRNA network of KCNQ1OT1/miR-335-5p/DMRT3 was constructed in LUAD. DMRT3 was significantly upregulated in the LUAD cell lines. DMRT3 was aberrantly expressed in pan-cancer and may promote tumorigenesis and progression via different mechanisms. DMRT3 can be used as a therapeutic target to treat cancer in humans. Globally, cancer is the leading cause of death and seriously reduces people’s chances of surviving . Lung adThe Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) is a member of the DMRT family, involved in determining the sexual dimorphism and reproducing sexually ,7. DMRT3In the present study, based on the expression, predictive value, clinicopathology, genetic alterations, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes), TME, immune-related genes, immune infiltration, MSI, TMB, and drug sensitivity in a variety of cancer types, we conducted a systematic analysis. Our findings suggest that DMRT3 may be of potential value in the tumor diagnosis and prognosis and may be used as a marker for immunotherapy.The DMRT3 mRNA expression map was constructed by the Human Protein Atlas (HPA) database (version: 20.1).http://timer.cistrome.org/ (accessed on 15 December 2022).It was investigated whether tumor and non-tumor tissues in various tumor types express DMRT3 differently, using the “Gene DE” module of TIMER (http://xenabrowser.net/datapages/) (accessed on 15 December 2022). RNAseq data from The Cancer Genome Atlas (TCGA) and GTEx, which were processed uniformly by the Toil process [R (Version 3.6.3) and ggplot2 [version 3.3.3]  were used for the statistical analysis and visualization . The mol process ,11. RNAs process .https://portal.gdc.com) (accessed on 15 December 2022). [p value, HR, and 95% CI of each variable through the ‘forestplot’ R package. All analysis methods and the R package were implemented by R version 4.0.3 [The RNA-sequencing expression (level 3) profiles and corresponding clinical information for 33 tumors were downloaded from the TCGA dataset (r 2022). ,14. The on 4.0.3 . The surhttp://gepia2.cancer-pku.cn/#index) (accessed on 15 December 2022), the DMRT3 expression data were obtained for cancers at different clinical stages [From the GEPIA 2 website  (accessed on 15 December 2022). The TMB and MSI were derived from [The RNAseq data (level3) and corresponding clinical information for 33 tumors were obtained from the TCGA database (accessed on 15 December 2022), the TCGA pan-cancer dataset was analyzed for the genetic alterations of DMRT3 [Using cBioPortal (http://timer.cistrome.org/)(accessed on 15 December 2022) was used to explore the correlation between the DMRT3 expression and the cancer-associated fibroblast infiltration using algorithms, such as the extended multidimensional immunome characterization (EPIC), microenvironmental cell population-counter (MCP-counter), cell type enrichment analysis (XCELL), and tumor immune dysfunction and exclusion (TIDE) algorithms, to explore the correlation between the DMRT3 expression and the cancer-associated fibroblast infiltration [The “Immunity” module of TIMER2  (accessed on 15 December 2022). SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 are the transcripts associated with the immune checkpoints [The RNAseq data (level 3) and corresponding clinical information for 33 tumors were obtained from the TCGA database (ckpoints . The expckpoints . A statickpoints . https://string-db.org/)(accessed on 15 December 2022). The following thresholds were set to obtain the bound proteins: network type as full network, active interaction source for the experiment, and a minimum required interaction score of low confidence (0.150). The significance of the network edges was set to evidence, while the maximum number of interactors displayed was set to no more than 50 interactors. We identified 17 proteins that bind to DMRT3. In addition, we analyzed genes with similar expression patterns to DMRT3 in pan-cancer by GEPIA2 and selected the top 100 genes as candidates. A GO enrichment analysis and KEGG pathway analysis of DMRT3, 17 binding proteins, and 100 candidate genes were performed on the DAVID website (https://david.ncifcrf.gov/)(accessed on 15 