Many primary-tumor subregions exhibit low levels of molecular oxygen and restricted access to nutrients due to poor vascularization in the tissue, phenomenon known as hypoxia. Hypoxic tumors are able to regulate the expression of certain genes and signaling molecules in the microenvironment that shift it towards a more aggressive phenotype. The transcriptional landscape of the tumor favors malignant transformation of neighboring cells and their migration to distant sites. Herein, we focused on identifying key proteins that participate in the signaling crossroads between hypoxic environment and metastasis progression that remain poorly defined. To shed light on these mechanisms, we performed an integrated multi-omics analysis encompassing genomic/transcriptomic alterations of hypoxia-related genes and Buffa hypoxia scores across 17 pancarcinomas taken from the PanCancer Atlas project from The Cancer Genome Atlas consortium, protein–protein interactome network, shortest paths from hypoxia-related proteins to metastatic and angiogenic phenotypes, and drugs involved in current clinical trials to treat the metastatic disease. As results, we identified 30 hypoxia-related proteins highly involved in metastasis and angiogenesis. This set of proteins, validated with the MSK-MET Project, could represent key targets for developing therapies. The upregulation of mRNA was the most prevalent alteration in all cancer types. The highest frequencies of genomic/transcriptomic alterations and hypoxia score belonged to tumor stage 4 and positive metastatic status in all pancarcinomas. The most significantly associated signaling pathways were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. The interactome network revealed high-confidence interactions among hypoxic and metastatic proteins. The analysis of shortest paths revealed several ways to spread metastasis and angiogenesis from hypoxic proteins. Lastly, we identified 23 drugs enrolled in clinical trials focused on metastatic disease treatment. Six of them were involved in advanced-stage clinical trials: aflibercept, bevacizumab, cetuximab, erlotinib, ipatasertib, and panitumumab. These highly aerobic cells undergo fundamental shifts in gene expression after sensing changes in the oxygen (O2) levels of the microenvironment3. These gene expressions are often triggered by metabolic demands, acclimatization or adaptations, causing tissue remodeling, increased cell metabolism, and improved physiological preparedness4. In the 90’s, the Hypoxia Inducible Factor (HIF) was identified, purified and cloned. HIF is a transcription factor that regulates oxygen-dependent responses and consists of two protein subunits: HIF-1α and ARNT6. HIF-1α is involved in the protective response towards hypoxia, but also has an important role in oxygen homeostasis, the response to ischemia, pulmonary hypertension, preeclampsia, intrauterine growth retardation, and cancer8. In 1995, the first full-length clone of the von Hippel-Lindau (VHL) tumor suppressor gene was constructed12. Subsequently, it was demonstrated in 1999 that VHL targets HIF-1α for oxygen-dependent proteolysis by a proteasomal mechanism14.Human cells are highly aerobic and their metabolism rely widely on oxygen supply, matching metabolic demands at the tissue, cellular, and mitochondrial level2 is not only a key nutrient required for aerobic metabolism to maintain intracellular bioenergetics, but it also works as a substrate for several organic and inorganic reactions. Hypoxia occurs in a variety of physiological, as well as, pathological conditions15. Half of all solid tumors are characterized by the dynamic gradients of O2 distribution and consumption. As result, the tumor environment consist of hypoxic subregions due to changes in tumor metabolism that increase O2 demand or insufficient tumor vasculature that decreases O2 supply22. Tumor adaptation to this imbalance between O2 demand and supply is associated with increased genomic instability, poor clinical prognosis23, development of tumor stem cell-protective niches25, resistance to radio- and chemotherapy27, and increased proclivity for distal metastases30.Molecular O32. In fact, the persistence and lethal relapse of disseminated cancers are driven by stem-like cells that show the ability to regenerate tumors in distal sites36. Due to treatment pressure by therapeutic resistance, tumor evolution triggers mitochondrial dysfunction and genomic alterations differing substantially between metastatic tumors and primary tumors37. Most cancers can be considered curable if the diagnosis occurs before cells are able to spread to secondary tissues; otherwise, cancer is often called incurable as available treatments are not able to prevent metastatic colonization40. According to Welch and Hurst32, the four hallmarks of metastasis are motility and invasion, modulation of the microenvironment, plasticity, and colonization. HIF signaling participates in the metastatic cascade by mediating invasion and migration, intravasation and extravasation, establishment of the premetastatic niche, as well as survival and growth at the distant site42.Metastasis is thought to be the end point of neoplastic cell transformation towards autonomous regulation due to genetic mutations that mediate cell proliferation and invasion44, an integrated multi-omics analysis between hypoxia and metastasis signaling has not been previously carried out. To shed light on these mechanisms, we evaluated genomic and transcriptomic alterations of 233 hypoxia-related genes (HRGs) and Buffa hypoxia score (HS) per tumor stage and metastatic status across 17 pancarcinomas taken from the PanCancer Atlas (PCA) project from The Cancer Genome Atlas (TCGA) consortium. Additionally, we generated a protein–protein interactome network and tracked the shortest paths from hypoxic proteins to metastatic and angiogenic phenotypes to reveal potential therapeutic targets and drugs for the metastatic disease.Although hypoxia is an adverse and targetable prognostic feature in multiple cancer types54. Figure 56. The PCA-TCGA types were breast invasive carcinoma (BRCA) with 991 (15.6%) individuals, colorectal adenocarcinoma (CRC) with 521 (8.2%) individuals, lung adenocarcinoma (LUAD) with 501 (7.9%) individuals, prostate adenocarcinoma (PRAD) with 481 (7.6%) individuals, thyroid carcinoma (THCA) with 478 (7.5%) individuals, lung squamous cell carcinoma (LUSC) with 464 (7.3%) individuals, head and neck squamous cell carcinoma (HNSC) with 431 (6.8%) individuals, stomach adenocarcinoma (STAD) with 399 (6.3%) individuals, bladder urothelial carcinoma (BLCA) with 369 (5.8%) individuals, kidney renal clear cell carcinoma (KIRC) with 352 (5.5%) individuals, liver hepatocellular carcinoma (LIHC) with 345 (5.4%) individuals, skin cutaneous melanoma (SKCM) with 258 (4.1%) individuals, cervical squamous cell carcinoma and endocervical carcinoma (CESC) with 217 (3.4%) individuals, pancreatic adenocarcinoma (PAAD) with 166 (2.6%) individuals, esophageal carcinoma (ESCA) with 163 (2.6%) individuals, testicular germ cell tumors (TGCT) with 127 (2.0%) individuals, and mesothelioma (MESO) with 80 (1.3%) individuals57.We analyzed genomic and transcriptomic alterations of 6343 individuals with 17 pancarcinomas taken from the PCA project from TCGA consortiumCOPS5 (0.204), ELOC (0.175), and ARNT (0.137); mRNA downregulation in SMAD4 (0.081), MAPK1 (0.044), and TP53 (0.034); CNV amplifications in PIK3CA (0.064), NDRG1 (0.059), and TFRC (0.054); CNV deep deletions in STC1 (0.035), ANGPT2 (0.034), and SMAD4 (0.021); putative driver mutations in TP53 (0.391), PIK3CA (0.134), and SMAD4 (0.029); fusion genes in ERBB2 (0.006), PRKCA (0.004), and TP53 (0.002); and all genomic alterations in TP53 (0.476), PIK3CA (0.341), and COPS5 (0.246)  across different types of alterations  Fig. . AdditioCOPS5 0.04, ELOC P < 0.001) by applying the Bonferroni correction test for multiple comparisons. For instance, ESCA, BRCA, and LUSC were significantly altered in comparison to TGTC, PRAD, MESO, KIRC, and THCA. Lastly, carcinomas with the highest mean frequency per alteration were ESCA (0.056), LUSC (0.053), and MESO (0.052) with mRNA upregulation; PRAD (0.006), CRC (0.006), and LIHC (0.005) with mRNA downregulation; ESCA (0.019), BRCA (0.018), and BLCA (0.016) with CNV amplification; KIRC (0.007), ESCA (0.006), and BLCA (0.005) with CNV deep deletion; CRC (0.005), ESCA (0.005), and MESO (0.005) with putative driver mutations; and BRCA (0.0007), LUAD (0.0006), and KIRC (0.0005) with fusion genes  between tumor stages. Figure U test showed that differences in alterations between M0 and M1 were statistically significant (P < 0.001).Figure 58. That is, higher mRNA abundance signatures indicate higher levels of hypoxia. Therefore, we quantified tumor hypoxia levels of 5,249 individuals with 13 pancarcinomas using the mentioned score58. Figure P < 0.001) levels of hypoxia score across 13 pancarcinomas  by applying the Bonferroni correction test for multiple comparisons. Figure P < 0.001) of HS across tumor stages  by applying the Bonferroni correction test for multiple comparisons. Finally, Fig. P < 0.001) of HS between M0 and M1 through the Bonferroni correction test.Buffa et al. used information from in