TFE3 and TFEB genes. Despite the initial description of typical morphology, their histological spectrum is wide, mimicking common subtypes of renal cell tumors. Thus, the diagnosis is challenging requiring the demonstration of the gene rearrangement, usually by FISH. However, this technique is limited in most laboratories and immunohistochemical TFE3/TFEB analysis is inconsistent. We sought to identify a useful immunohistochemical panel using the most common available markers to recognize those tumors. We performed an immunohistochemical panel comparing 27 TFE3-rearranged and 10 TFEB-rearranged renal cell carcinomas to the most common renal cell tumors . When dealing with neoplasms characterized by cells with clear cytoplasm, CA9 is a helpful marker to exclude clear cell renal cell carcinoma. GATA3, AMACR, and CK7 are useful to rule out clear cell papillary renal cell tumor. CK7 is negative in TFE3/TFEB-rearranged renal cell carcinoma and positive in papillary renal cell carcinoma, being therefore useful in this setting. Parvalbumin and CK7/S100A1 respectively are of paramount importance when TFE3/TFEB-rearranged renal cell carcinoma resembles oncocytoma and chromophobe renal cell carcinoma. Moreover, in TFEB-rearranged renal cell carcinoma, cathepsin K and melanogenesis markers are constantly positive, whereas TFE3-rearranged renal cell carcinoma stains for cathepsin K in roughly half of the cases, HMB45 in 8% and Melan-A in 22%. In conclusion, since TFE3/TFEB-rearranged renal cell carcinoma may mimic several histotypes, an immunohistochemical panel to differentiate them from common renal cell tumors should include cathepsin K, CA9, CK7, and parvalbumin.TFE3/TFEB-rearranged renal cell carcinomas are characterized by translocations involving The online version contains supplementary material available at 10.1007/s00428-022-03380-x. Molecular classification of renal cell carcinoma has been evolving in the last decades, with emerging of new entities and new genetic characteristics. The forthcoming Word Health Organization (WHO) includes TFE3-rearranged renal cell carcinoma and TFEB-rearranged renal cell carcinoma as separate entities differentiating from the previous one in which the term “MiT family translocation renal cell carcinoma” encompassed both tumor types .TFE3 gene translocation which fuses with one of several other genes, such as ASPL (ASPSCR1), PRCC, SFPQ, CLTC, PARP14, RBM10, NONO, and MED15 [TFEB gene, located on chromosome 6, most commonly translocated to chromosome 11 where fused with the Alpha (MALAT1) gene [COL21A1, CADM2, KHDRBS2, ACTB, EWSR1, CTLC, and NEAT1) [TFEB gene amplification despite translocation [Both tumors are frequently discovered during childhood ; howevernd MED15 . There ind MED15  and behand MED15 . Histolod NEAT1) –20, as wlocation . There ilocation . Morphollocation , 30.Although these characteristic patterns can be striking in the classic cases, much more often, those tumors present heterogeneous architectural and cytological features, resembling the common subtypes of renal cell carcinoma. TFE3-rearranged renal cell carcinoma may show a solid, trabecular, or microcystic pattern, mimicking clear cell renal cell carcinoma, papillary renal cell carcinoma, and clear cell papillary renal cell tumor , 31–33. TFE3 or TFEB gene translocation is required to reach the proper diagnosis. Although it could be argued to use TFE3 and TFEB immunostaining to demonstrate the translocation, the results are inconsistent due to the not infrequent false-positive and false-negative results [Due to the wide spectrum of morphology observed, the diagnosis is not straightforward on hematoxylin and eosin slides, and the identification of  results . For thi results –39. Rece results  even if Twenty-seven TFE3-rearranged renal cell carcinomas and ten TFEB-rearranged renal cell carcinomas were demonstrated by FISH analysis Fig. . One hunSections from tissue blocks of all the included types of renal cell carcinoma were immunohistochemically stained with the following antibodies: PAX8 , CD10 , carbonic anhydrase 9 CA9 , cytokeratin 7 , alpha-methylacyl-CoA racemase AMACR , S100A1 , parvalbumin , CD13 , GATA3 , and cathepsin K . To further analyze the immunohistochemical profile of MiT family translocation renal cell carcinomas, TFE3-rearranged-renal cell carcinomas and TFEB-rearranged renal cell carcinomas were also stained with HMB45 , Melan-A , CD68 , cytokeratin 8–18 , cytokeratin 20 , fumarate hydratase FH , and succinate dehydrogenase B SDHB .All samples were processed using a sensitive Bond Polymer Refine detection system in an automated Bond immunohistochemistry instrument . The immunohistochemical expression of each marker for every tumor subtype was recorded. Concerning TFE3 and TFEB-rearranged renal cell carcinomas, three different cutoffs of expression were evaluated for each marker, respectively of 5%, 10%, and 20%.P-values were based on a two-tailed hypothesis. The results were considered statistically significant if the P-value was less than 0.05.Fisher’s exact test was used to compare categorical data for immunohistochemical characteristics for each of above mentioned three different cutoff levels. All The pathological features of twenty-seven TFE3-rearranged renal cell carcinomas  and ten Cathepsin K was observed in the most of MiT family translocation renal cell carcinomas (66% and 63% using the threshold of 5% and 10% or 20% positive cells respectively). About TFE3-rearranged renal cell carcinomas, immunolabeling was observed in roughly half of the cases (54% and 50% using the threshold of 5% and 10% or 20% positive cells respectively) whereas all the eleven TFEB-rearranged renal cell carcinomas evaluated stained positive for cathepsin K.Melanocytic markers, HMB45 and Melan-A, were respectively positive in 15 of 36 (42%) and 14 of 34 (41%) of MiT family translocation renal cell carcinomas using the 5% cutoff. The percentage of cases considered positive for HMB45 drastically decreased when the higher cutoff was used (17% and 11% using the threshold of 10% and 20% respectively) while the percentage of positive cases was similar for Melan-A regardless of the cutoff (38% and 29% using the threshold of 10% and 20% respectively). TFEB-rearranged renal cell carcinomas were constantly immunolabeled for Melan-A , making such a reliable marker for the identification of these tumors, and frequently for HMB45 . The expression of Melan-A and HMB45 was significantly lower in TFE3-rearranged renal cell carcinoma compared with TFEB-rearranged renal cell carcinoma, even when a 5% cutoff was used (17% and 27% respectively).Regarding the proximal tubular markers such as CD10 and CD13, immunolabeling for the former was observed in 26 of 33 (79%), 25 of 33 (76%), and 22 of 33 (67%) of the MiT family translocation renal cell carcinomas, with the respective increasing cutoffs. As for TFE3-rearranged renal cell carcinomas, among 23 tumors, CD10 stained positive in 22 of the cases (96%), regardless of the cutoff considered, whereas it was positive just in 4 (40%), 3 (30%), and none (0%) of all the ten cases of TFEB-rearranged renal cell carcinomas, respectively, using a 5%, 10%, and 20% positivity threshold. CD13 expression, instead, was found in 13 of 32 (41%) and 10 of 32 (31%) MiT family translocation renal cell carcinomas tested, respectively, with a 5% and both a 10% and 20% cutoff. Among TFE3-rearranged renal cell carcinomas, 11 of 23 tumors (48%), considering a 5% cutoff, and 9 of 23 tumors (39%), both with a 10% and a 20% threshold, were positive for CD13; a weak expression of the same marker was instead noticed for TFEB-rearranged renal cell carcinomas, with only 2 (22%) and 1 (11%) of 9 tumors staining positive, employing a 5% and both a 10% and 20% respectively considered.As for the distal tubular markers such as GATA3 and parvalbumin, none of the MiT family translocation renal cell carcinomas retrieved was positive for the former. About parvalbumin expression, among 33 MiT family translocation renal cell carcinomas 12 (36%), using a 5% cutoff, and 7 (21%), both considering a 10% and 20% cutoff, labeled positive for it. While 5 of 22 TFE3-rearranged renal cell carcinomas showed positivity for such marker in more than 20% of the cells (22%), the remaining 17 cases were completely negative or patchy positive for it. Moreover, 7 of 10 (70%) and 2 of 10 (20%) TFEB-rearranged renal cell carcinomas revealed positive parvalbumin expression respectively considering a 5% threshold and both a 10% and a 20% threshold.Considering other markers commonly evaluated when dealing with renal cell carcinomas, CA9 was expressed by 6 of 35 (17%), 2 of 35 (6%), and none of 35 (0%) MiT family translocation renal cell carcinomas, respectively, using a 5%, 10%, and 20% cutoff. Namely, whereas CA9 immunolabeling was found in 6 of 25 (24%) TFE3-rearranged renal cell carcinomas with a 5% threshold, in 2 of them (8%) and none of them (0%), respectively, referring to a 10% and 20% cutoff, all the TFEB-rearranged renal cell carcinomas were negative for such marker. Among the 35 MiT family translocation renal cell carcinomas tested, AMACR, instead, stained positive in 26 (74%), 19 (54%), and 18 cases (51%), regarding a 5%, 10%, and 20% cutoff respectively. Positive expression was found in 23 of 25 (92%), 19 of 25 (76%), and 18 of 25 (72%) TFE3-rearranged renal cell carcinomas, using the same thresholds. Nevertheless, only 3 of 10 (30%) TFEB-rearranged renal cell carcinomas were considered positive for AMACR when a 5% cutoff was used, while none of them showed positive staining in more than 10% of the cells.Furthermore, S100A1 was typically positive and CK7 was usually negative regardless of the cutoff used either considering the overall MiT family translocation renal cell carcinomas or TFE3 and TFEB-rearranged