December 2022) [Proteins bound to DMRT3 were analyzed, based on the STRING website (version 11.5) (er 2022) .http://bio-bigdata.hrbmu.edu.cn/RNAactDrug/index.jsp) (accessed on 15 December 2022) was used to perform a drug sensitivity analysis of DMRT3 in pan-cancer. The omics screening condition was “expression”. The top five significantly positively correlated drugs and the top five significantly negatively correlated drugs were counted according to Spearman’s correlation coefficient.The RNAactDrug database (http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) (accessed on 15 December 2022) is a specialized single-cell sequencing database that provides different functional states of cancer cells at the single-cell level [CancerSEA (ll level . We downhttp://www.genome.ucsc.edu/cgi-bin/hgTracks) (accessed on 15 December 2022) was used to visualize the gene conversation of DMRT3 in vertebrates [The UCSC Genome Browser (version: 2021 update) (tebrates .http://mirtarbase.cuhk.edu.cn/) and TarBase V.8 (https://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r= tarbasev8%2Findex) (accessed on 15 December 2022) to predict the miRNAs that bind to target genes [http://starbase.sysu.edu.cn/) and LncBase Predicted v.2 (https://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2/index-predicted) (accessed on 15 December 2022) were used to predict the lncRNAs interacting with miRNAs.To explore the potential functions of DMRT3 in LUAD, we constructed a competitive endogenous RNA (ceRNA) network. Using miRTarBase  were used for the statistical analysis and visualization ,28. The R version 3.6.3, survminer package version 0.4.9, and survival package version 3.2-10  were used for the statistical analysis and visualization . The molOur laboratory preserved the LUAD cell lines, the A549 and PC9 cells, as well as the human normal lung epithelial cell line BEAS-2B, for use in this study. To identify the DMRT3 levels in the A549, PC9, and BEAS-2B cell lines, a qRT-PCR was performed, according to . The prip < 0.05 was considered a statistically significant difference.An unpaired t-test was used to compare the differences between the two groups, and the data were expressed as mean ± standard deviation. A Spearman rank correlation coefficient was used to assess the correlation between the two groups. The Kaplan–Meier method was used to assess the relationship between patient prognosis and the DMRT3 expression levels, and Based on the HPA dataset, we found that DMRT3 was highly expressed in tissues, including choroid plexus, parathyroid gland, and testis A. The DMp < 0.0001), KIRP , LUAD , and UCEC . As shown in p = 0.021), GBM , KIRC , KIRP , LUAD , PRAD , and UCEC . As shown in p < 0.0001), KIRP , LUAD , and UCEC .The relationship between the DMRT3 expression and prognosis in pan-cancer was analyzed using the univariate Cox regression analysis. As shown in p = 0.026), ESCA (p = 0.023), and READ (p = 0.013). As shown in p < 0.001) and LAML (p = 0.009), and negatively correlated with the TMB status of DLBC (p = 0.017), ESCA (p < 0.001), KICH (p = 0.016), and SKCM (p = 0.020). Overall, these results identify DMRT3 as a potential prognostic biomarker for various cancer types.GEPIA2 was used to investigate the relationship between the DMRT3 expression and tumor pathological stage. A high expression of DMRT3 was significantly associated with the advanced stages of KIRC, KIRP, SKCM, and THCA . The MSIn = 508) (p = 0.0276).Using the cBioPortal database, the highest rates of genetic alterations in DMRT3 were found in patients with pan-cancer, including lung cancer , bone cancer (amplification 4.46%), esophagogastric cancer , head and neck cancer , mature b-cell lymphoma (amplification 3.88%), myelodysplastic/myeloproliferative neoplasms (amplification 3.85%), ovarian cancer , melanoma , pancreatic cancer , hepatobiliary cancer , breast cancer , bladder cancer , skin cancer, non-melanoma (mutation 1%), prostate cancer (amplification 0.67%), renal cell carcinoma , non-small cell lung cancer , soft tissue sarcoma , endometrial cancer (amplification 0.29%), cancer of an unknown primary (deep deletion 0.26%), glioma (deep deletion (0.24%), and