vitro experiments combined with in vivo co-expression patterns regarding hypoxia-regulated genes and pathways in order to construct the Buffa hypoxia score60. Metastatic driver proteins were taken from the Human Cancer Metastasis Database (HCMDB), an integrated database designed to analyze large scale expression data of cancer metastasis61. Subsequently, we generated the interactome network encompassing 108 nodes and 603 high-confidence interactions (cutoff = 0.9)  Fig. . The mea.9) Fig.  and S25.et al62. On the one hand, we found that 99 (42%) proteins had paths to metastasis, of which, 49 proteins had positive regulation with an average distance of 3.0 and an average path length of 4.0, 16 proteins had negative regulation with an average distance of 3.5 and an average path length of 4.6, and 34 proteins with unknown regulation status had an average distance of 3.2 and an average path length of 4.3. The top ten hypoxia-related proteins with the shortest distance to metastasis were BACH1 (0.8), AKT2 (1.6), AKT1 (1.6), CAMK2B (1.7), EGFR (1.7), MAPK1 (1.7), MAPK3 (1.7), PRKCA (1.7), PRKCB (1.7), and EGF (1.8). The shortest paths from the 99 hypoxia-related proteins to the metastatic phenotype are fully detailed in Fig. 63. In our study we found 106 (45%) hypoxic-related proteins with shortest paths to angiogenesis, of which, 73 had positive regulation with an average distance of 3.5 and an average path length of 4.4, 20 proteins had negative regulation with an average distance of 3.9 and an average path length of 4.4, and 13 proteins with unknown regulation status had an average distance of 3.7 and an average path length of 4.7. The top ten hypoxia-related proteins with the shortest distance to angiogenesis were TGFA (0.9), TGFB1 (0.9), TGFB2 (0.9), TGFB3 (0.9), THBS1 (0.9), TIMP1 (0.9), TNF (0.9), VEGFC (0.9), VEGFA (1.2), and MMP2 (1.7)  Table .Figure 6Figure 64. Figure https://cancer.sanger.ac.uk/census)65. As results, invasion and metastasis were promoted by EGFR, AKT1, MTOR, NOTCH1, ERBB2, MAPK1, and CDH1; escape from programmed cell death was encouraged by AKT1, EGFR, MTOR, NOTCH1, ERBB2, PIK3CA, and MAPK1; changes in cellular energetics were stimulated by TP53, AKT1, EGFR, MTOR, NOTCH1, ERBB2, ARNT, MAPK1, and CDH1; angiogenesis was endorsed by AKT1, EGFR, MTOR, NOTCH1, PIK3CA, and ARNT; proliferative signaling was supported by AKT1, EGFR, MTOR, NOTCH1, ERBB2, and PIK3CA; suppression of growth was promoted by TP53, AKT1, and EP300; escape from immune response during cancer was encouraged by EGFR; lastly, cell replication towards immortality was stimulated by TP53 and NOTCH1.The hallmarks of cancer constitute an organizing principle for rationalizing the complexities of neoplastic disease. Nowadays, there are 14 biological capabilities acquired during the multistep development of human tumors68. The most significant GO biological processes with Benjamini–Hochberg correction and false discovery rate (FDR) < 0.001 were cellular response to hypoxia and positive regulation of cell migration. The most significant KEGG signaling pathways with Benjamini–Hochberg correction and FDR < 0.001 were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. Lastly, the most significant Reactome signaling pathways with Benjamini–Hochberg correction and FDR < 0.001 were PIP3 activates AKT signaling, PI3K/AKT signaling in cancer, signaling by ERBB2, and MTOR signaling  biological processes, 99 significant the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways, and 65 significant Reactome signaling pathwaysing Fig. .69. Subsequently, we performed an overall survival analysis comparing patients  with genomic alterations in the 30 essential hypoxia-related genes versus unaltered patients (n = 7213). As result, the altered group had a median overall survival  of 35.42 (34.86–36.24) months, and the unaltered group had a median overall survival of 57.04 (53.59–60.68) months. The log rank test showed a statistically significant P-value < 0.001 of overall survival (months) between patients with genomic alterations in the 30 essential HRGs and unaltered patients  is an integrated pan-cancer cohort of tumor genomic and clinical outcome data from 25,755 patients with patterns of metastatic dissemination across 50 tumor typesnts n = 1,446 withP < 0.001; Q < 0.001) versus the percentage of unaltered samples. Lastly, data can be accessed through the cBioPortal 69.On the other hand, Fig. 70. There are 23 drugs targeting 10 hypoxic/metastatic proteins that have been analyzed in 211 clinical trials. The cancer types with clinical trials focused on metastasis were prostate cancer, colorectal cancer, and melanoma  genomic alteration since hypoxic tumors overexpress intracellular signals to adapt to the environment77. Overall, the first approach revealed that 73 HRGs presented frequencies of alteration higher than the average (> 0.068). It is important to mention that the 233 hypoxia-related genes were collected from gene ontology terms, signaling pathways, the Buffa signature, and publications on high-altitude adaptive phenotypes. Nevertheless, a limitation of this study is that we did not perform a manual search and curation of publications on individual genes potentially related to hypoxia in order to enrich our initial gene set.The first approach consisted in the analysis of 100,643 genomic/transcriptomic alterations (233 HRGs) belonging to 6343 individuals with 17 pancarcinomas. ESCA, BRCA and LUSC were the most altered cancer types, whereas MESO, KIRC, and THCA were the least altered. The mRNA upregulation was the most significant , meaning that the number of genomic alterations in the HRGs increases as tumor stage and metastatic status evolves. Smith et al.have proposed this statement through a genetic model in which sequential accumulation of mutations in specific genes  drives the transition from healthy epithelia to metastatic colorectal cancer78.Regarding to genomic/transcriptomic alterations per tumor stage across 17 pancarcinomas, T4 stage presented the highest mean frequencies of mRNA upregulation (0.050) and putative driver mutations (0.004). On the other hand, the mean frequency of genomic alterations in HRGs was significant in patients with metastasis (0.072) in comparison to patients without it (0.069)  with significant differences  between T1 vs T4, T2 vs T4, and T3 vs T4. Regarding to metastatic status, the Buffa HS mean in patients with metastasis (8.3) was significantly higher  than in patients without it (2.2).In order to validate the significant differences in alterations of HRGs found in tumor stages and metastatic status, we analyzed the Buffa hypoxia score across 13 pancarcinomaset al82, protein interactions with therapeutic significance can be revealed by the integration of cancer proteins into networks. Protein interactions regulate essential signals such as proliferation or metastasis, and thus, represent potential targets for drug development and drug discovery. Regarding our networking analysis, we generated an interactome network encompassing 108 nodes and 603 high-confidence edges (cutoff = 0.9) with a mean degree centrality of 11.2. Consequently, the second approach revealed 108 highly connected hypoxic and metastatic proteins.The second approach was addressed towards a protein–protein interactome network between hypoxia-related proteins and metastatic driver proteins. In accordance with Li et al., Ivanov et al., and Wurth 62. In this context, the third approach was aimed at revealing the shortest paths from HRPs to metastasis83. Therefore, we found that 99 hypoxic proteins had paths leading to metastasis from which 49 had positive regulation and 16 had negative regulation. Additionally, the third approach was complemented with the analysis of angiogenic paths because the recruitment of new blood vessels is an essential component of the metastatic pathway63. Therefore, we identified 106 hypoxic proteins with shortest paths to the angiogenesis hallmark, of which, 73 had positive regulation and 20 had negative regulation. Then, the compendium of the 112 HRPs with the shortest paths to metastatic and angiogenic phenotypes made up the third approach.According to Ianuccelli et al., the value of the third approach permits to bridge the gap between genomic data and cancer phenotypes. However, it should be kept in mind that biology is more complex than graph theory. One important limitation of CancerGenNet is the finding that for up to 20% of the cancer genes we know little about the molecular mechanisms underlying their tumorigenic functionAn integrative analysis of the most relevant proteins per approach revealed the 30 essential proteins involved in the signaling crossroads between hypoxia and metastasis: AKT1, AKT2, AKT3, ANGPT1, ANGPT2, ARNT, CDH1, CDKN1B, E2F1, EGFR, EP300, ERBB2, IGF1R, LMNA, MAPK1, MAPK3, MDM2, MTOR, NOTCH1, PIK3CA, PRKAA1, PRKCA, RELA, RPS6KB1, SHC1, SMAD4, TGFB2, TP53, TSC2, and VEGFA.64 and the COSMIC-CGC database65, AKT1, EGFR, ERBB2, MTOR, NOTCH1, and PIK3CA promote the proliferative signaling; AKT1, EP300, and TP53 trigger suppression of growth; EGFR stimulates the escaping immune response to cancer; MTOR and TP53 encourage cell replicative immortality, AKT1, ARNT, EGFR, MTOR, NOTCH1, and PIK3CA