renal cell carcinoma separately. Regarding CK8-18 immunolabeling instead, it was noticed in 19 of 34 (56%), 15 of 34 (44%), and 13 of 34 (38%) MiT family translocation renal cell carcinomas, using a 5%, 10%, and 20% cutoff respectively. While the expression is lower for TFE3-rearranged renal cell carcinomas , most of TFEB-rearranged renal cell carcinomas labeled positive for CK8-18 .None of the MiT family translocation renal cell carcinomas considered expressed CD68 (PG-M1) neither CK20. SDHB and FH were retained in all the cases tested.The immunohistochemical results of clear cell renal cell carcinoma, papillary renal cell carcinoma, clear cell papillary renal cell tumor, chromophobe renal cell carcinoma, and oncocytoma are detailed in supplementary Table p = 0.0001) and CD13 (p = 0.0001), with MiT family translocation renal cell carcinomas and positive expression of AMACR (p = 0.0001), cathepsin K (p = 0.0001), and parvalbumin (p = 0.0001 with a 5% cutoff and p = 0.0007 with both a 10% and a 20% cutoff).Regardless of the cutoff used, in the differential diagnosis with clear cell renal cell carcinoma, a statistically significant correlation was found with MiT family translocation renal cell carcinomas and negative expression of CA9 (p = 0.0001) and CD13 (p = 0.0003 with a 5% cutoff and p = 0.0001 with both a 10% and a 20% cutoff) statistically correlated with the diagnosis of such tumor subtype, as well as cathepsin K (p = 0.0001), AMACR (p = 0.0001), and parvalbumin (p = 0.0021) positivity.As far as TFE3-rearranged renal cell carcinomas were concerned, despite the cutoff taken into account, a negative expression of CA9 (p = 0.0002 with a 5% cutoff and p = 0.0001 with both a 10% and a 20% cutoff), CA9 (p = 0.0002 with both a 5% and a 10% cutoff and p = 0.0001 with both a 20% cutoff), and CD13 (p = 0.0001) and with cathepsin K (p = 0.0001) and parvalbumin positivity (p = 0.0001 with a 5% threshold and p = 0.0048 with both a 10% and a 20% threshold), no matter what the cutoff used was.About TFEB-rearranged renal cell carcinomas, finally, a statistical correlation of relevant levels was reported with negative expression of CD10 , CK7 (p = 0.0001), and GATA3 (p = 0.0001), along with positive expression of CD10 (p = 0.0001 with both a 5% and 10% cutoff and p = 0.0011 with a 20% cutoff), AMACR (p = 0.0001 with both a 5% and 10% cutoff and p = 0.0002 with a 20% cutoff), and cathepsin K (p = 0.0001). Similar results were observed when TFE3-rearranged renal cell carcinoma was considered. However, in TFEB-rearranged renal cell carcinoma, CD10 and AMACR staining are not statistically correlated (p = 0.5 and p = 1 respectively).When referring to clear cell papillary renal cell tumor, there was a strong statistically significant correlation, despite the threshold of positivity considered, with MiT family translocation renal cell carcinomas and negative expression of CA9 , AMACR (p = 0.066 with a 5% cutoff and p = 0.0001 with both a 10% and 20% cutoff), and CD13 (p = 0.0001 with both a 5% and 10% cutoff and p = 0.0002 with a 20% cutoff) and with MiT family translocation renal cell carcinomas and positive expression of parvalbumin (p = 0.0001 with a 5% threshold and p = 0.0095 with a 10% and 20% threshold) and cathepsin K (p = 0.0001).As for the differential diagnosis with papillary renal cell carcinoma, despite what cutoff was used, a significant statistical correlation was noticed with MiT family translocation renal cell carcinomas and negative expression of CK7 (p = 0.0001) and CD13  was of statistically significant value, as well as positive immunolabeling for CD10 (p = 0.0002), parvalbumin (p = 0.0016), and cathepsin K (p = 0.0001). Furthermore, negative expression of CA9 (p = 0.017) and AMACR (p = 0.0045), both considering a 20% positivity cutoff, and positive staining for S100A1 (p = 0.0282), with the lowest cutoff of 5%, was significantly strong alike.Speaking of TFE3-rearranged renal cell carcinomas, regardless the cutoff, a negative expression of only CK7 (p = 0.0001), AMACR (p = 0.0001), and CD13 (p = 0.0003 with a 5% threshold and p = 0.0001 with both a 10% and a 20% threshold) along with their positive expression of cathepsin K (p = 0.0001), no matter what cutoff value was considered.A statistically relevant correlation was also noted between TFEB-rearranged renal cell carcinomas and negativity for CK7  and parvalbumin (p = 0.0001), along with immunolabeling for CD10 (p = 0.0001), AMACR (p = 0.0001), S100A1 (p = 0.0001), CD13 (p = 0.0001 with a 5% threshold and p = 0.0008 with both a 10% and 20% threshold), and cathepsin K (p = 0.0001) were statistically consistent with the diagnosis of MiT family translocation renal cell carcinomas, all these results observed concerning each of the above-mentioned positivity cutoffs.In cases with histological features recalling that of chromophobe carcinomas, negative expression of CK7 (p = 0.0239).TFE3-rearranged renal cell carcinomas revealed the same results. In addition, interestingly, positive staining for CA9 was also found to be statistically relevant, but only when a 5% positivity cutoff was considered (p = 0.0159 with both a 5% and 10% cutoff and p = 0.0014 with a 20% one) and parvalbumin (p = 0.0183 with a 5% threshold and p = 0.0001 with both a 10% and 20% one) as well as positive expression of S100A1 (p = 0.0001) and cathepsin K (p = 0.0001) statistically correlated with the diagnosis of such tumor subtype, despite the cutoff chosen. On the other hand, no relevant statistical correlation was found with immunohistochemical expression of either CD10, AMACR, or CD13, in contrast with the data observed with TFE3-rearranged renal cell carcinomas.Finally, as far as TFEB-rearranged renal cell carcinomas were concerned, negativity for CK7  and positive staining for CD10 , AMACR (p = 0.0001), CD13 (p = 0.0001), and cathepsin K (p = 0.0001) were strongly statistically consistent with the diagnosis of MiT family translocation renal cell carcinoma despite what positivity threshold was used. Similar results were also noted for negative expression of S100A1 (p = 0.0305) and CK7 (p = 0.0364), respectively referring to a 10% and 20% cutoff.Negative expression of parvalbumin (p = 0.0001) and a statistical correlation was also collected with parvalbumin negativity (p = 0.0001), even though only with a 5% and 10% positivity threshold.The analysis from TFE3-rearranged renal cell carcinomas data revealed similar results, whereas, regarding TFEB-rearranged renal cell carcinomas, cathepsin K positive staining was the only immunohistochemical result of statistical significance for all the positivity thresholds considered . For instance, in the differential diagnosis between papillary renal cell carcinoma and TFE3-rearranged renal cell carcinoma, AMACR results are statistically significant when used 20% of positivity as a threshold whereas its expression is not significant with lower cutoffs, supporting the hypothesis that it is better to consider a lower percentage of positivity. On the other side, S100A1 seems an important marker in the differential diagnosis between TFEB-rearranged renal cell carcinoma and papillary renal cell carcinoma (p = 0.0152) using 5% and 10% of positivity whereas it is not significant with higher cutoffs, suggesting to use the high threshold of positivity. Overall, aside from melanogenesis markers and AMACR, in clinical practice, it is better to use a higher threshold to consider a positive immunohistochemical marker.Another important aspect of this study is the proper cutoff employed to define an immunohistochemical marker as positive since positive/negative results can be considered differently among pathologists. As different thresholds of positivity have been reported, it is important to establish if there are variances in results using several cutoffs of expression to consider an immunohistochemical marker as positive or negative. As expected, the threshold for positivity established is important for HMB45 and Melan-A. Even a patchy expression should be considered a positive result, especially for TFE3-rearranged renal cell carcinoma in which melanogenesis markers are less consistent. On the other hand, the different percentages  of neoplastic cells labeling for cathepsin K do not change the result since the expression is usually strong and diffuse , 55. IntFinally, the study highlighted the importance, also from an immunohistochemical point of view, to maintain TFE3 and TFEB-rearranged renal cell carcinoma as distinct entities, as recommended by the forthcoming WHO classification of renal tumors. When grouped together under the heading MiT family translocation renal cell carcinoma, the value of some immunohistochemical markers changed dramatically. For instance, staining for CD10 which is usually observed in TFE3-rearranged renal cell carcinoma and under-expressed in TFEB-rearranged renal cell carcinoma seems significant in the differential diagnosis of MiT family translocation renal cell carcinoma and clear cell renal cell carcinoma. However, as abovementioned, CD10 is not useful in this differential diagnosis when we are dealing with TFE3-rearranged renal cell carcinoma.TFE3 gene inversions, such as RBM10 and NONO gene fusions, may lead to false negative results by FISH and require further molecular analysis.In conclusion, the threshold to define a positive immunohistochemical result is not univocal but depends on the immunohistochemical marker considered. For instance, any expression of melanogenesis markers should be considered positive. A panel of immunohistochemistry markers useful to distinguish TFE3/TFEB-rearranged renal cell carcinoma from other common renal cell neoplasms should include cathepsin K, CA9, CK7, and parvalbumin Table . In thisSupplementary file1 (DOCX 17 KB)Below is the link to the electronic supplementary material.