colorectal cancer (amplification 0.17%) A. The amThe tumor microenvironment (TME) is made up of four main components: the immune component, the vascular component, the extracellular matrix, and the stromal component, providing the crosstalk between the tumor cells and the microenvironment. There are many immune cells in the immune component, including T cells and B cells, while the stromal component consists of cancer-associated fibroblasts and mesenchymal stem cells . The TIMIn recent years, tumor immunotherapy has attracted a great deal of attention. We evaluated the correlation between the DMRT3 expression and eight immune checkpoint genes. The results show that the DMRT3 expression levels correlate significantly with the expression of the immune checkpoint genes in 31 tumors. DMRT3 was significantly correlated, positively or negatively, with the expression of the immune checkpoint genes in five tumor types, including BLCA, ESCA, KIRC, LUSC, and THYM. The results showed that the expression of DMRT3 correlated negatively with the expression of the immune checkpoint genes in 18 tumor types, including ACC, BRCA, CHOL, COAD, DLBC, GBM, HNSC, KICH, KIRP, LIHC, LUAD, OV, PAAD, PCPG, PRAD, READ, SARC, and SKCM . The resTo investigate the possible mechanisms of the DMRT3 action in pan-cancer, we extracted the top 100 genes with expression patterns similar to DMRT3, using GEPIA2 from the TCGA dataset, and obtained 16 genes co-expressed with DMRT3 using the STRING tool. We performed GO and KEGG analyses on these 116 genes. As shown in We further investigated the potential correlation between drug sensitivity and the DMRT3 expression using the RNAactDrug database. The DMRT3 expression was positively correlated with the drug sensitivity of tanespimycin, PD-0325901, PLX4720, trametinib, and refametinib . The DMRThe DMRT3 expression in GBM was significantly positively correlated with differentiation, and significantly correlated with the apoptosis, cell cycle, DNA damage, DNA repair, EMT, hypoxia, invasion, metastasis, and stemness; and the DMRT3 expression in RB was significantly positively correlated with the differentiation, inflammation, and metastasis A. As shoOur findings suggested that DMRT3 was up-regulated in most tumor types. However, there are no studies investigating the upstream regulatory mechanisms of DMRT3. Through a database analysis, we found that DMRT3 may be a target of miR-335-5p, while miR-335-5p may be a target of KCNQ1OT1. In LUAD, miR-335-5p was lowly expressed in LUAD A and thep = 0.0311) (p = 0.0268)  . The exp 0.0268) . These rUsing pan-cancer analyses, we are able to better understand the underlying molecular abnormalities of various cancers and to find biomarkers for early detection and the targeted treatment of cancer. TCGA analyzes 33 common tumor types using multi-omics data, offering the opportunity to uncover molecular aberrations across cancer types ,35. DMRT3 is aberrantly expressed in pancreatic ductal carcinoma, CRC, and LUAD ,37,38. HIn addition, we attempted to analyze the correlation between the DMRT3 mRNA expression levels and prognosis in pan-cancer. A high expression of DMRT3 is significantly associated with a poor OS in KIRC, KIRP, LUAD, and UCEC. A high expression of DMRT3 is significantly associated with a poor PFS in ACC, GBM, KIRC, KIRP, LUAD, and UCEC. A high expression of DMRT3 is significantly associated with a poor DSS in KIRC, KIRP, LUAD, LUSC, and UCEC. In summary, these findings suggest that DMRT3 may be a useful predictor of cancer prognosis for practical applications.However, there are no relevant studies on the DMRT3 gene alterations in human cancers. Using the cBioPortal database, we discovered that amplification is the most common DMRT3 alteration in pan-cancer. The co-occurrence of LINC01230, DMRT1, DMRT2, SMARCA2, DOCK8, DOCK8-AS1, VLDLR-AS1, KANK1, PUM3, and GLIS3 alterations was found to be observed in the DMRT3 alteration group.In this study, we first showed evidence of a potential association between the DMRT3 expression and staging, the MSI or TMB. The results showed that a high expression of DMRT3 in four tumors was significantly associated with the