promote angiogenesis; AKT1, EGFR, ERBB2, MAPK1, MTOR, NOTCH1, PIK3CA, and TP53 trigger the escaping programmed cell death; and AKT1, ARNT, CDH1, EGFR, ERBB2, MAPK1, MTOR, NOTCH1, and TP53 stimulate the change of cellular energetics 84.Subsequently, the functional enrichment analysis of these 30 essential proteins revealed that the most significant GO biological processes (Benjamini–Hochberg correction FDR < 0.001) related to hypoxia were cellular response to hypoxia and regulation of transcription in response to hypoxia; and related to metastasis were regulation of cell migration and blood vessel endothelial cell migration. Additionally, the most significant KEGG pathways (Benjamini–Hochberg correction FDR < 0.001) were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF signaling pathways. On the other hand, it is important to mention that several of these proteins not only play a main role in metastasis but also promote other cancer phenotypes. For instance, according to the hallmarks of cancer studyics Fig. B. Lastly70. In this study we identified 23 drugs targeting 10 hypoxic/metastatic proteins that have been analyzed in 211 clinical trials. Six of them  were involved in phases III/IV clinical trials. Ipatasertib, erlotinib, lapatinib, neratinib, afatinib, sapitinib, gefitinib, tucatinib, linsitinib, and buparlisib are small molecules; aflibercept is a recombinant protein; and cetuximab, bevacizumab, panitumumab, futuximab, necitumumab, trastuzumab emtansine, pertuzumab, ganitumab, dalotuzumab, brontictuzumab, and MEDI-3617 are monoclonal antibodies. Lastly, the mechanism of action of these drugs is fully detailed in Table Regarding clinical trials reported on the hypoxic/metastatic essential proteins, the Open Targets Platform is an available resource for the integration of proteogenomics and chemical data to aid systematic drug target prioritization85. Physical health is not the only affected aspect, but millions of people who experience the economic, emotional, and social impacts of the disease and the increasing economic cost to the health systems. Specifically for low-and-middle income countries, there is a gap in the access to health, including a timely quality diagnosis and treatment. Additionally, several studies have highlighted the need to diversify oncological studies to populations representing several ethnic groups along with the development of novel strategies to enhance race/ethnicity data recording and reporting90. In this sense, multi-omics technologies and public–private investment related to identifying therapeutic targets improving metastatic disease treatments are crucial to reduce inequalities in health and strengthen mechanisms that can improve survival rates of different types of cancer. Relevantly, the effective and ethical use of these technologies would contribute to the development of knowledge and further explanations around the cause of diseases, with the ultimate aim of reducing morbidity and mortality; thus, increasing wellbeing. National health systems should work towards the identification of opportunity costs related to multi-omics research investment and its potential benefits on clinical applications to help diagnose and/or prevent certain diseases. As also stated by Hassin et al., multi-omics approach offers the opportunity to understand the flow of information that underlies disease91. The success of the omics approach has to be addressed not only in a technical and/or financial factors-assessment but on the importance of developing conceptual research shifts; focused also on the relations between biological factors and the interrelations with the social determinants of health.From a public health perspective, it is fundamental to recognize the holistic view that multi-omics provide to the understanding of cancer; for example, providing accurate selection of patients for the assessment of specific therapies. As reported by Atun et al., the cancer burden continues to grow globally, projecting to have around 24.6 million cases by 2030In conclusion, hypoxia and HIF-dependent signaling play an important role in metastasis tumor progression. The hypoxic tumor microenvironment influences both the early and late stages of metastasis. Our findings suggest that individuals with metastasis present higher frequencies of genomic/transcriptomic alterations and Buffa hypoxia score across pancarcinomas from PCA-TCGA. The most altered signaling pathways were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. Finally, since cancer is a group of complex and heterogeneous diseases, the study of multi-omics approaches is an effective way to understand the molecular landscape behind hypoxia and metastasis that revealed 30 potential therapeutic targets and 23 drugs to improve metastatic disease treatments. These drugs can be considered for treating metastasis after being thoroughly evaluated in specific clinical trials.92; the GO terms: response to hypoxia (GO:0001666), cellular response to hypoxia (GO:0071456), and cellular response to decreased oxygen levels (GO:0036293) (n = 102)66; the hypoxic genes according to Buffa et al.(n = 52)58; and the high-altitude adaptive phenotypes in populations from the Andean Altiplano, Semien Plateau and the Tibetan Plateau according to Bigham (n = 75)93.We analyzed a set of 233 HRGs encompassed by the KEGG HIF-1 signaling pathway (n = 109)https://portal.gdc.cancer.gov/) and the cBioPortal 56. Lastly, the Mann–Whitney U test and the Bonferroni correction test for multiple comparisons were performed to determine significant differences (P < 0.001) between genomic/transcriptomic alterations, clinical data, and PCA-TCGA type.Genomic and transcriptomic alterations  were analyzed in 6343 individuals  from 17 pancarcinomas from PCA-TCGA . Omics and clinical data related to tumor stage (T1-T4) and metastatic status (M0-M1) were taken from the Genomics Data Commons of the National Cancer Institute  of 52 hypoxic genes reported by Buffa et al79. Data related to Buffa hypoxia score, tumor stage, and metastatic status were taken from the Genomics Data Commons and the cBioPortal56. Lastly, the Bonferroni correction test for multiple comparisons was performed to determine significant differences (P < 0.001) between hypoxia score and clinical data.The Buffa hypoxia score was analyzed in 5249 tumors from 13 pancarcinomas: HNSC, LUSC, CESC, CRC, BLCA, SKCM, KIRC, LUAD, PAAD, BRCA, LIHC, PRAD, and THCA94, which imports protein–protein interaction data from the String database97. The PPi network was encompassed by hypoxia-related proteins and metastatic driver proteins, which were taken from the Human Cancer Metastasis Database (https://hcmdb.i-sanger.com/). HCMDB is an integrated database designed to analyze large scale expression data of cancer metastasis61. The degree centrality of a node represents the number of edges the node has to other nodes in the network and it was calculated using the CytoNCA app99. All nodes and edges were organized through the organic layout and visualized through the Cytoscape software v.3.7.160. Lastly, the hypoxia-related proteins involved in metastasis signaling were differentiated by colors according to the degree centrality.The PPi network with high-confidence interactions (cutoff = 0.9) and zero node addition was created using the human proteome of Cytoscape StringApphttps://signor.uniroma2.it/CancerGeneNet/) is a resource that links proteins that are frequently mutated in all cancer types to cancer phenotypes. This resource is based on the annotation of experimental information that allows to embed the cancer proteins into the cell network of causal protein relationships curated in SIGNOR100. Therefore, this bioinformatics tool allows to infer likely paths of causal interactions linking cancer associated proteins to cancer phenotypes such as metastasis and angiogenesis101. Iannuccelli et al.explained that the shortest paths from a specific protein to cancer phenotypes was programmatically implemented using the shortest path function of igraph R package, obtaining a distance score and a path length score102. Hence, we analyzed the shortest paths from our hypoxic proteins to metastatic and angiogenic phenotypes to better understand the association to these hallmarks of cancer64.CancerGeneNet  to obtain significant annotations (Benjamini–Hochberg FDR < 0.001) related to GO biological processes, KEGG signaling pathways, and Reactome signaling pathways92. The functional enrichment analysis was performed using the Human Cancer Metastasis proteins as background set, and annotations were visualized through Manhattan plots. Lastly, significant terms related to hypoxia, metastasis, and oncogenic signaling pathways were manually curated.The enrichment analysis gives scientists curated interpretation of protein sets from omics-scale experimentshttps://www.targetvalidation.org) is comprehensive and robust data integration for access to and visualization of potential drug targets associated with several cancer types and metastasis. Additionally, this platform shows all drugs in clinical trials associated with hypoxic/metastatic proteins, detailing its phase, type of drug, action type, and target class70.The Open Targets Platform (Supplementary Information.