However, there have been few reports on the role of ACO2 in tumorigenesis and cancer progression.Recent evidence increasingly suggests key roles for the tricarboxylic acid cycle and fatty acid metabolism in tumor progression and metastasis. Aconitase 2 (ACO2) is a component of the tricarboxylic acid cycle and represents a key cellular metabolic hub that promotes Through the comprehensive use of datasets from The Cancer Genome Atlas, Genotype-Tissue Expression Project, cBioPortal, Human Protein Atlas, UALCAN, Gene Expression Profiling Interactive Analysis, DNA Methylation Interactive Visualization Database, and TIMER2, we adopted bioinformatics methods to uncover the potential carcinogenic roles of ACO2, including by analysing ACO2 expression and correlations between prognosis, genetic mutations, immune cell infiltration, DNA methylation, tumor mutational burden, and microsatellite instability in different tumors. Additionally, the expression level and tumor-promoting effect of ACO2 were verified in hepatocellular carcinoma (HCC) cells. To explore the underlying mechanisms of ACO2 in human cancer, ACO2-related gene enrichment analysis and lipid metabolomics were performed using LM3 cells with or without ACO2 knockdown.The results indicated that ACO2 was highly expressed in most cancers, showing early diagnostic value in six tumor types, and was positively or negatively associated with prognosis in different tumors. Moreover, ACO2 expression was associated with immune cell infiltration, such as CD8+ T cells and tumor-associated neutrophils, in some cancers. For most cancer types, there was a significant association between immune checkpoint-associated genes and ACO2 expression. Compared with normal hepatocytes, ACO2 was upregulated in HCC cells, which promoted their proliferation and migration. Furthermore, to explore the underlying molecular mechanism, we performed KEGG pathway enrichment analysis of ACO2-associated genes and lipidomics using LM3 cells with or without ACO2 knockdown, which screened 19 significantly altered metabolites, including 17 with reduced levels and 2 with increased levels.Through pan-cancer analysis, we discovered for the first time and verified that ACO2 could be a useful diagnostic biomarker for cancer detection. Additionally, ACO2 could be used as an auxiliary prognostic marker or as a marker for immunotherapy in some tumor types. Cancer is the second leading cause of death worldwide, and it will probably become the leading cause by 2060 . AccumulHowever, such concept has been challenged and may need to be revisited because studies have demonstrated that mitochondrial activity is fully functional in cancers on the basis of 13C-labelled metabolomic analysis, which could induce mitochondrial biogenesis and oxidative phosphorylation and promote metastasis . Moreovede novo fatty acid biosynthesis  is closely related to mitochondrial citrate synthesis, which is the precursor metabolite required for lipogenesis and is exported to the cytosol for conversion into acetyl-CoA and subsequently into malonyl-CoA for ynthesis . Signifiynthesis . Howeverynthesis , breast ynthesis , and colynthesis .To fully assess the role of ACO2 across cancers, in this study, we tentatively performed a pan-cancer analysis that primarily involved gene expression, survival/prognosis, genetic alteration, methylation, and ACO2-associated immune infiltration data from large clinical datasets to explore the oncogenic role of ACO2. Furthermore, we confirmed the expression and tumor-promoting effect of ACO2 in hepatocellular carcinoma (HCC) cell lines, after which ACO2-related gene enrichment analysis and lipid metabolomics were performed to observe the potential underlying molecular mechanisms. Our data suggest that ACO2 could be employed as a diagnostic, prognostic, and/or immunological predictor in some cancers, which may ultimately lead to ACO2-targeting anti-tumor therapies.https://www.Rproject.org) was used for data analysis, and the “ggplot2” R package was applied to draw the box diagrams.The mRNA expression profiles and correlative clinical data from 33 types of cancer and healthy control samples from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were extracted for analysis through their portal websites , 15. Thehttps://www.proteinatlas.org/)  online website (as.org/)  was usedAs provided with each sample by TCGA, specific tumor stages were chosen, and associations with ACO2 expression were analysed using “ggplot2” R packages, which are drawing packages that can separate drawing and data, data-related drawing, and data irrelevant drawing.To evaluate the diagnostic accuracy of ACO2, using TCGA combined with GTEx cohort, receiver operating characteristic (ROC) curve analysis was conducted on the basis of sensitivity and specificity using the “pROC” package, with area under the curve (AUC) ranging from 1.0 (perfect diagnostic) to 0.5  .http://gepia.cancer-pku.cn)  database (-pku.cn)  is an onhttps://www.cbioportal.org/)   provideshttp://www.unimd.org/dnmivd/)   is a comhttps://www.aclbi.com/static/index.html#/) (TMB is a quantifiable immune-response biomarker that reflects the number of mutations in tumor cells . MSI res.html#/) , an onlihttp://timer.cistrome.org/) (Tumor Immune Estimation Resource 2 (TIMER2.0) (me.org/)  provideshttp://SangerBox.com/Tool) website  (https://hiplot.com.cn) (The STRING website (db.org/)  was useddb.org/) , an inte.com.cn) , a comprhttp://www.sangerbox.com/tool) to obtain the latest KEGG pathway gene annotation. The minimum gene was set as 40, and the maximum gene was set as 5000. A P value of < 0.05 was considered statistically significant.The raw count and metadata of GSE1112271 , which cLO2, HepG2, LM3, and Hep1-6 cells were obtained from the cell bank of The Shanghai Institute of Biological Sciences  and maintained in DMEM supplemented with 10% fetal bovine serum at 37°C and 5% CO2. All cell lines were routinely monitored for mycoplasma .Cells were lysed using TRIzol reagent , and total RNA was extracted with chloroform and isopropyl alcohol. cDNA was then synthesized using a Reverse Transcription Kit  in accordance with the manufacturer’s protocols. A SYBR Green Master Mix Kit was used for the relative quantification of RNA levels in accordance with the manufacturer’s instructions. GAPDH was chosen as the internal control. ACO2 expression levels were normalized to the internal controls and determined by the 2−ΔΔCt method. The sequences of the primers used for qPCR were as follows: β-actin (control): forwards, 5′-CCTAGAAGCATTTGCGGTGG-3′ and reverse, 5′-GAGCTACGAGCTGCCTGACG-3′; and ACO2: forwards, 5′-CAATCGTCACCTCCTACAACAGG-3′ and reverse, 5′-GTCTCTGGGTTGAACTTGAGGG-3′.RIPA Lysis and Extraction Buffer  was used for total protein extraction. Equal amounts of total protein samples were separated by SDS−PAGE and then transferred to polyvinylidene fluoride membranes . Anti-ACO2 primary antibody  was incubated with the membranes at 4°C overnight. After washing, the membranes were incubated with the secondary antibody at room temperature for 1 h. Immunoreactive bands were visualized using an ECL kit. Relative expression from western blot results was calculated by the amount target protein/β-actin using Intensity of Opacity (IOD) data from ImagePro Plus.Cells were seeded in 6-well plates at a high density and allowed to form cell monolayers overnight. A 200-μL sterile plastic tip was used to create a wound line across the surface of the wells, and cells in suspension were removed by washing with PBS. Cells were cultured in reduced serum DMEM in a humidified 5% CO2 incubator at 37°C for 48 h, and then images were taken with a phase-contrast microscope. Each assay was replicated three times.3 cells per well, in accordance with the manufacturer’s protocol. We then performed the assay at 0 h, 24 h, 48 h, 72 h, and 96 h.We used the CCK-8 assay  to measure the proliferation of HCC cells. The assay was performed by initially plating 2×104 transfected cells were seeded into the upper chamber with serum-free medium, while the bottom chamber contained DMEM with 10% FBS. After the cells had migrated for 24 h, they were fixed and stained with crystal violet. Migrated HCC cells were counted under an inverted light microscope.For Transwell migration assays, 2.5×10For knockdown of ACO2, shRNA sequences targeting ACO2 (5’-GCCCAACGAGTACATCCATTA-3’) were inserted into the LV3-pGLV-H1-GFP/puro lentiviral vector . The empty vector was used as a negative control. The cells were infected with lentivirus supernatant and selected in medium containing puromycin (2 μg/ml).After washing twice with PBS, cells (approximately 107 cells/ml) were suspended in 1 ml normal saline. The cell suspension was centrifuged at 4°C and 1000 rpm/min for 3 min. After centrifugation, the cells were resuspended in 0.1 mL water in a 1.5 mL centrifuge tube, followed by the addition of 0.4 ml isopropyl alcohol. The internal standard  was added at a concentration of 5 μM. After 3 min of vortexing, the mixture was centrifuged at 15000 rpm/min for 15 min. Then, the supernatants were used for LC−MS analysis.For lipidomics measurement, supernatants were analysed by injection onto an ACQUITY UPLC BEH C8 column  at a flow rate of 0.3 ml/min using an LC-30AD Shimadzu pump system and an SIL-20AXR autosampler interfaced with an API 6500Q-TRAP mass spectrometer . A discontinuous gradient was generated to resolve the analytes by mixing solvent A (methol/acetonitrile/water=1/1/1 with 5 mM ammonium acetate) with solvent B . A gradient elution was optimized for the separation: 0-1 min, 20% Solution B; 1-2.5 min, 20%-40% Solution B; 2.5-4.0 min, 40%-60% Solution B; 4.0-14.0 min, 60%-90% Solution B; 14.0-15.0 min, 90% Solution B; 15.0-15.1 min, 90-20% Solution B; 15.1-17 min, 20% Solution B, with a total run time of 17 mins. The flow rate was 0.3 ml/min, and the column was maintained at 40°C. Analytes were monitored using electrospray ionization in separately positive-ion mode and negative-ion mode with multiple reaction monitoring (MRM) of precursor and characteristic product-ion transitions.Raw data were acquired using Xcalibur 4.1 (Thermo Fisher Scientific), and lipids were identified with 5 ppm mass tolerance by LipidSearch 4 (Thermo Fisher Scientific). Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to identify the differential metabolites between two groups using SIMCA-P version 14 .For bioinformatic validation, all the gene expression data were normalized by log2 transformation. The link between ACO2 expression and targets of interest, such as immune cell infiltration scores, TMB and MSI, was assessed using Spearman’s or Pearson’s test. The Kaplan-Meier curve, log-rank test and Cox proportional hazard regression model were used for all survival analyses in this study. Depending on whether the samples were paired, paired t tests or the t test were used to compare ACO2 expression levels across groups or between tumor and normal tissues. Significance was defined as a P value of less than 0.05. All statistical analyses were processed by R software (Version 4.0.3).For molecular biology verification, all statistical calculations were performed using the GraphPad Prism . Data are reported as the means ± SD. Two-tailed Student’s t test was used to determine statistically significant differences. Differences were defined as statistically significant if the P value <0.05.via cis-aconitate in the second step of the TCA cycle , relative diagnostic accuracy (AUC: 0.9–0.7), and low diagnostic accuracy (AUC: 0.7–0.5). Our analysis of the diagnostic value of ACO2 was performed for the 21 types of cancers. As shown in Next, among the 21 tumor types with differential ACO2 expression, we also investigated whether there were associations between stage and ACO2 levels. We discovered that four cancer types had significantly increased ACO2 expression in early tumor stages, including CHOL, KICH, LIHC, and LUSC, while ACO2 expression was decreased in early-stage BLCA and KIRC Figure 5With the aim of investigating associations between ACO2 expression and prognosis, we performed a survival association analysis using GEPIA2 for each cancer. According to ACO2 expression levels, we divided all cases into two groups, the high-expression and low-expression groups. As shown in Next, we analysed correlations between ACO2 expression and TMB/MSI across all tumors of TCGA. We observed a negative correlation between ACO2 expression and TMB for LGG (p<0.001) and thyroid carcinoma (THCA) (p<0.05) but found a positive correlation for UCEC (p<0.001), PAAD, colon adenocarcinoma (COAD), SKCM, and BRCA (p<0.01)  and decreased in BLCA and READ . Principal component analysis (PCA) showed significant between-group differences . Unfortunately, in UVM and MESO, this analysis was unsuccessful due to a lack of normal control samples. With the accumulation of datasets, this will be worthy of further exploration in the future. Xin You et al.  showed tCurrently, early cancer detection is of great clinical significance to push back the frontier of early cancer detection. The AUC is a standard used to measure the quality of the classification model. Gonzalez-Ericsson PI et al.  tested tTo investigate associations between ACO2 expression and prognosis, a survival analysis was performed using Kaplan-Meier survival curves for each type of cancer, including OS and DFS. By combining these results, we found that high ACO2 expression was linked to poor prognosis (OS) in LAML, LIHC, and SKCM, while for KIRC, KIRP, and LGG, high ACO2 expression was a favorable prognostic factor according to OS and DFS.Increasing evidence indicates a link between genomic mutations and methylation status and tumor progression , 40. ForRecent studies have shown that tumors with high MSI and TMB show a better response to immunotherapy . Our resGrowing evidence suggests that innate immune cells  as well as adaptive immune cells (T cells and B cells) contribute to tumor progression when present in the tumor microenvironment . By invein vitro experiments with HCC showed that ACO2 expression was significantly increased in HCC cells compared with hepatic cells, which was consistent with the result of Yilin Pang et al. .www.liwenbianji.cn) for editing the English text of a draft of this manuscript.We thank James P. Mahaffey, PhD, from Liwen Bianji (Edanz) (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


FAM83 family plays a key role in tumorigenesis and cancer progression. However, the role of the FAM83 family in the development of breast tumors is unclear to date. This report explores the expression, prognostic significance, and function of the FAM83 family members in breast cancer using public databases.The FAM83 family members in breast cancer. Furthermore, we validated the expression of FAM83 family members in twenty pairs of breast cancer and normal tissues by RT-PCR. Kaplan–Meier plotter database was used to explore the prognostic significance of FAM83 family members in breast cancer. GeneMANIA and DAVID databases were used for functional and pathway enrichment analysis of genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G. MEXPRESS and UALCAN databases were used to analyze the level of DNA promoter methylation of FAM83A, FAM83D, FAM83F, and FAM83G in breast cancer. TIMER database was utilized to explore the relationships between immune cell infiltration and FAM83A, FAM83D, FAM83F, and FAM83G expression.UALCAN database was used to explore the expression of FAM83 family members, FAM83A, FAM83D, FAM83F, and FAM83G were higher expressed in breast cancer than in normal tissues. We also validated the significant high expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA in breast cancer than in normal samples. Their increased expression has an adverse prognostic effect on breast cancer patients. These genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G might take part in cell proliferation, G2/M transition of the mitotic cell cycle, regulation of apoptosis process and other cancer-related biological processes. In addition, they were mainly enriched in the Hippo signaling pathway, Hedgehog signaling pathway, PI3K/AKT signaling pathway, and other cancer-related pathways. We also found that promoter DNA methylation might regulate the expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA in most CpG islands. At last, we found the expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA was significantly related to immune cell infiltration.Among FAM83A, FAM83D, FAM83F, and FAM83G were highly expressed in breast cancer tissues and had an adverse effect on the survival outcomes of breast cancer patients. Also, they were involved in breast cancer-related signal pathways. Therefore, they might serve as potential therapeutic targets for breast cancer clinical treatment.The online version contains supplementary material available at 10.1186/s12957-022-02636-9. Breast cancer has been the most common malignancy in women around the world and the second leading cause of cancer death in women. The number of new breast cancer cases accounted for about 30% of the total number of all new malignant tumors in women every year, representing a significant threat to women’s health and wellness worldwide . HoweverFAM83) family possess oncogenic properties, and their expression levels were elevated in many human cancers [FAM83 gene. For example, some lower organisms such as Drosophila, Saccharomyces cerevisiae and Caenorhabditis elegans. But all jawed vertebrates encoded the FAM83 gene [Recent studies have found that the proteins of the FAMily with sequence similarity 83  was used to analyze the mRNA levels of database .http://kmplot.com; mRNA gene chip KM Plotter for breast cancer) was used to analyze the effect of the FAM83 family gene expression on relapse-free survival (RFS) and overall survival (OS) at RNA-sequence level [The Kaplan–Meier plotter (ce level .www.proteinatlas.org) was made available freely to provide the expression profiles at protein levels, and immunohistochemistry images for a wide variety of cancer tissues. Genome‐wide transcriptomics data and clinical metadata of almost 8000 patients were also used to analyze the proteome of 17 major cancer types including breast cancer [HAP database (t cancer . In our http://www.genemania.org) was designed to construct an interaction network in terms of physical interactions, co-expression, predictions, and genetic interaction [https://david.ncifc rf.gov) was employed to conduct a functional and pathway enrichment analysis of the genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G [To evaluate the functions of FAM83A, FAM83D, FAM83F, and FAM83G, the GeneMANIA database was used. GeneMANIA (eraction . The DAVd FAM83G .https://mexpress.be/) was employed to analyze the level of DNA promoter methylation of FAM83A, FAM83D, FAM83F, and FAM83G in breast invasive carcinoma (BRCA) [The MEXPRESS database (a (BRCA) .https://cistrome.shinyapps.io/timer/), a user-friendly web-based interface, was used to systematically evaluate the tumor infiltration of different immune cells [The TIMER database  was an innovative online database that provided literature-based information on the interactions between oncogene products and chemotherapeutic compounds. We used this tool to screen potential therapeutic compounds that could target and reduce the mRNA expression of FAM83A, FAM83D, FAM83F, and FAM83G .FAM83 family genes and GAPDH gene on the Fast-Real-Time PCR System. GAPDH was used as a reference gene. The primers were designed by Shanghai Sangon Biotech Co., Ltd., and the sequence of primers can be found in Supplementary Table −ΔΔCt method [Total RNA extracted from twenty pairs of fresh breast cancer and healthy breast tissues were isolated using Trizol solution . Extracted RNA was then reverse-transcribed using a cDNA synthesis kit (TaKaRa) following the manufacturer’s instructions. qPCR was performed using the SYBR Green PCR Master Mix (TaKaRa) and primers binding to the t method . These bt test. All these P values were two-sided, and the significance was at P < 0.05. Unless otherwise stated, analyses were performed by SPSS 25.0 software.The comparison between the expression level of FAM83 family mRNAs in cancer tissues and normal tissues were calculated by independent sample FAM83 family genes using the UALCAN database . We found that not all members of the FAM83 family had the same effects on RFS. High expression of FAM83A (hazard ratios (HR) = 1.62 (1.02–2.57); P = 0.04; Fig. P = 0.0022; Fig. P = 0.028; Fig. P = 0.4; Fig. P = 0.12; Fig. P = 0.078; Fig. P = 0.087; Fig. P = 0.12; Fig. Next, we explored the effect of the FAM83 family on overall survival (OS). Similar to RFS, we found that not all members of the FAM83 family had the same effect on OS. High expression of FAM83C (HR = 1.57 (1.14–2.18); P = 0.006; Fig. P = 0.044; Fig. P = 0.043; Fig. P = 0.058; Fig. P = 0.24; Fig. P = 0.11; Fig. P = 0.071; Fig. Next, we investigated the effect of the gene expression levels of the  1.57 1.1–2.18; P  1.57 1.1–2.18; P  1.57 1.1–2.18; P  1.57 1.1–2.18; P  1.57 1.1–2.18; P  1.57 1.1–2.18; P  1.57 1.1–2.18; P  1.57 1.1–2.18; P Based on the analysis above, the following genes were selected for downstream analysis: FAM83A, FAM83D, FAM83F, and FAM83G. The oncogene selection was based on two criteria: high gene expression in breast cancer tissues and adverse effects on patients’ RFS or OS.The UALCAN database was a powerful integrated data-mining platform for analyzing cancer OMICS data. Hence, we utilized this database to explore the expression levels of the selected oncogenes based on patients’ age, individual cancer stage, menopause status, nodal metastasis stats, and breast cancer subclasses.P = 8.22E − 04). However, among breast cancer patients in different age subgroup, the