advanced tumor stage. Further analysis showed that the DMRT3 expression was significantly associated with the MSI in three cancer types and with the TMB in six cancer types. According to these results, the DMRT3 expression may affect cancer patients’ responses to immunocheckpoint therapy, which will contribute to a better understanding of immunotherapy mechanisms for cancer treatment.Researchers are currently focusing on the tumor microenvironment (TME). CAF plays various pro-tumorigenic functions during tumorigenesis, as a major component of the TME . TherefoDMRT3 and OAS3 are involved in human disorders of sex development (DSD) through the control of the ESR1 expression . DMRT3/5It is unknown whether the DMRT3 expression is associated with a drug sensitivity or resistance. However, we found that it was associated with several sensitivities using the RNAactDrug database, including tanespimycin, PD-0325901, PLX4720, GSK690693, PHA-793887, and NPK76-II-72-1. Therefore, we hypothesize that DMRT3 may play a role in chemotherapy and may be associated with chemoresistance.The DMRT3 expression in GBM was significantly positively correlated with the differentiation and negatively correlated with the differentiation, inflammation, and metastasis. The specific regulatory mechanisms of DMRT3 in GBM require further study. Our study has some limitations. Firstly, the relatively small sample sizes of some of the less common tumor types may lead to batch effects or inaccurate results. Secondly, the study demonstrates only the preliminary findings linking DMRT3 to cancer progression in various types of tumors, and more experiments are required to determine its precise molecular function in tumorigenesis. We constructed a ceRNA network of KCNQ1OT1/miR-335-5p/DMRT3 in LUAD. The findings in this paper are based on the bioinformatics analysis without experimental data. Further research is needed to verify our findings. The role of DMRT3 in cancer could be better understood with a large sample size and comprehensive analysis of DMRT3.There are a number of cancers where DMRT3 can be a potential prognostic factor. A number of pathways are modulated by DMRT3, including the MSI, TMB, cancer-associated fibroblast infiltration, immune checkpoint inhibitors, and drug sensitivity. A multifaceted role of DMRT3 in pan-cancer is highlighted in this study, providing a rationale for the use of DMRT3 as a new therapeutic approach.


Traumatic brain injury (TBI), known as mechanical damage to the brain, impairs the normal function of the brain seriously. Its clinical symptoms manifest as behavioral impairment, cognitive decline, communication difficulties, etc. The pathophysiological mechanisms of TBI are complex and involve inflammatory response, oxidative stress, mitochondrial dysfunction, blood-brain barrier (BBB) disruption, and so on. Among them, oxidative stress, one of the important mechanisms, occurs at the beginning and accompanies the whole process of TBI. Most importantly, excessive oxidative stress causes BBB disruption and brings injury to lipids, proteins, and DNA, leading to the generation of lipid peroxidation, damage of nuclear and mitochondrial DNA, neuronal apoptosis, and neuroinflammatory response. Transcription factor NF-E2 related factor 2 (Nrf2), a basic leucine zipper protein, plays an important role in the regulation of antioxidant proteins, such as oxygenase-1(HO-1), NAD(P)H Quinone Dehydrogenase 1 (NQO1), and glutathione peroxidase (GPx), to protect against oxidative stress, neuroinflammation, and neuronal apoptosis. Recently, emerging evidence indicated the knockout (KO) of Nrf2 aggravates the pathology of TBI, while the treatment of Nrf2 activators inhibits neuronal apoptosis and neuroinflammatory responses via reducing oxidative damage. Phytochemicals from fruits, vegetables, grains, and other medical herbs have been demonstrated to activate the Nrf2 signaling pathway and exert neuroprotective effects in TBI. In this review, we emphasized the contributive role of oxidative stress in the pathology of TBI and the protective mechanism of the Nrf2-mediated oxidative stress response for the treatment of TBI. In addition, we