Contingent upon concentration, ROS influence cancer development in contradictory ways, either stimulating cancer survival and growth or causing cell death. Cells developed evolutionarily conserved programs to sense and adapt redox the fluctuations to regulate ROS as either signaling molecules or toxic insults. The transcription factor nuclear factor erythroid 2-related factor 2 (NRF2)-KEAP1 system is the master regulator of cellular redox and metabolic homeostasis. NRF2 has Janus-like roles in carcinogenesis and cancer development. Short-term NRF2 activation suppresses tissue injury, inflammation, and cancer initiation. However, cancer cells often exhibit constitutive NRF2 activation due to genetic mutations or oncogenic signaling, conferring advantages for cancer cells’ survival and growth. Emerging evidence suggests that NRF2 hyperactivation, as an adaptive cancer phenotype under stressful tumor environments, regulates all hallmarks of cancer. In this review, we summarized the source of ROS, regulation of ROS signaling, and cellular sensors for ROS and oxygen (O NOX fam2O2 spontaneously, particularly at low pH or catalyzed by superoxide dismutase (SOD) 1, SOD2, and SOD3, which are located in the cytoplasm, mitochondrial matrix, and extracellular space, respectively . Gl. Gl2O2 sm (GSSG) . Glutathm (GSSG) . In addim (GSSG) ,25. A him (GSSG) . PON2 ham (GSSG) ,28.•OH is the most reactive and short-lived, and it causes damage at or near the site of formation. H2O2 is a relatively weak but more stable oxidant, which can travel across the membranes by facilitating transport via channels, such as aquaporins, or by passive diffusion throughout the cells and cellular organelles ,4323,43]2 as reaction substrates, HO-1 and HO-2 both catalyze the oxidative cleavage of heme at the alpha-methene bridge carbon, released as ferrous iron, biliverdin, and carbon monoxide (CO), a heme ligand. CO is considered a signaling molecule, and biliverdin can be further reduced to potent antioxidant bilirubin catalyzed by biliverdin reductase (BVR) at the expense of NADPH. HO-1 is sensitive to all kinds of stimuli that cause oxidative stress and pathological conditions, including hyperoxia, hypoxia, GSH depletion, heat shock, ischemia, radiation, metal ions, cellular transformations, and disease states. Expression HO-1 is transcriptionally regulated by NRF2 . Un. Un2 senof HIF-α . Under htivation . Both PH2O2 through direct oxidation of p53 cysteine residues and indirect modulation of signaling networks . Gl. Gl2 supe uptake ,85. xCT e uptake . NRF2 ree uptake ,85. NRF2e uptake . TRX ande uptake . By decr disease . Comparete smoke . In addite smoke . Consider cancer ,92, color cancer , breast r cancer , prostatr cancer , and blar cancer . TherefoKEAP1, the third most commonly mutated gene behind the tumor-suppressor TP53 (46%) and KRAS (32%) oncogene [NRF2-activating mutations and loss of function mutations in KEAP1 and Cul3 that prevent effective NRF2 repression frequently occur in many cancers, such as liver cancer , lung caoncogene ,103. Tho+/H+ exchanger 1 (NHE1), CDC42, and PIK3CG that induces an alternative route for tumors to scavenge nutrients from extracellular sources [NRF2 regulates autophagy, cancer metabolism, and macropinocytosis to support the nutrient demands for the rapid growth of cancer cells. Autophagy is a vital process in which the body’s cells “clean out” any unnecessary or damaged components to allow intracellular nutrient recycling, especially under starvation conditions. Due to limited nutrient supply, cancer cells often elevate autophagy and depend on autophagy-mediated scavenging and recycling of intracellular macromolecules to maintain survival and growth ,104. NRF sources . In panc sources . TherefoUPR is the mechanism by which cells control endoplasmic reticulum (ER) protein synthesis, folding, modification, and transport of secretory and organelle-bound proteins, as well as their degradation. ER stress during cancer growth, stress-rich microenvironments, such as low pH, hypoxia, nutrition deprivation, and metabolic stress, can induce ROS formation and accumulation of misfolded proteins that lead to ER stress and UPR via activation of three signaling arms coordinated by IRE1-XBP1, PERK-eIF2a-ATF4, and ATF6. During metastasis, metastasizing cancer cells have to be able to survive from migration through the stroma, intravasation through the endothelium into the blood or lymphatic vessels, circulation in the vessels, and subsequently extravasation through the endothelium, and colonization at distant tissues. All of these processes exhibit various levels of mechanical forces, including fluid shear stress, hydrostatic pressure, and tension and compression forces, which trigger stress responses. UPR is upregulated in cancers and UPR coordinates with NRF2 to sustain cancer survival, proliferation, and metastasis . ER streER stress and UPR share an intimate connection with proteostasis. NRF2 regulates the activity of the proteasome. NRF2 regulates the expression of the 20S proteasome subunits PSMA1, PSMB3, and PSMB6 and 19S proteasome subunits PSMC1, PSMC3, and PSMD14, as well as a proteasome chaperone-proteasome maturation protein (POMP) . Not allCRY2 gene, resulting in the repression of CLOCK/BMAL1-regulated E-box transcription and alteration of circadian rhythms [Nfe2l2-deficient mouse fibroblasts, hepatocytes and liver also altered rhythmicity. These data support that NRF2 links metabolism signals to the ticking of the circadian clock. In addition, NRF2 activation at a circadian time corresponding to the peak generation of endogenous oxidative signals resulted in NRF2-dependent reinforcement of circadian amplitude, suggesting that NRF2 amount and/or timing of expression are important to timekeeping in cells. Furthermore, NRF2 itself is also transcriptionally upregulated by BAML1 [The circadian rhythm is a natural, internal process that regulates the sleep–wake cycle and other human activities in a manner of roughly every 24-h period of light and dark on earth. It is driven by the circadian clock, an evolutionarily conserved timekeeping system for numerous biological rhythms that allow organisms to anticipate and adapt their behavior and physiology to predictable changes in their environment . Numerou rhythms . Nfe2l2-by BAML1 ,121. Theby BAML1 . Therefo2 is one of the defining moments in evolution. Oxidation and reduction processes participate in almost all aspects of life, such as mitochondria oxidative phosphorylation, which is the major source of ROS production. Environmental stress is ubiquitous for all living beings and threatens to disrupt cell functions, causing oxidative stress and ROS. ROS can function as both physiological signaling molecules required for numerous cellular processes. However, abnormal levels of ROS promote disease pathogenesis. Timely induction of oxidative stress response and resolution of stress facilitates the tissue repair and wound healing process and ultimately restores a stress-free state or adapts to the stressed state. The amount and duration of ROS could determine the outcome. HIFs, the HO system, the NRF2-KEAP1 system, and several other transcription factors are the common ROS and O2 sensing and response pathways. NRF2, as a master regulator of the oxidative stress response, sits at the center of a complex regulatory network that contributes to the initiation and development of many diseases, particularly cancer. The extent and duration of NRF2 activation determine the beneficial or deleterious effects of NRF2. Generally speaking, NRF2 activation executes a protective role under physiological conditions, but it promotes cancer development, metastasis, and anticancer drug resistance after cancer is established. Recent studies uncovered new roles of NRF2 activation in sustaining cancer cell growth and maintaining the drug-tolerant state, including regulation of autophagy, UPR, micropinocytosis, and metabolic reprogramming. Cancer remains an elusive, highly complex, and deadly disease. More NRF2 functions and the crosstalk of NRF2 and other oncogenic pathways in cancer will be uncovered in the future. Timely activation of NRF2 can be a promising pharmacological target to prevent and treat cancer. Selective NRF2 modulators can be used as adjuvant therapy after conventional chemotherapy, targeted therapy, and immunotherapy.O