difference in FAM83A expression was not significant . Among breast cancer patients in different subgroup of individual cancer stage, the expression of FAM83A in stage 2 patients was significantly higher than in stage 1 patients, the difference in other comparison was not significant . However, among breast cancer patients in different subgroup of menopause status, the difference in FAM83A expression was not significant . Among breast cancer patients in different subgroup of nodal metastasis status, the expression of FAM83A in N3 patients was significantly higher than in N1 patients, the difference in other comparison was not significant . The expression of FAM83A in HER2-positive patients was also significantly higher than in luminal patients and triple negative breast cancer (TNBC) patients . The expression of FAM83D in 21–40 years old patients was significantly higher than in 61–80 years old patients. The expression of FAM83D in 21–40 years old patients was also significantly higher than in 81–100 years old patients. Additionally, the expression of FAM83D in 41–60 years old patients was significantly higher than in 81–100 years old patients. The difference in other comparison was not significant . Among breast cancer patients in different subgroup of individual cancer stage, both the expression of FAM83D in stage 2 and stage 3 patients were significantly higher than in patients with stage 1. The expression of FAM83D in stage 4 patients was significantly higher in patients with stage 2. The difference in other comparison was not significant . However, among breast cancer patients in different subgroup of menopause status, the difference in FAM83D expression was not significant . Among breast cancer patients in different subgroup of nodal metastasis status, the difference in all comparison was significant except in “N0 vs N1” and “N0 vs N2”. The result was shown in Supplementary Fig. P = 1.62E − 12). And among breast cancer patients in different subgroup of major subclasses, the differences in all comparison were significant. The result was shown in Supplementary Fig. For FAM83D, 21–40 years old patients have the highest FAM83D expression (P = 2.02E − 05). However, among breast cancer patients in different age subgroup, the difference in FAM83F expression was not significant . Among breast cancer patients in different subgroup of individual cancer stage, the difference in FAM83F expression was not significant . Among breast cancer patients in different subgroup of menopause status, the difference in FAM83F expression was not significant . Among breast cancer patients in different subgroup of nodal metastasis status, the difference in all comparison was not significant except in “N0 vs N1” and “N0 vs N3”. The result was shown in Supplementary Fig. P = 1.09E − 09). And the expression of FAM83F in TNBC patients was significantly higher than in Luminal and HER2-positive breast cancer patients. The result was shown in Supplementary Fig. For FAM83F, 21–40 years old patients have the highest FAM83F expression (P = 1.93E − 11). However, among breast cancer patients in different age subgroup, the difference in FAM83G expression was not significant . Among breast cancer patients in different subgroup of individual cancer stage, the difference in FAM83F expression was not significant . The expression of FAM83G was significantly higher in pre-menopause patients than in peri-menopause patients. And the expression of FAM83G was also significantly higher in post-menopause patients than in peri-menopause patients. The result was shown in Supplementary Fig. P = 1.90E − 03). Among breast cancer patients in different subgroup of nodal metastasis status, the difference in all comparison was not significant. The result was shown in Supplementary Fig. P = 3.91E − 10). The expression of FAM83G in TNBC patients was significantly higher in Luminal and HER2-positive breast cancer patients. In addition, the expression of FAM83G in Luminal patients was also significantly higher than in HER2-positive breast cancer patients. The result was shown in Supplementary Fig. For FAM83G, 61–80 years old patients have the highest FAM83G expression , the In order to obtain mechanistic insights, we firstly performed a protein–protein interaction analysis. We found several genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G, including BRCA1, SKP2, and PLEKHB2 Fig. A. To gaiNext, we assessed the role of methylation in FAM83 gene expression. Using MEXPRESS and UALCAN analysis, we explored if the expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA was regulated by promoter DNA methylation.Interestingly, the tools gave slightly different results. Using MEXPRESS, we found that a low expression region was significantly associated with a high promoter DNA methylation status in most CpG islands for FAM83A, FAM83D, FAM83F, and FAM83G Fig. A–D. HoweWe further explored the relationships between immune cell infiltration and FAM83A, FAM83D, FAM83F, and FAM83G expression using the “TIMER” analysis tool. The expression of FAM83A was negatively correlated with the infiltration of CD4 + T cells but positively correlated with the infiltration of macrophages and neutrophils Fig. A. The exP = 0.45, Supplementary Fig. P = 3.3E − 13, Supplementary Fig. P = 1.9E − 8, Supplementary Fig. P = 8.1E − 22, Supplementary Fig. P = 0.12, Supplementary Fig. P = 0.82, Supplementary Fig. P = 0.03, Supplementary Fig. P = 3.4E − 10, Supplementary Fig. P = 0.55, Supplementary Fig. P = 1.6E − 4, Supplementary Fig. P = 3.1E − 5, Supplementary Fig. P = 1.7E − 18, Supplementary Fig. Besides the “TIMER” analysis tool, we also used the “ESTIMATE” analysis tool to validate these data with immune cells. As a result, we found that, for StormalScore, FAM83A expression was not significantly related to StromalScore (FAM83 oncogene in breast cancer patients.Taking advantage of the CTD database, we aimed to screen potential therapeutic compounds that could decrease the mRNA expression of the Interestingly, we found that different members of the FAM83 family are affected by different drugs, maybe suggesting a distinct action mechanism. We found that abrine, arsenite, ethinyl estradiol, and other compounds can decrease the mRNA expression of FAM83A Table . For FAMFAM83 family has been recently described as a novel oncogene family [FAM83 family members had a highly conserved N-terminal DUF1669 domain. This domain, with unknown function, was necessary to drive the oncogenic transformation [FAM83 family proteins in breast malignancies for the first time.The e family , 13, butormation , 8, 26. FAM83 family did not have such effects. We performed a functional and pathway enrichment analysis of genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G. We found that these genes played an essential role in several cancer-related biological processes such as cell proliferation, G2/M transition of the mitotic cell cycle, and regulation of apoptosis. Additionally, they were enriched in different cancer-related signaling pathways, including the Hippo, Hedgehog, and PI3K/AKT pathways. We also found that the expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA was regulated by promoter DNA methylation in most CpG islands and significantly related to immune cell infiltration. At last, we screened different compounds using the CTD database and identify potential therapeutic compounds which can decrease the mRNA expression of FAM83A, FAM83D, FAM83F, and FAM83G mRNA for breast cancer. These cancer-related signaling pathways, the results of promoter methylation and the potential compounds for FAM83A, FAM83D, FAM83F, and FAM83G were found by us for the first time.In this study, we found that FAM83A, FAM83D, FAM83F, and FAM83G were higher expressed in breast cancer tissues compared to normal tissues and had an adverse effect on the patients’ survival outcomes. However, other members of the It has been previously reported that FAM83A expression was elevated in breast cancer, pancreatic cancer, and hepatocellular cancer –29. CuipMany studies have found that FAM83D can promote the development of colorectal cancer , invasivFew reports are available for FAM83F and FAM83G. Gongchun Fan et al. found that FAM83F was upregulated in lung adenocarcinoma cells, and the high expression was related to cancer progression and poor survival. Also, FAM83F can reduce the sensitivity to cisplatin or docetaxel of lung adenocarcinoma cells . MohammeOnly a few studies found that FAM83G can control Wnt signaling by association with casein kinase 1α . FAM83G We would like to acknowledge the limitations of our study. First, we did not validate the prognostic effect of FAM83A, FAM83D, FAM83F, and FAM83G in breast cancer clinical samples. Second, we need to perform a functional experiment to explore the role of FAM83A, FAM83D, FAM83F, and FAM83G in the development of breast cancer in vitro and in vivo. At last, we would also like to explore the regulatory mechanism of FAM83A, FAM83D, FAM83F, and FAM83G in breast cancer in the future.In conclusion, we found that FAM83A, FAM83D, FAM83F, and FAM83G were highly expressed in breast cancer tissues and had an adverse effect on breast cancer patients’ survival outcomes. Genes co-expressed with FAM83A, FAM83D, FAM83F, and FAM83G might be enriched in the Hippo, Hedgehog, and PI3K/AKT signaling pathways, therefore playing an essential role in the development and progression of breast cancer. FAM83A, FAM83D, FAM83F, and FAM83G may serve as potential therapeutic targets for breast cancer clinical treatment, and further research needs to be done in this direction.Additional file 1: Figure S1. The expression of FAM83A based on different clinicopathological characteristics by UALCAN database.Additional file 2: Figure S2. The expression of FAM83D based on different clinicopathological characteristics by UALCAN database.Additional file 3: Figure S3. The expression of FAM83F based on different clinicopathological characteristics by UALCAN database.Additional file 4: Figure S4. The expression of FAM83G based on different clinicopathological characteristics by UALCAN database.Additional file 5: Figure S5. Exploration the expression and subcellular localization of FAM83A by the HPA database.Additional file 6: Figure S6. Exploration the expression and subcellular localization of FAM83D by the HPA database.Additional file 7: Figure S7. Exploration the expression and subcellular localization of FAM83F by the HPA database.Additional file 8: Figure S8. Exploration the expression and subcellular localization of FAM83G by the HPA database.Additional file 9: Figure S9. The relationships between immune cell infiltration and the expression of FAM83A, FAM83D, FAM83F and FAM83G by “ESTIMATE” analysis tool.Additional file 10: Table S1. The sequence of primers.Additional file 11: Table S2. The basic clinicopathological characteristics of these twenty patients.