summarized the research advances of phytochemicals, including polyphenols, terpenoids, natural pigments, and otherwise, in the activation of Nrf2 signaling and their potential therapies for TBI. Although there is still limited clinical application evidence for these natural Nrf2 activators, we believe that the combinational use of phytochemicals such as Nrf2 activators with gene and stem cell therapy will be a promising therapeutic strategy for TBI in the future. Traumatic brain injury (TBI) refers to the damage to the brain structure and function caused by mechanical and external forces, including two stages of primary injury and secondary injury . It is aTBI, also known as acquired intracranial injury, occurs in the brain. It is caused by an external force, including a blow, bump, or jolt to the head, and the sudden and serious hit of the head by an object or the deep pierce of an object into the skull through the brain tissue . AccordiTBI is classified in various ways, including type, severity, location, mechanism of injury, and the physiological response to injury . In geneAt present, the therapeutic strategies for TBI include hyperbaric oxygen therapy (HBOT), hyperventilation and hypertonic therapy, noninvasive brain stimulation, drug therapy, and biological therapy . Most imκB (NF-κB) and activator protein-1 (AP-1) sense oxidative stress via oxidation and reduction cycling [κB, resulting in proinflammatory responses in various diseases such as neurodegenerative diseases, spinal cord injury, and TBI [Oxidative stress occurs owing to an imbalance of free radicals and antioxidants in the body, which lead to cell and tissue damage . Therefo cycling . In addi and TBI . Therefo3+, which also can be sequestered intracellularly by ferritin, an iron storage protein. Thus, iron in the brain is maintained at a relatively low level under normal conditions. However, the value of pH is decreased in the brain of TBI, which is accompanied by the release of iron from both transferrin and ferritin. Then, the excessive levels of active iron catalyze the oxygen radical reaction and induce oxidative damage and ferroptosis [2O2) or lipid hydroperoxides, and the level of iron can be further increased as the pH decreases to 6.5 or even below [2+ in TBI secondary injury. Then, NO reacts with free radical superoxide to generate “reactive nitrogen species” peroxynitrite (PN) in the forms of 3-nitrotyrosine (3-NE) and 4-HNE, which are found in the ipsilateral cortex and hippocampus of TBI animal models [Although the initial brain insult of TBI is an acute and irreversible primary damage to the parenchyma, the ensuing secondary brain injury progressing slowly over months to years seriously affects the treatment and prognosis of TBI . Thereforoptosis . Additioroptosis . Iron isen below . Therefoen below . Moreovel models . For exal models . Therefoα), which can inhibit CNC-bZIP factors and the transcription of genes targeting Nrf2. Neh6 has two motifs including DSGIS and DSAPGS of β-transducing repeat-containing protein (β-TrCP) functioning as a substrate receptor for the Cul3-Rbx1/Roc1 ubiquitin ligase complex [β-TrCP to ubiquitinate Nrf2 [In 1995, Itoh, K. et al. first discovered and reported that Nrf2 was the homolog of the hematopoietic transcription factor p45 NF-E2 . To date complex . DSGIS iate Nrf2 . The Nehate Nrf2 . The Nehate Nrf2 . In addiUnder normal conditions, Nrf2 is kept in the cytoplasm by a cluster of proteins including Keap1 and Cul3, which then undergoes degradation via the ubiquitin-proteasome system (UPS) . In brieBecause the primary injuries in TBI commonly result in acute physical damage and irreversible neuronal death, the therapies mainly aim at stabilizing the injury site and preventing it from secondary damage. As described above, the secondary damage of TBI is induced by various risks such as oxidative stress and develops progressively. To date, multiple therapeutic manners are developed, including the inhibition of excitotoxicity by glutamate receptor antagonists such as dexanbionol, the improvement of mitochondrial dysfunction using neuroprotective agents such as cyclosporine A, and the inhibition of axonal