Manihot esculenta Crantz) at the genome-wide level. The phylogenetics, exon-intron structure, motif organization, and conserved domains of these genes were analyzed to characterize the members of the JAZ, PPD, and ZML subfamilies. Chromosome location and synteny analyses revealed that 26 JAZ, PPD, and ZML genes were irregularly distributed across 14 of the 18 chromosomes, and 18 gene pairs were implicated in large-scale interchromosomal segmental duplication events. In addition, JAZ, PPD, and ZML gene synteny comparisons between cassava and three other plant species  uncovered vital information about their likely evolution. The prediction of protein interaction network and cis-acting elements reveal the function of JAZ, PPD, and ZML genes. Subsequently, expression patterns of JAZ, PPD, and ZML genes were validated by qRT-PCR as being expressed in response to osmotic, salt, and cadmium stress. Moreover, almost all JAZ subfamily genes were responsive to jasmonic acid (JA) treatment. In particular, MeJAZ1, MeJAZ13, and MeJAZ14, were highly up-regulated by three treatments, and these genes may deserve further study. This comprehensive study lays the groundwork for future research into TIFY family genes in cassava and may be valuable for genetic improvement of cassava and other related species.Plant-specific TIFY [TIF(F/Y)XG] proteins serve important roles in the regulation of plant stress responses. This family encodes four subfamilies of proteins, JAZ (JASMONATE ZIM-domain), PPD (PEAPOD), ZML (Zinc-finger Inflorescence-like), and TIFY. In this work, a total of 16 JAZ, 3 PPD, 7 ZML, and 2 TIFY genes were found in cassava ( Arabidopsis thaliana (Oryza sativa) (Zea mays) (Vitis vinifera) (Populus trichocarpa (Tricicum aestivum) (Brassica rapa (TIFY [TIF(F/Y)XG] gene family is a transcription factor family that is thought to play a key role in a range of biological activities, including plant defense and growth regulation . TIFY fathaliana , 20 in r sativa) , 27 in mea mays) , 19 in ginifera) , 24 in Pchocarpa , 49 in westivum) , and 36 ica rapa .COI1), which subsequently stimulates JAZs degradation by the 26S proteasome, causing the activation of JA-responsive genes for defense to occur  signaling pathway. When plants respond to developmental or environmental cues, JA-coupled isoleucine (JA-ile) is identified by F-box CORONATINE INSENSITIVE 1  transcriptional factors, reducing cotton’s resistance to Verticillium dahlia and insect herbivory , a woody Euphorbiaceae shrub, is recognized as the sixth most important food and economic crop in Africa, Asia, Latin America, and the Caribbean . Our research provides critical information that will aid in the future characterization of TIFY genes in cassava.Owing to its edible starch-rich storage root, cassava  genome sequences and annotations were obtained from EnsemblPlants (http://plants.ensembl.org/index.html). TIFY family members in the cassava genome were discovered using two separate methods. First, 18 Arabidopsis , Jas (PF09425), and CCT motifs (PF06203) HMM profiles were obtained from Pfam (http://pfam.xfam.org/) and used to screen proteins in HMM3.3.1 . (pI (isoelectric point) were predicted using ExPASy (http://au.expasy.org/tools/pitool.html) (http://www.prabi.fr/). The gene ontology (GO) of cassava TIFY genes was analyzed using omicshare online tools (https://www.omicshare.com/tools/Home/Soft/gogsea).Cassava (bidopsis  and 20 rbidopsis  amino ace 0.01). . Thereafol.html) . The secArabidopsis, rice, Populus trichocarpa, Brassica napus, Gossypium arboretum, Vitis vinifera, and Brachypodium distachy  analysis of conserved motifs, the number of conserved motifs was set to 10. InterProScan (http://www.ebi.ac.uk/Tools/pfa/iprscan/) was used to annotate the discovered motifs. An integrated graphic of the phylogenetic tree, conserved motif, and conserved domain was constructed using the TBtools software  acquired from the Phytozome database (https://phytozome-next.jgi.doe.gov) were evaluated in gene structure display server http://gsds.gao-lab.org/ , synonymous (Ks), and Ka/Ks of each duplicated gene pair and the syntenic TIFY gene pairs; Ka/Ks < 1 indicated purifying selection, Ka/Ks = 1 showed neutral selection, and Ka/Ks > 1 indicated positive selection  (The TBtools program was used to extract the 2000 bp upstream sequences of the ex.html) .Arabidopsis interologues were used to predict the protein-protein interaction network to examine the relationship of cassava JAZ, PPD, and ZML proteins further. The functional protein association network was analyzed using the STRING online service (https://cn.string-db.org/) with a confidence parameter of 0.15  were collected by the National Center for Biotechnology Information (NCBI) , the wild subspecies (W14), and Kasetsart University 50 (KU50), as well as in response to exogenous ABA, were used to evaluate the expression patterns of  TBtools .-1 CdCl2 for 4, 12, and 24 hours (h), respectively, for the osmotic, salt, and cadmium stress treatments. In all cases, untreated seedlings (0 h) acted as controls. Before RNA extraction, the leaf samples were collected, instantaneously frozen in liquid nitrogen, and stored at -80°C.Cassava South China 205 (SC205), a widely farmed cassava cultivar in China, was used in this study. Three-noded stem segments from 8-month-old cassava plants were subcultured for 40 days on Murashige and Skoog medium at 25°C under a 16 h light/8 h dark cycle at Hainan University . Then, uniform seedlings were subjected to exogenous JA and abiotic stress treatments, respectively. Exogenous JA treatments were conducted by spraying leaves with 50 mM methyl jasmonate (MeJA) for 1 and 6 h post treatment. For abiotic stresses, seedlings were treated with MS medium with 30% polyethylene glycol (PEG) 6000, 400 mM NaCl, and 100 mgLJAZ, PPD, and ZML genes were investigated using qRT-PCR. Total RNAs were extracted from all samples using RNAprep Pure Plant Plus Kit  according to the manufacturer’s instructions. Approximately 1 μg of extracted total RNA was used to synthesize cDNA using a reverse transcriptase kit and the manufacturer’s instructions . The first strand of cDNA was created using a reverse transcriptase kit . A 7500 Real-Time PCR System with a total reaction volume of 20 μL was used for real-time PCR, which included 2 μL cDNA template, 1 μL forward primer, 1 μL reverse primer, 10 μL qPCR Master Mix, and 6 μL sterilized ddH2O. The real-time PCR amplification protocol was set at 95°C for 30 seconds (s) for 40 cycles, 95°C for 5 s, 55°C for 30 s, 72°C for 30 s, and 72°C for 10 minutes  as a reference gene. The primers used are listed in The expression patterns of cassava  minutes . The ΔΔCP-value ≤ 0.05 is considered to be statistically significant.The results are expressed as mean ± standard deviation (SD). The Duncan’s multiple range tests were used to analyze the differences between groups. BLAST and HMMER searches revealed 28 putative TIFY family genes in the cassava genome Table 1.MeJAZ1 through MeJAZ16, MePPD1 through MePPD3, MeZML1 through MeZML7, and MeTIFY1 through MeTIFY2. The lengths of the CDS ranged from 396 bp (MeJAZ11) to 1311bp (MeTIFY2). Similarly, the amino acid sequence lengths range from 132 a.a. (MeJAZ11) to 437 a.a. (MeTIFY2), and the expected molecular weights range from 14.95 kDa (MeJAZ11) to 45.47 kDa (MeTIFY2). The fact that the anticipated pI of 21 of 28 cassava TIFYs was more than 7.0 suggests that the vast majority of cassava TIFYs are alkaline. Based on subcellular localization data, the majority of cassava TIFY proteins (23 of 28) were anticipated to be found in the nucleus. Actinidia eriantha and A. chinensis) TIFYs were predicted to be located in the nucleus, which is similar with the present study. The secondary structure analysis discovered that random coil was the most common secondary structure in cassava TIFY amino acid sequences, accounting for up to 73.77% (MeJAZ12), followed by alpha helix, extended strand, and beta turns. The secondary structure of the cassava TIFY protein was made up of 64% random coil, 21% alpha helix, 11% extended strand, and 4% beta twists, respectively , the CCT domain, and the GATA domain . In geneJAZ, PPD, and ZML genes, the cassava plant genome’s pseudochromosome assembly was used to map their chromosomal locations to the duplicated blocks. The 26 JAZ, PPD, and ZML genes were unequally distributed across 14 of cassava’s 18 chromosomes  and a monocot plant (rice) were produced to study the possible evolutionary implications of the cassava TIFY gene family. When cassava was compared to other species, a substantial number of syntenic blocks were discovered. There were 21 and 17 syntenic linkages between cassava TIFY genes and those of Populus trichocarpa and Arabidopsis, followed by rice, which has 6 genes  were found in both dicots and monocots  proteins involved in the TIFY family networks in Arabidopsis  and root (7) tissues of the farmed cultivar Argentina 7 (Arg7), the wild subspecies (W14), and Kasetsart University 50 (KU50) were investigated using ten publicly available transcriptomes  in leaf tissue in Arg7, W14, and KU50; and MeJAZ1, MeJAZ2, MeJAZ8, MeJAZ12, MeJAZ13, MeJAZ14, MeJAZ15, and MeJAZ16 had high expression levels  in early storage root tissue in Arg7 and KU50. These genes showed constitutive expression for a given tissue in both wild subspecies and domesticated cultivars, indicating that they may play important roles in tissue development or function. Notably, MeJAZ12 showed relatively high transcript abundance  in all studied tissues in Arg7, W14, and KU50, indicating that the gene may be involved in plant growth and development  exhibited an elevated expression pattern, with MeJAZ1 having the greatest expression. The expression levels of MeJAZ4, MeJAZ6, MeJAZ7, MeJAZ9, MeJAZ10, MeJAZ12, MePPD2 and MePPD3 were much lower than the control. Interestingly, ten of the twenty-six genes  tended to trend higher and peaked 4 and 12 hours following osmotic stress treatment , and peach (Prunus persica), has members of all four subfamilies based on domain traits   , Populuschocarpa , and kiwchocarpa . Previouchocarpa . AccordiPopulus trichocarpa, and Brassica rapa have 4, 10, and 18 duplicated TIFY genes, respectively  shown parallel expression patterns under salt and cadmium treatment , GmTIFY10e and GmTIFY10g transgenic plants grew faster and had longer root lengths and fresh weights than wild-type plants . It was hypothesized that the cassava TIFY gene family may contribute to resistance via the JA signaling system. ABA is important in a plant’s ability to adapt to harsh environmental circumstances , Natural Science Foundation of China (32160324), and the China Agriculture Research System (CARS-11-hncyh).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.