Structural and functional alterations of vasculature caused by age-related factors is critically involved in the pathogenesis of ischemic stroke. The longevity genes sirtuins (SIRTs) are extensively investigated in aging-associated pathologies, but their distinct roles in ischemic stroke still remain to be clarified. To address this question, we applied oxygen and glucose deprived/reperfusion (OGD/R) to induce ischemic injury in human endothelial cells (ECs), which are the main component of vasculature in the brain. The results showed that OGD/R led to various damages to ECs, including compromised cell viability, increased LDH release, overproduced ROS, enhanced apoptosis and caspase activity. Meanwhile, the expression of mitochondrial SIRT3 was robustly decreased in ECs after OGD/R treatment. Consistently, rescue of SIRT3 by ectopic expression, but not nuclear SIRT1, in ECs reversed the OGD/R-induced cell damage. Interestingly, some front-line drugs for ischemic stroke, including clopidogrel, aspirin and dl-3-n-butylphthalide (NBP), also rescued SIRT3 and reduced OGD/R-induced endothelial injury, suggesting that the recovery of SIRT3 expression was critical for the protection of these drugs. Moreover, our results demonstrated that 10-hydroxy-NBP (OHNBP), a major metabolite of NBP, showed better blood-brain barrier crossing capability than NBP, but still retained the effects on SIRT3 by NBP. Together, our results suggested that SIRT3 may serve as a potential novel target for treatment of ischemic stroke.  Ischemic stroke is a leading cause of death and long-term disability, and is usually caused by the occlusion of cerebrovascular vessels, which leads to an interrupted or reduced supply of blood to the brain and induces the necrosis of brain tissues. Currently, thrombolysis using intravenous recombinant tissue plasminogen activator (rtPA) is the only therapy approved by the FDA for ischemic stroke, while it can only be delivered to a small fraction of patients due to the narrow treatment window . Other tAging is the most robust risk factor for ischemic stroke, which doubles every 10 years after age 55 years. Approximately three-quarters of all strokes occur in persons aged ≥65 years . With ag+-dependent deacetylases with a broad range of biological processes, such as aging, cell metabolism, inflammation and apoptosis, which are key to the stroke pathophysiology [Several proteins regulating aging are shown to determine cerebral damage in ischemic injury. Specifically, sirtuins are NADysiology . SIRT3 iysiology ,14. Prevysiology ,16. SIRTysiology ,18,19. Tysiology ,21, but In view of the important role of endothelial cells in microvascular circulations, we hypothesized that the endothelial expression of the longevity gene SIRT3 may be protective in ischemia-like models. Here, we verified that SIRT3, but not SIRT1, decreased in ECs damaged by OGD/R. Furthermore, we investigated whether rescue of SIRT3 expression in ECs could reverse the OGD/R-induced damage. Furthermore, we tested whether the pharmacological phenotype of clinical drugs, such as clopidogrel (Clo), aspirin (Asp), NBP and edaravone (ED), was linked to the target SIRT3. Finally, we optimized NBP to enhance its ability to cross the blood-brain barrier, and retain the protective effects on SIRT3 by NBP.OGD/R could induce EC injury including decreased cell viability, increased LDH release, apoptosis and promoted ROS production. Here, we treated ECs with OGD for 2 h, 4 h or 6 h and measured cell viability after reperfusion at 24 h using CCK-8 or CTG methods. We observed that OGD/R induced decreased cell viability, increased LDH release both in HUVEC cells A–C and HThe increasing evidence of sirtuins in the field of age-related diseases, such as ischemic stroke, indicates that they may provide novel targets for treating ischemic stroke . First, OE cells and revealed approximately 2-fold increases of total SIRT3 protein, compared with the vector group , aspirin (Asp), dl-3-n-butylphthalide (NBP) and edaravone (ED), is actually linked to the target SIRT3 is required. First, we performed Western blotting analysis to determine how these clinical drugs affected SIRT3 in ECs after exposure to OGD/R. As shown in Furthermore, we asked whether these findings could be applied in improving the clinical drugs for ischemic stroke, such as NBP. Based on our findings, we hypothesized that NBP derivatives, which showed better blood-brain barrier crossing capability and retained the enhancing effects on SIRT3, might be considered as promising drug candidates for ischemic stroke. Furthermore, 10-hydroxy-NBP (OHNBP), a major circulating metabolite of NBP, was reported to have a much greater ability to cross the blood-brain barrier than NBP ,25,26. T2O2, compared to NBP , clopidogrel (Clo) and edaravone (ED) were purchased from MedChemExpress . NBP and OHNBP were synthesized by WuXi AppTec . The other reagents and materials are mentioned in each method.Healthy male Sprague Dawley rats weighing 180–240 g were purchased from Beijing Vital River Experimental Animal Technology Co., Ltd. . The rats were housed in a specific-pathogen-free laboratory, kept under standard environmental conditions  and had free access to standard food.2/95% air (v/v) at 37 °C. SK-N-SH cells were purchased from ATCC. SK-N-SH cells were cultured in MEM supplemented with 10% FBS and 1% penicillin/streptomycin in a humidified incubator with 5% CO2/95% air (v/v) at 37 °C.HUVEC and HBMEC cells were purchased from ATCC and BeNa Culture Collection, respectively. The two endothelial cell lines were cultured in ECM supplemented with 5% FBS, 1% ECGS and 1% penicillin/streptomycin  in a humidified incubator with 5% CO3 HUVEC, 4 × 103 HBMEC or 4 × 103 SK-N-SH cells were cultured in 96-well plates under normal conditions for 24 h. After pretreated with or without NBP, OHNBP, Clo, Asp or ED  for 2 h, the culture was replaced by glucose-free DMEM  and placed under hypoxia conditions  at 37 °C for applied time periods. The medium was then replaced by normal medium without FBS and reoxygenated under normoxic conditions  for another 24 h to induce OGD/R injury. Cells incubated in normal medium without FBS under a normoxic atmosphere were used as the control.Approximately 4 × 102O2 was added to the SK-N-SH cells for 1 h or 2 h with or without pretreatment with NBP or OHNBP for 2 h. The SK-N-SH cells were then cultured with or without NBP or OHNBP for another 24 h to detect cell viability.Furthermore, 1 mM HCell viability was detected using Cell Titer-Glo Luminescent Assay (CTG)  or the Cell Counting Kit-8 (CCK-8) , and all experiments were performed according to the manufacturer’s instructions. Luminescence or absorbance were measured by BioTek SynergyNEO .The release of cytoplasmic LDH indicates the loss of cell membrane integrity, which represents the death of the cell. We used the commercial LDH-Cytotoxicity Colorimetric Assay Kit purchased from Biovison , according to the manufacturer’s instructions. In brief, 50 μL supernatant from each cell culture well was collected for a coupled enzymatic reaction in which LDH catalyzed the conversion of lactate to pyruvate via the reduction of NAD+ to NADH for 30 min at room temperature. It was then measured spectrophotometrically at 490 nm with a BioTek SynergyNEO . + PI− cells + Annexin V+ PI+ cells)/total number of cells × 100. The early apoptosis rate (%) was calculated as Annexin V+ PI− cells /total number of cells × 100. The late apoptosis rate (%) was calculated as Annexin V+ PI+ cells /total number of cells × 100.Cell apoptosis was detected using an Annexin V-FITC/propidium iodide (PI) apoptosis assay kit . HUVECs or HBMECs were cultured in 12-well plates and harvested after OGD/R treatment. The cells were then stained with Annexin V-FITC and PI according to the manufacturer’s instructions and analyzed using flow cytometry. The total apoptosis rate (%) was calculated as   was used as a probe. Following OGD/R treatment, HUVEC cells were incubated with 10 μM DCFH-DA in culture medium without FBS at 37 °C for 30 min. The cells were washed with PBS twice and then captured by a fluorescence microscope . Alternatively, the fluorescence intensity of the cells in the 96-well black plate after exposure to OGD/R was measured using a BioTek SynergyNEO  at an excitation wavelength of 488 nm and an emission wavelength of 525 nm.6 cells in 100-mm dishes. On the following day, cells were transfected with 10 μg Fugw-SIRT3 constructs, 7.5 μg of pSPAX2 and 5 μg of pMD2G via PEI. The virus-containing supernatant was collected at 48 h and 72 h, respectively, and it was then centrifuged at 1000× g for 5 min, before being passed through 0.45 μm filters. The lentivirus was further concentrated by ultracentrifugation at 27,000× g for 2 h. The pellets were then resuspended in 200 μL PBS, aliquoted and stored at −80 °C. For SIRT3 or SIRT1 overexpression experiments, HUVECs or HBMECs were seeded in 6-well plates before concentrated lentivirus infection (minimum multiplicity of infection) in the presence of polybrene . After 12 h, the medium was refreshed. The expression of SIRT3 or SIRT1 was determined by Western blotting at 72 h. For SIRT3 or SIRT1 overexpression experiments, SIRT3 pcDNA3.1 or SIRT1 pcDNA3.1 were cloned into a Fugw vector. Lentivirus packing and infection were conducted as follows. HEK293T cells were seeded at a density of 7.5 × 10After treatment with OGD/R, cells were extracted by 500 μL TRIzol Reagent  to obtain total RNA according to the manufacturer’s instructions. Reverse transcription was conducted using PrimeScript RT master mix  under the following conditions: 37 °C, 15 min and 85 °C, 15 s. Then, the 25 μL reaction system containing 4 μL prediluted cDNA, 0.25 μM of each primer and 2 × HotStart SYBR Green qPCR master mix , was analyzed using a Stratagene Mx3000P . The reaction parameters were as follows: 95 °C for 10 min; 95 °C for 30 s, 40 cycles; 60 °C for 30 s; and 72 °C for 30 s. An additional cycle was performed for evaluation of the primer’s dissociation curve: 95 °C for 1 min, 60 °C for 30 s and 95 °C for 30 s. GAPDH was used as an internal control. Primers used were as follows:SIRT1, forward 5′-AGGCCACGGATAGGTCCATA-3′, reverse 5′-GTGGAGGTATTGTTTCCGGC-3′; SIRT3, forward 5′-TGGGCTTGAGAGAGTGTCGG-3′, reverse 5′-GAACCCTGTCTGCCATCACG-3′; eNOS, forward 5′-AGCACATTTGGGAATGGGGAT-3′, reverse 5′-AGCGGATCTTATAACTCTTGTGC-3′; GAPDH, forward 5′-AGATCCCTCCAAAATCAAGTGG-3′, reverse 5′-GGCAGAGATGATGACCCTTTT-3′.After treatment with OGD/R, total cell lysates were separated by 10 or 12.5% SDS-PAGE gels, and then transferred onto nitrocellulose membranes. Each membrane was blocked with 5% nonfat milk in TBST for 1 h at room temperature. Membranes were subsequently incubated with the designated primary antibodies: SIRT3 , SIRT1 , eNOS , procaspase-3 , cleaved caspase-3  and actin , at 4 °C overnight followed by horseradish peroxidase (HRP)-conjugated secondary antibody. Membranes were then incubated with an ECL substrate and visualized by mini chemiluminescent imaging and analysis system.n = 3 for each timepoint), and then the brain and plasma were obtained and stored at −20 °C for LC/MS test.SD rats were orally administered with 20 mg/kg NBP or OHNBP dissolved in soybean oil at 2 mg/mL. Rats were sacrificed at 0.5 h, 2 h, 4 h and 8 h after administration  was prepared by centrifugation of blood at 1000 rpm × 10 min at room temperature and transferred to polypropylene tubes. To induce platelet aggregation, platelets were activated in the absence or presence of agonists. Particularly, aliquots (85 μL) of PRP were preincubated with 100 μM NBP, OHNBP or Asp for 10 min. For platelet labeling, 1 μL of anti-human CD61-FITC was added for 15 min at room temperature in the dark. The agonist was then added for 5 min at 37 °C under low shear stress conditions. The following platelet agonists were used: 1 mM arachidonic acid , 10 μg/mL collagen  and 10 μM ADP . After incubation, samples were fixed with 400 μL of 4% PFA. Finally, platelet aggregates were analyzed by FCM as CD61-FITC positive events in the region.t test or one-way ANOVA and Tukey’s multiple comparisons test using GraphPad Prism 9.0 . A value of p < 0.05 was defined as statistically significant.The data are presented as means ± SD, and were analyzed statistically with student