degeneration by calpain inhibitors such as MDL 28170 . EmerginQuercetin belonging to flavonoids is commonly found in dietary plants including vegetables and fruits such as onions, tomatoes, soy, and beans . EmerginCurcuma longa rhizomes, has been reported to possess multiple biological activities, including antioxidative, anti-inflammatory, and anticancer effects [α), interleukin 6 (IL-6), and interleukin-1beta (IL-1β) [κB signaling pathway [β-APP, and NF-H. The mechanistic study revealed that curcumin activated the PERK/Nrf2 signaling pathway [Curcumin, a polyphenol isolated from  effects . Most im effects . In addi effects , 80–82.  effects . Meanwhi effects , 83–85.  pathway  and aqua pathway . Diffuse pathway . Most im pathway . In addi pathway . Taken tα and IL-6 as well as the mRNA level of Cyclooxygenase-2(COX-2) were also reduced by formononetin. The mechanistic study revealed that formononetin increased the protein expression of Nrf2 [Formononetin, an O-methylated isoflavone phytoestrogen, is commonly found in plants such as red clover . Accumul of Nrf2 . Further of Nrf2 . All eviScutellaria baicalensis [Baicalin, known as 7-D-Glucuronic acid-5,6-dihydroxyflavone, is a major flavone found in the radix of calensis . Emergincalensis . In addicalensis . In weigcalensis . As is kβ, iNOS, and IL-6 were decreased by catechin. At the same time, catechin significantly alleviated the brain damage as revealed by the decrease in the brain water content and brain infarction volume, as well as improved motor and cognitive deficits [Catechin is a flavan-3-ol and belongs to a type of natural polyphenols . It is adeficits . In addideficits . In CCI-deficits . TherefoCotinus coggygria by Jacob Schmid in 1886 [κB pathway, and the level of TNF-α, IL-1β, and IL-6 was significantly decreased. Meanwhile, the BBB disruption of TBI mice was attenuated by fisetin [κB-mediated inflammatory signaling pathway.Fisetin, also known as 3,3′,4′,7-tetrahydroxyflavone, is a flavonol compound and was first extracted from  in 1886 , and its in 1886 . Emergin in 1886 . Fisetin in 1886 . In addi in 1886 . In addi in 1886 . Moreove fisetin . TherefoLuteolin, belonging to flavonoids, is abundant in fruits and vegetables such as carrots, green tea, and celery . EmerginSinofranchetia chinensis, Glycyrrhiza uralensis, and Dalbergia odorifera. [2O2 concentration and MDA levels. However, the KO of Nrf2 significantly attenuated the neuroprotective effect of isoliquiritigenin in mice after TBI. The mechanistic study demonstrated that isoliquiritigenin increased the nuclear translocation of Nrf2 and the protein and mRNA expression of NQO1 and HO-1. In the in vitro study, isoliquiritigenin also activated the Nrf2-ARE signaling pathway and increased the cell viability in oxygen and glucose deprivation (OGD)-induced SH-SY5Y cells. In addition, isoliquiritigenin inhibited shear stress-induced cell apoptosis in SH-SY5Y cells, as well as suppressed the inflammatory response and inhibited neuronal apoptosis in CCI-induced TBI mice or rats via the PI3K/AKT/GSK-3β/NF-κB signaling pathway [β pathway [Isoliquiritigenin, a chalcone compound, is often found in plants including orifera. . Isoliquorifera. . In addiorifera. . For exa pathway , 123. Mo2O2 consumption. In addition, apoptosis-related proteins including cleaved caspase-3 and Poly (ADP-ribose) polymerase (PARP), as well as Bax/Bcl-2, were significantly inhibited by tannic acid. Meanwhile, the inflammatory response indicated by the increased levels of TNF-α and IL-1β and GFAP immunofluorescence intensity was also suppressed. The mechanistic study demonstrated that tannic acid increased the protein expression of Nrf2, PGC-1α, Tfam, and HO-1. Therefore, tannic acid exerts a neuroprotective effect in TBI via activating the PGC-1α/Nrf2/HO-1 signaling pathway.Tannic acid, a natural polyphenol, is commonly found in green and black teas as well as nuts, fruits, and vegetables . Emerginα, IL-1β, and IL-6 levels were reduced by ellagic acid [κB and COX-2 while increasing the protein expression of Nrf2 [κB pathway in TBI.Ellagic acid, an innate polyphenol, is commonly found in various berries such as