Streptococcus iniae strain AH1, which was isolated from moribund farmed hybrid tilapia (Oreochromis niloticus × Oreochromis aureus) in Saudi Arabia. The genome is composed of a single linear chromosome of 2,068,661 bp, with a G+C content of 36.8%.Here, we describe the whole-genome sequence of  Streptococcus iniae is a hemolytic, non-Lancefield-classified Gram-positive pathogenic bacterium that was isolated originally from a subcutaneous abscess of a captive Amazon freshwater dolphin, Inia geoffrensis, in 1976  in March 1992 in Saudi Arabia as described previously , at 30°C. Genomic DNA was extracted using the DNeasy blood and tissue kit (Qiagen), following the manufacturer’s instructions. DNA quantity and quality were checked with a NanoDrop spectrophotometer (Thermo Fisher Scientific) and gel electrophoresis, respectively. The whole genome was sequenced using a combination of the PacBio Sequel II platform  and the NovaSeq 6000 platform (150-bp paired-end reads) . For PacBio sequencing, genomic DNA was sheared using the g-TUBEs  and then size selected to greater than 10 kb using BluePippin, end repaired and ligated with universal hairpin adapters using the SMRTbell template prepare kit v1.0 (Pacific Biosciences) according to the manufacturer’s instructions, and sequenced using PacBio P6-C4 chemistry and 240-min movies. The Illumina sequencing library was prepared using the Nextera XT library preparation kit (Illumina) following the manufacturer’s instructions.A single colony of N50 value of 14,277 bp, totaling 2.33 Gbp. De novo assembly was performed using Flye v2.8 , 68 tRNAs, and 18 rRNAs , 68 tRNAs, and 18 rRNAs, which is consistent with the other ae 89353  and S. iae YM011 .S. iniae strain AH1, reported in the manuscript, has been deposited at DDBJ/ENA/GenBank under the accession number JAHZUZ000000000, BioProject accession number PRJNA742166, and BioSample accession number SAMN19929365. The version described in this paper is the first version, JAHZUZ010000000. The raw Illumina and PacBio sequence reads were deposited in the Sequence Read Archive (SRA) under accession number SRR17134084 and SRR17157250, respectively.The whole-genome sequence of


EZH2 is associated with HCC progression. However, the molecular mechanism by which non-coding RNAs (ncRNAs) regulate EZH2 remains elusive. Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data were used to perform differential expression analysis and prognostic analysis. We used the Encyclopedia of RNA Interactomes (ENCORI) database to predict candidate miRNAs and lncRNAs that may bind to EZH2. Subsequently, the comprehensive analysis  identified ncRNAs that contribute to EZH2 overexpression. Results: EZH2 was found to be upregulated in the majority of tumor types and associated with a poor prognosis. Hsa-miR-101-3p was identified as a target miRNA of EZH2. Additionally, SNHG6 and MALAT1 were identified as upstream lncRNAs of hsa-miR-101-3p. Meanwhile, correlation analysis revealed that EZH2 expression was significantly associated with the infiltration of several immune cell types in HCC. Conclusion: SNHG6 or MALAT1/hsa-miR-101-3p/EZH2 axis were identified as potential regulatory pathways in the progression of HCC.Background: Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and is accompanied by a complex regulatory network. Increasing evidence suggests that an abnormal gene expression of  Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for 90% of primary liver cancer cases, and is the leading cause of cancer-associated death worldwide ,2. InfecEZH2) is a member of the Polycomb group (PcG) family that forms multimeric protein complexes; the PcG family is involved in maintaining the transcriptional repressive state of genes over successive cell generations and in controlling the progression of the cell cycle, and it participates in the maintenance of cell differentiation [EZH2 regulates cell cycle progression, its dysregulation accelerates cell proliferation and prolongs cell survival, which may lead to carcinogenesis and cancer development [EZH2 is abnormally expressed in numerous tumors, including liver cancer, gastric cancer, and breast cancer, and correlates with tumor progression, metastasis, and drug resistance in prostate cancer cells [EZH2 may be a novel molecule involved in HCC progression, as well as a potential prognostic biomarker and therapeutic target [EZH2/miR-622/CXCR4 as a potential adverse prognostic factor and therapeutic target for HCC patients. Similarly, Bae et al. shared the same view that overexpression of EZH2 was an independent biomarker for poor outcomes of HCC, and that EZH2 may be used as a therapeutic target in patients with HCC [EZH2 are still lacking in HCC. In addition, the relevance of EZH2 to tumor immune infiltration is uncertain in HCC.Enhancer of Zeste Homolog 2 , U.S. National Library of Medicine. Secondly, the protein structure and conserved domains were explored via the Uniprot database . Thirdly, conserved amino acid sequences encoded by EZH2 and the phylogenetic tree of the EZH2 family were explored via the Constraint-based Multiple Alignment Tool  in NCBI. Finally, the distribution of the EZH2 protein was obtained from the Human Protein Atlas  database.In this study, we aimed to explore the oncogenic role and potential biological mechanism of human EZH2, TIMER [https://cistrome.shinyapps.io/timer/, accessed on 26 October 2021) was utilized to compare the differential expression levels of EZH2 between tumor and normal tissues in various tumor types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Subsequently, we used the GEPIA2 database [http://gepia2.cancer-pku.cn/#index, accessed on 2 November 2021) to validate the mRNA expression of the EZH2 gene with data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Two-tailed Student’s t-tests were used to analyze the data, and a difference was considered to be statistically significant when p < 0.01 and |log2 FC (fold change)| ≥ 1.To evaluate the expression of 2, TIMER   and low (50%) cutoff values. The log-rank test was used as the hypothesis test, and the Kaplan-Meier (K-M) curves were also obtained through the “Survival Analysis” module of GEPIA2. The Sangerbox  database was used to investigate how EZH2 expression influenced tumor prognosis, including disease-specific survival (DSS) and progression-free interval (PFI).We used the “Survival Map” module of GEPIA2 to obtain the overall survival (OS) and disease-free survival (DFS) significance map of https://starbase.sysu.edu.cn/index.php, accessed on 8 November 2021) database [EZH2 and corresponding miRNAs. The upstream-binding miRNAs of EZH2 were selected based on the following criterion: present in at least five of the following databases, consisting of PITA, RNA22, miRmap, microT, miRanda, PicTar, and TargetScan. The “pan cancer” module of ENCORI was used to perform the miRNA differential expression and survival analysis, miRNA-target co-expression, and RNA-RNA co-expression analysis.The Encyclopedia of RNA Interactomes  was used to provide the comparison of tumor infiltration levels among tumors with different somatic copy number alterations for the EZH2 gene. In addition, the “Gene” module of TIMER was also used to visualize the correlation of EZH2 expression with the immune infiltration level in HCC tissues. We assessed the relationships among EZH2 expression and immune cell chemotaxis, immune cell biomarkers, and immune checkpoints based on the “Correlation” module of TIMER. GEPIA2 was used to verify the correlation between EZH2 expression and immune cell biomarkers.The “SCNA” module of TIMER  was performed using the Sangerbox  online service. The top terms of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and HALLMARK analyses were exhibited.To explore the potential biological function and pathway relationships of EZH2 in HCC. EZH2 (Gene ID: 2146) is encoded on chromosome 7q36.1 and contains twenty-five exons  domain and a SET (cl02566) domain and is highly conserved in multiple species , hsa-miR-363-3p , hsa-miR-92b-3p , and hsa-miR-138-5p  positively regulated EZH2 expression (p = 9.68 × 10−11) and hsa-miR-26b-5p  negatively regulated EZH2 expression  was markedly downregulated in HCC and its upregulation was positively linked to patients’ prognosis  (p = 2.90 × 10−22) was markedly downregulated in HCC (p = 0.41) (EZH2 is displayed (EZH2 expression might be involved in LIHC progression.Non-coding RNA (ncRNA) comprises RNA molecules that do not encode a protein but regulate gene expression at multiple levels, including RNA splicing, editing, chromatin structure, and transcription . To detepression B. Howevepression B. The ge × 10−3) C,E. Furtd in HCC D. Howeve = 0.41) F. Meanwhisplayed G. Taken Here, we explored upstream lncRNAs of hsa-miR-101-3p based on the ENCORI database. The results reveal that 63 possible lncRNAs were identified as upstream lncRNAs of hsa-miR-101-3p. We assessed these lncRNAs’ differential expression levels between tumor and normal tissues using data from the TCGA and GTEx databases. Among the selected lncRNAs, we only identified three lncRNAs, namely, AC239868.3, SNHG6, and MALAT1, whose expression was significantly upregulated in HCC compared with normal controls A–C. AddiEZH2 in immune cell development, differentiation, and function [EZH2 expression associated with tumor-related immune cell infiltration will facilitate the monitoring of the HCC immunotherapy response and the exploration of the immune infiltration mechanism. We investigated the relationship between EZH2 and immune cell infiltration in HCC and found that the high copy number amplification in B cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells indicated significantly downregulated expression in HCC , in LIHC tissues , T lymphocyte-related chemokines (CCL1), mast cell-related chemokines , eosinophil-related chemokines , and neutrophil-related chemokines (CXCL8). Collectively, these results are in line with expectations and strongly suggest that EZH2 is positively linked to immune cell infiltration and chemotactic activities and plays a vital role in LIHC immunity.Given the known role of immune cells in tumor pathogenesis and the involvement of function , the iden in HCC A. Immune tissues B,C. ManyEZH2 is positively associated with immune cell infiltration in HCC, we investigated the relationship between EZH2 expression and the representative immune markers of several immune cells, including B cells, CD8+ T cells, CD4+ T cells, M1 macrophages, M2 macrophages, neutrophils, and dendritic cells , CD8+ T cells (CD8A and CD8B), CD4+ T cells (CD4), M1 macrophages (IRF5), M2 macrophages , neutrophils (ITGAM), and dendritic cells . Similar results were observed in the TIMER database. Taken together, the findings indicate that these immune marker genes play a key role in immune cell infiltration, indicating that EZH2 may be involved in immune surveillance and immune escape.To further validate the notion that ic cells . In the EZH2 expression had on immunotherapy in patients with LIHC, we next evaluated the relationship between EZH2 expression and PD-1, PD-L1, or CTLA-4 based on two different databases. For TIMER, the expression of EZH2 in HCC was significantly positively correlated with PD-1, PD-L1, and CTLA-4 , such as anti-CTLA-4 and anti-PD-L1/PD-1 antibodies, elicit durable and effective responses in some solid tumors . HoweverIn summary, we found that EZH2 was highly expressed in multiple types of human cancer (including HCC) and was associated with a poor prognosis in HCC. We constructed an ncRNA-mediated regulatory mechanism of EZH2 in hepatocarcinogenesis and progression, namely, EZH2-hsa-miR-101-3p-SNHG6/MALAT1. Additionally, we found that EZH2 expression was not only associated with immune cell infiltration but also correlated with the expression of immune checkpoint genes.