The results of RNA-seq showed that the average reads among the four groups were 11.0 Gbase. The genome mapping rate averaged 88.1% and the gene mapping rate averaged 82.5%. The analysis of differentially expressed genes (DEGs) indicated that the peroxisome proliferator-activated receptors (PPAR), cAMP, and calcium signaling pathways and muscle contraction in muscle tissue were linked to thermogenesis in cold-exposed lambs. Furthermore, PCK1 (phosphoenolpyruvate carboxykinase1) increased glyceroneogenesis in cold-exposed Altay lambs, and APOC3 (apolipoprotein C3), LPL (lipoprotein lipase), and FABP4  were involved in the intake and transport of free fatty acids. In Hu sheep, cAMP biosynthesis from ATP hydrolysis was regulated by ADCY10 (adenylate cyclase) and ADORA2a (adenosine A2a receptor). Skeletal muscle contraction was regulated by MYL2 (myosin light chain 2). In conclusion, cold exposure altered the expression level of genes involved in heat production in muscle tissue. Some potential mechanisms were revealed, including calcium ion transport in the calcium signaling pathway, fatty acid metabolism in the PPAR signaling pathway, and cAMP biosynthesis in the cAMP signaling pathway. This study implied that skeletal muscle plays an important role in thermoregulation in lambs.Cold tolerance is an important trait for sheep raised at high altitudes. Muscle tissue, comprising 30–40% of the total body mass, produces heat during cold exposure. However, little is known about the genetic mechanisms of this tissue and its role in thermogenesis in lambs. We examined genes in skeletal muscle tissue in a cold-adapted sheep breed, Altay, and a cold-intolerant sheep breed, Hu, when exposed to low air temperature. Three ewe-lambs of each breed were maintained at −5°C and three ewe-lambs of each breed were maintained at 20°C. After cold exposure for 25 days, the  Cold stress has negative effects on livestock production. For example, feed digestibility is reduced, respiratory disorders are increased, and blood-flow distribution is affected in ruminants . Neonata2+-leakage is mediated by sarcoplasmic reticulum Ca2+-ATPase (SERCA), ryanodine receptor 1 (RyR1), sarcolipin (SLN) and other factors (The capacity of heat production (HP) is critical for animals to maintain core body temperature and prevent hypothermia when exposed to cold. Both shivering thermogenesis (ST) and non-shivering thermogenesis (NST) increase HP in cold-exposed animals. ST generally occurs in the initial stage of cold exposure, and is related to actin, myosin, and hydrolysis of ATP . With pr factors . In a st factors . A betteUCP1 in retroperitoneal adipose tissue than lean sheep  and six Hu (31.8 ± 2.7 kg) ewe-lambs, 6-month of age, were used in this study. All lambs were maintained individually in metabolic cages (1.2 m × 0.6 m × 1.8 m), and offered alfalfa pellets (12.4 MJ metabolizable energy and 148 g/kg crude protein on dry matter basis) -libitum , with frAll lambs were kept at 20°C for 7 days to adapt to the conditions, and then were divided into four groups. Three-Altay lambs (AM1) and three Hu lambs (HM1) were kept in a room in which the temperature was lowered from 20°C to −5°C by reducing the temperature by 5°C every 2 days for 25 days . The othlongissimus dorsi muscle tissue was collected from each lamb and placed in a 5 ml Eppendorf tube filled with RNAlater  to prevent the degradation of RNA from degrading. All samples were snap-frozen immediately in liquid nitrogen and then stored at −80°C for analysis.On day 36, the lambs were euthanized with an intravenous injection of 20% sodium pentobarbital . Then the Total RNA from each muscle was extracted using Trizol reagent  according to the manufacturer’s instructions. The concentration and integrity of total RNA were assessed using the Agilent 2100 Bioanalyzer . All RNA samples were screened at the RNA Integrity Number (RIN) > 7.0 and the 28S/18S rRNA ratio > 1.0.The poly-A mRNA was isolated from the total RNA using OligodT magnetic beads  and fragmented using RNA fragmentation kits . The first-strand cDNA was synthesized from the fragmented mRNA using random oligonucleotide primers and reverse transcriptase . The synthesis of the second cDNA usd DNA polymerase I and RNAase Ⅱ treatments. The cDNA fragments produced had a single ‘A' nucleotide base added, followed by ligation of an adapter. The products were then purified and enriched with PCR amplification to create the final cDNA library.Ovis aries  using the HISAT alignment tool . The expression levels of genes were calculated and assessed based on the FPKM (fragments per kilobase of exon model per million reads mapped) value.The raw reads were filtered with quality control software of SOAPnuke  to exclude low quality reads , adaptor reads (reads with joint contamination), and highly unknown base N content . Clean reads obtained were aligned to the reference genome of http://www.geneontology.org/) with a threshold of adjusted p ≤ 0.05.Gene ontology analyses was used to analyze the functions of differentially expressed genes (DEGs). Significantly changed GO terms were obtained by mapping DEGs to the online GO database (http://www.kegg.jp/kegg/pathway.html/). The significantly enriched pathways for DEGs were screened according to the adjusted p ≤ 0.05.The KEGG pathway were analyzed based on the online database .lipoprotein lipase (LPL), troponin C1, slow skeletal and cardiac type (TNNC1), fatty acid binding protein 4, adipocyte (FABP4), and parvalbumin (PVALB), were verified using quantitative real-time PCR (qRT-PCR). The primers were designed using Primer Premier 6   with gDNA Erase , according to the manufacturer’s instructions. The qPCR reaction contained 2 µl of cDNA, 0.8 µl of each forward and reverse primers (10 um/ul), 10 µl of TB GreenTM Premix Ex Taq II , 6 µl of RNAase free water, and 0.4 µl of ROX Reference Dye II (50×). The qPCR reaction used an Aligent Mx3000P system , and a two-step amplification method: 1) a pre-degeneration of 15 s at 95°C; and 2) 5 s at 95°C and 34 s at Tm for 40 cycles. Cycle threshold (CT) values were recorded and relative expressions were determined according to the 2-△△ct method  , and thet method .p  ≤ 0.001 and |log2FC| ≥ 1. The adjusted p value is similar to the p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate, and it effectively avoids some null hypotheses with significant features . Significance was accepted at a All lambs appeared to be healthy throughout the study. The cold-exposed Altay and Hu lambs increased their rectal temperatures . In total, 20,122 genes were co-expressed in all lambs, with 441, 256, 326, and 375 genes expressed only in AM1, AM2, AH1, and AH2 lambs, respectively .versus HM1 and AM2 versus HM2  were classified into cellular components, molecular functions, and biological processes  pathways are presented in supplementary file 4. The PPAR and calcium signaling pathways changed significantly in Altay lambs at -5°C, while the calcium and cyclic AMP (cAMP) signaling pathways were enriched in Hu lambs at −5°C . The DEGThe results of KEGG pathways displayed substantial genetics differences between Altay and Hu lamb . The calFABP4, LPL, PVALB, PCK1 (phosphoenolpyruvate carboxykinase1), PLIN1 (perilipin 1), APOA2 (apolipoprotein A2), APOC3 (apolipoprotein C3), and SLC27A6 (solute carrier family 27 members 6). Direct relations among their coding proteins, predicted by String Database, were detected  were selected for qRT-PCR analysis   , and thel (VDAC)  and SLN l (VDAC)  increasel (VDAC)  was likel (VDAC)  and fat l (VDAC) , and UCPBCAS1 (breast carcinoma amplified sequence 1), CALN1  and CABP1  increased in cold-exposed Altay lambs. The calcium ion is a second messenger in cell excitation and excitation-contraction coupling in the myocyte, and the calcium signaling pathway is likely involved in producing heat. In cell contraction, passive calcium ions in the SR are transported into the cytoplasmic matrix by the ryanodine receptor (RYR). To maintain intracellular calcium homeostasis, calcium is propelled back into the SR via the SERCA against the concentration gradient (2+ transport (SERCA1 (or ATP2A1), RYR1, SLN, and VDAC1. One possible reason is that they may contribute to thermogenesis (including NST) via coding protein and post-translational modifications, as reported difference in cold-induced protein expression in mice (longissimus dorsi contains mainly fast-twitch type Ⅱ fibers that are highly glycolytic (extensor digitorum longus (EDL). In contrast, the soleus muscle is composed of more slow-twitch type I fibers that are highly oxidative. The muscle with slow- and fast-twitch fibers in cold-induced thermogenesis differ in their contractile apparatus, metabolic features, and protein level of SERCA   . Extracele (TCA) PCK1, PLIN1, FABP4, APOA2, SLC27A6, APOC3, and LPL, in the PPAR pathway increased in cold-exposed Altay. Some of these DEGs are likely involved in the metabolism of glucose and fatty acids in cells. PCK1 is a cytosolic isoenzyme of phosphoenolpyruvate carboxylase, and also an important rate-limiting enzyme that catalyzes the synthesis of phosphoenolpyruvic acid from oxaloacetate  and impair the ability to break down fat  and Fos were greater whereas Adenylate Cyclase (ADCY10) and Adenosine A2a Receptor (ADORA2a), which were linked to energy homeostasis, were lesser in cold-exposed lambs than the lambs at 20°C. ADCY10 codes soluble adenylyl cyclase (sAC), which catalyzes the synthesis of cAMP by ATP from TCA and oxidative phosphorylation in intracellular mitochondria  signaling pathway changed in cold-exposed Hu lambs. It was reported that intracellular cAMP deviated from the hydrolysis of ATP, mediated by adenylyl cyclase (AC) and some ions , with concomitant heat generation. Four annotated DEGs in the cAMP signaling pathway were identified. chondria . ADORA2achondria , and, inchondria . These rBDNF (brain-derived neurotrophic factor), PVALB, TNNC1, MYL2 (myosin light chain 2), and ACTC1 , were higher in muscle tissue of Altay lambs than Hu lambs.Regardless of air temperature, calcium signaling pathways and cardiac muscle contraction differed between Altay and Hu lambs. Expressions of five potential genes, including + initially binds to troponin subunit C, which leads to the conformation change of troponin, and the exposure of the myosin cross-bridge binding site on the actin chain. Then actin, bound to the cross-bridge in myosin, activates ATP hydrolysis and contributes to heat generation (+ (The muscle contraction is mediated by troponin, myosin, actin, calcium ion and ATP . Ca2+ inneration . Five pration (+ , while Tation (+ . Importaation (+ . BDNF isation (+ . The higation (+  in AltayAPOC3, FABP4, LPL, PCK1, PLIN1, CABP1, and CALN1 were up-regulated. In cold-exposed Hu lambs; the cAMP signaling pathway was activated and ADCY10 and ADORA2A were down-regulated. The gene expressions of PVALB, TNNC1, MYL2, and ACTC1 related to muscle contraction were higher in muscle tissue of Altay than Hu lambs (In the present study, UCP1 independent pathways most likely participated in thermogenesis in cold-exposed sheep. In cold-exposed Altay lambs, PPAR signaling and calcium ion pathways in muscle tissue were activated and the genes, such as Hu lambs . These r