blueberries, strawberries, blackberries, together with walnuts and nuts . Severalgic acid , 135. In of Nrf2 . Therefoβ (GSK-3β) and IL-6 by breviscapine is associated with its neuroprotective effect in TBI [Breviscapine is an aglycone flavonoid and is isolated from the Erigeron plant . Modern t in TBI , 140. ThCentella asiatica [β, IL-18, IL-6, and TNF-α, and counteracted oxidative stress by decreasing ROS, H2O2, and MDA levels while increasing SOD activity and glutathione production. The underlying mechanisms include the activation of Nrf2/HO-1 and the inhibition of the NLRP3 inflammasome pathway. In addition, asiatic acid could alleviate tert-butyl hydroperoxide (tBHP)-induced oxidative stress in HepG2 cells. The researchers found that asiatic acid significantly inhibited tBHP-induced cytotoxicity, apoptosis, and the generation of ROS, which attributed to the activation of Keap1/Nrf2/ARE signaling pathway and the upregulation of transcription factors including HO-1, NQO-1, and GCLC [Asiatic acid belonging to pentacyclic triterpene is isolated from natural plants such as asiatica . Studiesasiatica . For exaand GCLC . In a CCand GCLC . Taken tEucommia ulmoides [2O2-induced primary cortical neurons and weight drop-induced TBI mouse model, aucubin was found to significantly decrease the excessive generation of ROS and inhibit neuronal apoptosis. In addition, aucubin could reduce brain edema, improve cognitive function, decrease neural apoptosis and loss of neurons, attenuate oxidative stress, and suppress the inflammatory response in the cortex of TBI mice. The mechanistic study demonstrated that aucubin activated the Nrf2-ARE signaling pathway and upregulated the expression of HO-1 and NQO1, while the neuroprotective effect of aucubin in Nrf2-KO mice after TBI was reversed [Aucubin, an iridoid glycoside isolated from natural plants such as ulmoides , is repoulmoides –147. Reculmoides . In addiulmoides . In H2O2reversed . TherefoUrsolic acid, a pentacyclic triterpenoid compound, is widely found in various fruits and vegetables such as apples, bilberries, lavender, and hawthorn. . UrsolicRosmarinus officinalis and Salvia officinalis [Rosmarinus officinalis [Carnosic acid, a natural benzenediol abietane diterpene, is found in icinalis . Carnosiicinalis . Emerginicinalis . In CCI-icinalis , 158. Inicinalis . TherefoκB signaling pathway in bone marrow-derived immune cells and astrocytes [−/− mice after TBI. Therefore, fucoxanthin activates the Nrf-2 signaling pathway and induces autophagy to exert a neuroprotective effect in TBI.Fucoxanthin, a carotenoid isolated from natural plants such as seaweeds and microalgae, is considered a potent antioxidant . Severaltrocytes . In moustrocytes . Recentltrocytes . In thisβ-carotene, abundant in fungi, plants, and fruits, is a member of the carotenes and belongs to terpenoids [β-carotene acting as an antioxidant has potential therapeutic effects in various diseases, such as cardiovascular disease, cancer, and neurodegenerative diseases [β-carotene was also reported in CCI-induced TBI mice; the administration of β-carotene significantly improved neurological function and brain edema as evidenced by the decreased neurological deficit score and brain water content and increased time of wire hanging of mice after TBI. In addition, β-carotene could maintain the BBB permeability as indicated by the EB extravasation and ameliorate oxidative stress as showed by the increased SOD level and decreased MDA levels. The mechanistic study demonstrated that β-carotene activated the Keap1-Nrf2 signaling pathway and promoted the expression of HO-1 and NQO1 [β-carotene provides a neuroprotective effect in TBI via inhibiting oxidative stress through the Nrf2 pathway.rpenoids . Accumuldiseases , 168. Meand NQO1 . Therefo2O2-induced primary cortical neurons by reducing oxidative damage and inhibiting apoptosis via the SIRT1/Nrf2/Prx2/ASK1/p38 signaling pathway [Astaxanthin is a carotenoid and is commonly found in certain plants and animals, such as salmon, rainbow trout, shrimp, and lobster . Emergin pathway . TherefoCalendula officinalis, spinach, and Brassica oleracea [α, IL-1β, IL-6, and Monocyte chemoattractant protein-1 (MCP-1). Meanwhile, lutein decreased ROS production and increased SOD and GSH activity, suggesting that lutein attenuated TBI-induced oxidative damage. Moreover, the mechanistic study found that lutein inhibited the protein expression of intercellular adhesion molecule-1 (ICAM-1), COX-2, and NF-κB, while increasing the protein expression of ET-1 and Nrf2. Therefore, the neuroprotective effect of lutein in TBI may be regulated via the NF-κB/ICAM-1/Nrf2 signaling pathway [β, IL-6, and NF-κB was inhibited by lutein/zeaxanthin [κB signaling pathways.Lutein, a natural carotenoid, is commonly found in a variety of flowers, vegetables, and fruits, such as oleracea . Accumuloleracea . In seve pathway . It is kaxanthin . TherefoAesculus chinensis Bunge and chestnut [Sodium aescinate (SA) is a mixture of triterpene saponins isolated from the seeds of chestnut . Amountichestnut –181. In chestnut , 183. A chestnut . The intMelatonin, commonly found in plants, animals, fungi, and bacteria, plays an important role in the regulation of the biological clock . MelatonSinomenium acutum (Thunb.) Rehd. et Wils. and Sinomenium acutum var. cinereum Rehd. et Wils [Sinomenine is an alkaloid compound that is isolated from the roots of climbing plants including  et Wils . Sinomen et Wils . Therefoα3), GPx, and HO-1, as well as enhanced the enzymatic activity of NQO1 in the brain and brain microvessels of TBI mice, suggesting that sulforaphane activated the Nrf2-ARE signaling pathway to protect BBB integrity. Furthermore, sulforaphane could reduce brain edema as evidenced by the decrease in brain water content, which was closely associated with the attenuation of AQP4 loss in the injury core and the further increase of AQP4 level in the penumbra region [Sulforaphane, also known as isothiocyanate, is commonly found in certain kinds of vegetables, including cabbage, broccoli, and cauliflower . Emergina region . Moreovea region . In addia region . Taken tIt is known to us that TBI causes irreversible primary mechanical damage, followed by secondary injury. Studies have shown that multiple mechanisms contribute to the development of TBI during secondary injury, mainly including inflammatory response, oxidative stress, mitochondrial dysfunction, BBB disruption, and otherwise. Among them, oxidative stress leads to mitochondrial dysfunction, BBB disruption, and neuroinflammation. Therefore, oxidative stress plays a central role in the pathogenesis of TBI. Nrf2 is a conserved bZIP transcription factor, and the activation of the Nrf2 signaling pathway protects against oxidative damage. Under stress conditions or the treatment of Nrf2 activators, Nrf2 is translocated from the cytoplasm into the nucleus where it protects against oxidative damage via the ARE-mediated transcriptional activation of genes, including HO-1, NQO1, and GCLC, thereby inhibiting mitochondrial dysfunction, apoptosis, inflammation, and oxidative damage. Therefore, targeting the activation of the Nrf2 signaling pathway is a promising therapeutical strategy for TBI. To date, increased Nrf2 activators were reported to exert neuroprotective effects in various neurodegenerative diseases, cerebral ischemia, cerebral hemorrhage, and TBI , 200. PhκB, NADPH oxidase-2 (NOX-2) with gene, and stem cell therapy will be a promising strategy for the treatment of TBI.Although a large number of studies have demonstrated the neuroprotective effect of most of the phytochemicals in vivo and in vitro models of TBI, there is a lack of effective clinical application evidence. In addition, little is known about the safety and pharmacokinetics of these phytochemicals. Therefore, increasing studies are needed to be performed to accelerate the process of phytochemicals entering the clinic. In the later period of TBI recovery, the selective permeability of BBB also gradually recovered. At this time, BBB is a big obstacle, which greatly limits the neuroprotective effect of drugs, and the use of drugs based on nanomaterials effectively improves the BBB permeability of drugs, bringing new hope for these phytochemicals. In addition, the combinational use of phytochemicals targeting multi-targets such as Nrf2, NF-