The genome packaging motor of bacteriophages and herpesviruses is built by two terminase subunits, known as large (TerL) and small (TerS), both essential for viral genome packaging. TerL structure, composition, and assembly to an empty capsid, as well as the mechanisms of ATP-dependent DNA packaging, have been studied in depth, shedding light on the chemo-mechanical coupling between ATP hydrolysis and DNA translocation. Instead, significantly less is known about the small terminase subunit, TerS, which is dispensable or even inhibitory in vitro, but essential in vivo. By taking advantage of the recent revolution in cryo-electron microscopy (cryo-EM) and building upon a wealth of crystallographic structures of phage TerSs, in this review, we take an inventory of known TerSs studied to date. Our analysis suggests that TerS evolved and diversified into a flexible molecular framework that can conserve biological function with minimal sequence and quaternary structure conservation to fit different packaging strategies and environmental conditions. Viral packaging motors are functionally conserved throughout the virosphere, from tailed bacteriophages, nature’s most abundant viruses, to herpesviruses, which represent some of humans’ most common pathogens. These powerful molecular machines generate forces as high as ~60 pN , capable of packaging genomes inside precursor capsids (procapsids or proheads) at speeds approaching 2000 bp/sec ,2. The pi) Initiation: when TerS and TerL form a ‘pre-packaging initiation complex’ that recognizes packaging initiation sites on viral DNA and introduces nicks into the double-stranded DNA (dsDNA) around the packaging site region. In P22 [ii) Translocation: triggered by TerL docking to the procapsid conformation of the portal vertex to form a ‘packaging terminase motor’ [iii) Termination: characterized by the TerL cleavage of the newly packaged DNA and the concomitant binding of tail factors to the portal protein [Genome packaging is a multi-step reaction, highly diversified in the virosphere, that uses both the virus and host reactants. Although all packaging components have been purified and characterized, the enzymology of packaging is exceedingly complex and challenging to study . In gene. In P22 ,16, λ [1. In P22 ,18, and . In P22 , the pree motor’ ,21,22,23 protein , which tDuring all three steps described above, TerL undergoes major conformational rearrangements. It is typically monomeric in solution ,26,27,28Salmonella phage P22 [Bacillus subtilis phages SPP1 and SF6 [Genetic evidence of the existence of a gene product involved in recognizing packaging initiation sites dates to 1982 . This gehage P22  and Baci and SF6 . Since t and SF6 .Bacillus subtilis; G20c/P74-26 for Thermus thermophilus; NV1/PaP3 for Pseudomonas aeruginosa; and T3/T7, P21/λ, and T4/44RR for E. coli). TerSs from phages infecting different bacteria occupy standalone groups (Myoviridae SK1 (identity/similarity = 14%/27%) than the Podoviridae P22 (identity/similarity = 12%/25%). Even TerS from two common Escherichia-phages, such as T7 and T4, have significantly diversified, with sequence identity and similarity scores of only 12% and 19%, respectively. By comparing TerS from two Myoviridae, he Actinomycetes phage SK1 and E. coli phage T4, the sequence identity/similarity is just 8% and 16%, respectively, dropping to only 5/8% when compared to the Podoviridae T7 , Shigel.e., Sf6 ,62), Sal.e., P22 ,64,65,66F6, SPP1 ), Pseudoe., PaP3 , NV1 [68e., PaP3 ), and The., G20c , P74–26 e., G20c ) , in equilibrium with a 13.3 S species of ~530 kDa, consisting of four protomers [1:TerS2 complex is devoid of catalytic activity [Salmonella-phage P22 [TerL and TerS assemble during genome packaging; however, with a few exceptions, a biochemically stable TerS:TerL complex has proven challenging to form in vitro as terminase subunits usually interact transiently. The TerS:TerL complex is stably populated only in three viruses studied thus far. Phage λ terminase subunits form a stable ~114.2 kDa heterodimer, consisting of one TerL (gpA) bound to two TerS subunits , a major mechanistic difference exists between phages that use a sequence .cos packagers such as phage λ [cos sequence serves as both the packaging initiation site used for genome recognition during genome packaging and a specific packaging termination sequence. The cos site represents a point of junction between two genome units within a concatemer that results from annealing two cohesive ends at the cos sequence. For instance, phage λ carries 12 base-long single-strand extensions (5′-GGGCGGCGACCT-3′) surrounding its chromosome that generate a cos site upon entry into a host cell. Cos sites are recognized by TerS and processed by TerL. Specifically, TerL introduces precisely staggered nicks in the cos sequence, while TerS binds the cos site in a sequence-specific manner, with detectable biochemical affinity in vitro, as seen for phages λ (gpNu1) [cos site specificity, cos packagers can accurately package one genome unit at a time without terminal duplications. This specificity is also observed in vitro, where TerS from cos packagers [cos sequence. For instance, recombinant PaP3 TerS binds a 20mer cos dsDNA oligonucleotide (5′-GCCGGCCCCTTTCCGCGTTA-3′) with an equilibrium binding constant Kd ~10 μM [In  phage λ , the cos (gpNu1)  and PaP3 (gpNu1) . As a reackagers ,58,90 bid ~10 μM .pac sequence, which is the putative recognition site for TerS. The packaging reaction is initiated by a first cut in the proximity of a pac site that consists of a 22-bp asymmetric sequence in the TerS gene for P22 [pac packagers and is terminated by a non-specific cut when the procapsid is full (hence the name ‘head-full’ packaging). The termination cut is also the start of the packaging for the next chromosome along the concatemer. Unlike P22, in SPP1, the pac site is estimated to be used only once every four packaging events [pac sequence, located within the TerS gene in P22, is supported mainly by genetic evidence [pac sequence (5′-AGAGAAGATTTATCTGAAGTCG-3′) were unsuccessful [pac site in other pac packagers such as Sf6 [In contrast, viruses that use the head-full packaging mechanism, such as P22 , Sf6 9292, SPP1  for P22 . Genome g events . Notablyccessful ,66. Simih as Sf6 ,62, P76-h as Sf6 , SF6 [67h as Sf6  was weakh as Sf6 . As no nh as Sf6 ). Moreovh as Sf6 .pac site in the T4 genome, and T4-like phages [pac site is recognized, and packaging initiates randomly. This packaging strategy is successful because T4-phages degrade the host DNA, ensuring that phage DNA is the main substrate available for packaging [Myoviridae phage E217 did not show appreciable DNA binding activity in vitro [More difficult to decipher is the association of T4 TerS with DNA. There is no unique e phages  package e phages  by a mecackaging . The T4-ackaging ,59. The ackaging  that is ackaging . Additioin vitro , possiblcos packagers that bind DNA specifically, in phage λ TerS, residues K5 and K6 (in helix α1) and S15, R17, T18, Q20, N21, and Q23 (in helix α2) are essential for DNA binding [pac packagers , thought to function as a chaperone or assembly factor ,78. TerLhomology , includihomology ,104 supehomology ,105  against the nuclease domain and surrounding the entire N-terminal ATPase domain with its C-terminal core (res. 532–775)  a. TerS c532–775) b. The re532–775) a,b. Unlipac sites on the HCMV genome. The mechanisms of genome packaging are significantly less well-understood in HCMV than in bacteriophages. pUL56 is known to bind concatemeric DNA on cis-acting packaging signals termed pac motifs [pac1 and pac2 lie within regions of HCMV DNA that are coincident with cis-acting cleavage elements recognized by TerL pUL89. Consistent with its function as a DNA-binding protein, pUL56 contains a zinc finger between residues 190 and 220 [pac1 [A low-resolution negative-stain EM structure of HHV-5  TerS (pUL56)  revealedc motifs . Specifi and 220 ,114,115. and 220  strength20 [pac1  motif on50 = 5.1 ± 1.2 nM) and a selectivity index exceeding of 15,000 [pUL56 TerS is the cellular target of letermovir (M.W. ~572.5 Da), an FDA-approved antiviral agent of great potential in the treatment of HCMV opportunistic infection in transplant patients, currently under development by Merck & Co. Letermovir exhibits outstanding anti-HCMV activity in vitro The gene encoding TerS has diverged more and faster than TerL or portal proteins, suggesting that this terminase subunit exerts a function that can be conserved with significant variations on the same structural framework. TerS is functionally conserved in herpesviruses but fundamentally reinvented from bacteriophages.(ii)TerS is essential in vivo but dispensable in vitro where an excess of TerL, viral DNA, and ATP are sufficient to promote energy-dependent genome packaging.(iii)Phage TerS are oligomeric in bacteriophages, but the stoichiometry of oligomerization varies mainly with nonamers, decamers, and octamers. This suggests that TerS can retain its function with a different stoichiometry of oligomerization. In herpesviruses, TerS is larger than TerL and adopts a helical solenoid-like structure.(iv)TerS interacts with TerL to promote genome packaging, but this association can be very transient or remarkably stable, suggesting that the heterotypic association of terminase subunits has diverged significantly, even in closely related phages.(v)cos versus pac packagers. Cos packagers encode TerSs that make strong and saturable interactions with dsDNA. In contrast, TerS from pac packagers makes weak, sequence-independent contacts with dsDNA.TerS association with DNA is fundamentally different in (vi)cos sites, a vital function of TerS. Whatever model is contemplated must consider how TerS can read DNA nucleobases. TerS contains Zn-binding domains in herpesvirus, possibly responsible for specific DNA recognition.Both threading and nucleosome models are incomplete and unsupported by biochemical data. Neither model can explain the sequence-specific recognition of (vii)HCMV TerS (pUL56) is the target of letermovir, an FDA-approved antiviral agent of great potential in treating HCMV opportunistic infection in transplant patients.Four decades after the identification of a gene product required to recognize ac sites , the bioIn conclusion, forty years since its discovery, the small terminase subunit remains the most enigmatic of all factors involved in viral genome packaging. Structural studies of TerS in complex with DNA are needed to decipher how the identified DNA-binding determinants in TerS result in the sequence-specific recognition of packaging initiation sites, an essential activity in the virosphere. The hope is that cryo-EM will allow scientists to capture TerS or a TerS:TerL complexes bound to DNA, thus reconciling decades of sparse and often conflicting biochemical observations.