These nanocomposites were characterized by scanning electron microscopy, X-ray diffraction, and dynamic water sorption vapor measurements. The structural characterization of these nanostructures highlighted good crystallinity including only the rutile phase. The electrochemical characteristics were investigated by cyclic voltammetry and galvanostatic charge–discharge measurements, which were performed in a KOH electrolyte solution. Among the Cu-doped TiO2 nanostructures that were prepared, the one containing 0.5% Cu exhibited superior electrochemical properties, including high specific gravimetric capacitance of 1183 F·g−1, specific capacitance of 664 F·g−1, energy density of 45.20 Wh·kg−1, high power density of 723.14 W·kg−1, and capacitance retention of about 94% after 100 cycles. The dielectric investigation shows good dielectric properties for all materials, where the dielectric constant and the dielectric loss decreased with the frequency increase. Thus, all the interconnected studies proved that these new materials show manifold ability and real applicative potential as pseudocapacitors and high-performance dielectrics. Future work and perspectives are anticipated for characterizing electrochemical and dielectric properties for materials including larger amounts of Cu dopant.In this work, pure TiO Conductive carbon black  was purchased from PowderNano.Nanostructures of pure TiOous work . Titaniu2 and Cu-doped TiO2 nanostructures according to the procedure described in a previous paper, but calcined in air at 900 °C, with a 15 °C/min heating rate [2 blank sample (T2). Cu-doped TiO2 composites were obtained by adding to solution 2 various amounts  of Cu acetate. These two solutions were mixed and the resulting homogeneous solution was transferred to an electrospinning syringe. All materials were prepared using a homemade electrospinning installation according to our previous studies [2, CT2 for 0.5% Cu-TiO2, CT3 for 1% Cu-TiO2, and CT4 for 2% Cu-TiO2.The electrospinning method was used to prepare pure TiOing rate . Briefly studies . Finally2 materials was investigated using a Verios G4 UC Scanning Electron Microscope  equipped with an energy dispersive spectrometer . A fully automated gravimetric analyzer, IGAsorp, supplied by Hiden Analytical, Warrington (UK), with an ultrasensitive microbalance, was used to measure the dynamic water vapor sorption capacity of the samples as the weight changes with variation in humidity at a constant temperature. Each sample was dried in flowing nitrogen (250 mL/min) until the weight of the sample was in equilibrium at RH < 1%. Experiments were carried out at 25 °C in the relative humidity (RH) range of 0–90%, with 10% humidity steps, each having a pre-established equilibrium time between 40 and 60 min . The Brunauer–Emmett–Teller (BET) method was used to calculate the specific surface areas of the samples [A Rigaku SmartLab-9kW  diffractometer was used to record the XRD patterns of samples and to examine their crystalline structure. The morphological analysis of the Cu-doped TiO samples . Broadba2 on nickel foam as the working electrode. This electrode was prepared according to the following protocol: 80 wt% of TiO2 and Cu -doped TiO2 nanostructures, 10 wt% of carbon black, and 10% PVDF dissolved in DMF. These mixtures were dropped on Ni foam supports (previously cleaned with ethanol in an ultrasonic bath) and then dried for 12 h at 60 °C [2 and Cu-doped TiO2 on the Ni foam electrode were approximately 5.98, 4.84, 6.48, and 6.25 mg, respectively. The specific capacitance of pure and Cu-doped TiO2 composites was studied by galvanostatic charge–discharge measurements recorded for a potential between 0 and 0.7 V at a current of 10 mA.The electrochemical measurements were performed using a Voltalab PST050 electrochemical workstation with a three-electrode system using a 1 M KOH electrolyte solution at room temperature. A platinum wire was used as the auxiliary electrode, Ag/AgCl as the reference electrode, pure and Cu-doped TiOat 60 °C . In our 2 and Cu  doped TiO2 calcined at 900 °C. These nanostructures showed good crystallinity containing only the rutile phase. It is known that rutile is the predominant phase during the calcination of TiO2 materials at 900 °C [2 and associated with the crystallographic planes (110), (101), (200), (111), (210), (211), (220), (002), (310), (301), and (112), respectively. We note that no additional peaks were identified, which means that the Cu ions were fully integrated into the rutile structure of TiO2 [2 , 0, (301), of TiO2 ,33. More of TiO2 .2 and the Cu-doped TiO2 nanostructures were examined by SEM imaging, as shown in The surface morphology of pure TiO2 and Cu-doped TiO2 nanostructures are shown in 2 composite. The sorption capacities of the prepared composites were correlated with their hysteresis behavior and were found to have the highest values for the 0.5% Cu-TiO2 composite (12.30% d.b.). From 2 to 4.31 nm for 0.5% Cu-TiO2) and their sorption capacity values. The BET surface area  than that of Ti ions (0.68 Å). Ganesh et al. also reported that upon increasing the Cu-doping concentration in TiO2 thin film, BET surface area values decreased [2/g,) is for 0.5% Cu-TiO2 having the largest average pore size  were observed (2 compared to other samples (CT3), indicating higher capacitance for this sample.The electrochemical performance of Cu-doped TiOped TiO2 , suggestobserved , demonstobserved . From th2 and Cu -doped TiO2 composites recorded at scanning rates of 25, 50, 150, and 250 mV/s. All these curves exhibited oxidation and reduction peaks positioned at the potentials of 0.46 and 0.52 V, whose intensities increased with increasing scanning speed values. The area under the CV curves for the 2% Cu-TiO2 composite at 250 mV/s was larger than the area under the CV curves for TiO2, 0.5% Cu-TiO2, and 1% Cu-TiO2 , [E, Wh·kg−1) [P, W·kg−1) [SPC, I, Δt, ΔV, and m are the specific capacitance, the charge–discharge current (A), the discharging time (s), the applied potential window (V), and the active mass of the electrode (g), respectively.Based on the galvanostatic charge–discharge curves, the specific capacitance ,  the enerWh·kg−1) , and pow W·kg−1)  were cal2 (CT2) nanostructure showed a longer charge–discharge time than the other doped materials , demonstrating the highest specific capacitance. Several factors can affect the specific capacitance of the electrode materials, including specific surface area, shape, nanostructure size, and conductivity [−1 (at 0.15 V/s) corresponds to sample CT2 (with 0.5% Cu). Pure TiO2 had a specific capacitance value of 526 F·g−1 (at 0.15 V/s), which is higher than previously reported for some other TiO2-based electrodes [2 carbon nanofibers can be successfully used as electrodes for supercapacitors due to their outstanding specific properties, including specific capacity (106.57 F·g−1) and 84% capacity retention after 2000 cycles. Likewise, He et al. prepared electrode material based on TiO2 nanofiber using an electrospinning technique and then treated them in KOH. The specific capacity found for this material improved dramatically from 0.04 F·g−1 (for untreated TiO2) to 65.84 F·g−1 at 1 mV/s [From galvanostatic charge–discharge measurements, it was found that the 0.5% Cu-doped TiOuctivity . Figure ectrodes ,43,44. Fectrodes  reported2 shows better electrochemical performance compared to the pure TiO2 nanoparticles, suggesting that copper contributes to the improvement of the electronic conductivity of TiO2. Therefore, the highest values for energy density (45.20 Wh·kg−1) and power density (723.14 W·kg−1) were found for 0.5% Cu-doped TiO2 composite.The specific capacitance, energy density, and power density values for all evaluated materials using Equations (1)–(4) are listed in 2 and Cu-TiO2 electrode materials, this parameter was evaluated by measuring the consecutive charge–discharge curves (2 (88%), suggesting good stability at 100 cycles. Moreover, the results obtained in this work indicated that the electrochemical performance of Cu-doped TiO2 is superior to pure TiO2 and is comparable to other related metal-doped TiO2 systems found in the literature  and the phase angle (θ = tan−1 (Zim/Zre), where Zim and Zre are the imaginary and the real components of the impedance) with the frequency at various temperatures are represented in a common diagram in 2 samples. This may suggest that copper doping acts as a source of free charge carriers.The behavior of the measured conductivity  Cu-doped TiO2 composites exhibited a higher specific capacitance, better rate capability, and cycling stability compared to pure TiO2, although the particle size and pore size were larger and surface area was lower. It was found that the 0.5% Cu-doped TiO2 (CT2) nanostructure showed a longer charge–discharge time (227 s) than other doped materials, demonstrating the highest specific capacitance. It was found that 0.5% Cu-TiO2 is the optimal dopant concentration in terms of electrochemical and capacitive performances. The maximum capacitance value obtained for this material was 1183.89 F·g−1 (at 0.15 V/s). After 100 charge–discharge cycles, the capacitance retention of about 94% for Cu-doped TiO2 shows good cycling stability. The dielectric constant, the dielectric loss, and the dc conductivity of materials in the frequency range 10 Hz–1 MHz were measured at different temperatures. With increasing frequency and temperature, the electric conductivity increased. Therefore, regarding its use in pseudocapacitor applications, electrospun Cu-doped TiO2 nanostructures exhibit superior electrochemical properties compared to pure TiO2. To determine to what extent and how this can influence the electrochemical and dielectric properties, future work and perspectives will be directed toward investigating these characteristics when using larger amounts of Cu dopant.The pure TiO