FDX1), the core regulatory gene in cuproptosis, is rarely studied, and the regulation of FDX1 in tumor biology remains obscure. A comprehensive pan-cancer analysis of FDX1 is needed.The mechanism of copper-induced cellular death was newly discovered and termed cuproptosis. Inducing cuproptosis in cancer cells is well anticipated for its curative potential in treating tumor diseases. However, ferredoxin 1 , microsatellite instability (MSI), stemness, tumor immune dysfunction and exclusion (TIDE), and immunotherapy-related analyses were performed. FDX1 protein expression was assessed by kidney renal clear cell carcinoma (KIRC) tissue microarray immunohistochemistry. The function of FDX1 in KIRC was further explored by experiments in 786-O cell lines in vitro.Thirty-three types of tumors were included with paired normal tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. The interaction between transcription, protein, phosphorylation, and promoter methylation levels was analyzed. Survival, immune infiltration, single-cell FDX1 is highly expressed in 15 tumor types and lowly expressed in 11 tumor types. The corresponding changes in protein expression, phosphorylation, and promoter methylation level of FDX1 have been described in several tumors. Survival analysis showed that FDX1 was related to favorable or poor overall survival in eight tumors and progression-free survival in nine tumors. Immune infiltration and single-cell analysis indicated the indispensable role of FDX1 expression in macrophages and monocytes. Multiple established immunotherapy cohorts suggested that FDX1 may be a potential predictor of treatment effects for tumor patients. Tissue microarray analysis showed decreased FDX1 expression in KIRC patients’ tumor tissues. Knockdown of FDX1 resulted in the downregulation of cuproptosis in kidney renal clear tumor cells. Mechanistically, the FDX1-associated gene expression signature in KIRC is related to the enrichment of genes involved in the tricarboxylic acid (TCA) cycle, NOTCH pathway, etc. Several NOTCH pathway genes were differentially expressed in the high- and low-FDX1 groups in KIRC.FDX1, has differential expression and modification levels in various tumors, which is associated with cellular function, immune modulation, and disease prognosis. Thus, FDX1-dependent cuproptosis may serve as a brand-new target in future therapeutic approaches against tumors.Our analysis showed that the central regulatory gene of cuproptosis,  Science in 2022, sparking worldwide concern (FDX1), also called adrenodoxin, was identified in 1967 as one component of the electron transport system in adrenal steroid hydroxylase (FDX1 is required for the synthesis of steroid hormones or heme A and Fe-S proteins (FDX1 expression and diseases showed that downregulation of FDX1 was probably related to the onset of polycystic ovary syndrome (PCOS)  nephropathy  , while ahttp://xena.ucsc.edu/)    and 7,862 samples from Genotype-Tissue Expression (GTEx) were downloaded from The University of California Santa Cruz (UCSC) Xena (sc.edu/) Table 1.E135222) , and LauE135222) . The KimFDX1 expression provided by the algorithm were involved in our models. A comparison of survival curves was performed using log-rank tests, with p-values <0.05 considered significant.Kaplan–Meier survival curves and univariate Cox proportional hazards models were plotted with the R Survival package. For assessment of the clinical features [overall survival (OS) and progression-free survival (PFS)], both the 50% and the optimal survival cutoff value of http://timer.cistrome.org/) was used to estimate FDX1-related immune infiltration in the 33 tumors. The algorithm included TIMER, tumor immune dysfunction and exclusion (TIDE), CIBERSORT-ABS, quanTIseq, and xCELL. Correlations between FDX1 and immune cells that were consistent among two or three algorithms were regarded as meaningful, and some of them were selected to draw a correlation plot.TIMER2.0    was selehttp://tisch.comp-genomics.org/home/), a single-cell RNA sequencing (scRNA-seq) database. Two cell type annotations including major lineage and malignancy were used to present the specific expression of FDX1. Single-cell profile of GBM_GSE131928_10X_Adults and KIRC_GSE171306, and the visualization of FDX1 expression were performed by R package Seurat. About 10580 and 11828 high-quality cells were obtained in KIRC and GBM studies separately.Single-cell gene expression data across all available cell types were downloaded from the Tumor Immune Single-cell Hub (TISCH) (The TMB function from the R package maftools was used. TCGA microsatellite instability (MSI) algorithm was based on a previous study . The onehttp://tide.dfci.harvard.edu).TIDE uses a set of gene expression markers to evaluate the immune escape ability of tumor cells. Higher TIDE scores mean a poorer immune checkpoint blockade (ICB) therapy response . The TIDFDX1 expression groups (50% cutoff) and taking the logFC absolute value >0.5, p < 0.05 as the differential locus.Differentially expressed genes (DEGs) were obtained through the Limma package by calculating the differential genes in the high and low Then, genes were ranked by log2FC and imported into gene set enrichment analysis (GSEA). Then, GSEA was performed using the R package clusterProfiler v3.14.3. GSEA gene sets included HALLMARK REACTOME, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene sets in which | NES | > 1, p-value < 0.05 and q value < 0.25 were considered as significant.FDX1 antibody (12592-1-AP) was bought from Proteintech (www.ptglab.com). The immunohistochemistry (IHC) experiment was done as follows: 1) Bake slices at 60°C for 30 min, routinely dewaxed and hydrated; 2) Antigen retrieval: Recover antigen with 0.01 M citrate buffer (pH 6.0) under high pressure for 2 min, cool to room temperature, and wash with phosphate buffered saline (PBS) for 5 min × three times; 3) Block endogenous peroxidase with 3% H2O2-methanol, wash at room temperature for 10 min, and wash with PBS for 5 min × three times; 4) Add normal non-immune animal serum dropwise at room temperature for 10 min; 5) Remove the serum, add the primary antibody dropwise, and refrigerate at 4°C overnight; 6) Wash with 0.1% Tween-20 PBS for 5 min × three times; 7) Add biotin-labeled goat anti-mouse/rabbit IgG dropwise and incubate at room temperature for 10 min; 8) Wash with 0.1% Tween-20 PBS for 5 min × three times; 9) Add streptavidin-peroxidase dropwise and incubate at room temperature for 10 min; 10) Color with Diaminobenzidine (DAB) for 5 min and wash with distilled water to stop color development; 11) After hematoxylin counterstaining, washing, and differentiation, fully washed and returned to blue; 12) Conventional dehydration and transparent, neutral gum sealing. Quantification of the IHC-positive signals of FDX1 was performed by ImageJ and the IHC Profiler plugin.We obtained the kidney renal clear cell carcinoma tissue microarray from the Outdo Biotech company and the ethics was approved. The 2. Small interfering RNA (siRNA) was purchased from Sangon Biotech . The 786-O cells were transfected with 50 nM FDX1 siRNAs using LIPO3000. Elesclomol (10 nM) and CuCl2 (10 μM) were used to induce cuproptosis. RT-PCR was used to evaluate the knockdown efficiency after 48 h of transfection. The following siRNA sequences were used: hFDX1-378-F, GUCCACUUUAUAAACCGUGAUTT; hFDX1-378-R, AUCACGGUUUAUAAAGUGGACTT. The primers were as follows: FDX1-F, TTCAACCTGTCACCTCATCTTTG; FDX1-R, TGCCAGATCGAGCATGTCATT; GAPDH-F, GGA GCGAGATCCCTCCAAAAT; and GAPDH-R, GGCTGTTGT CATACTTCTCATGG. The Cell Counting Kit-8 (CCK-8) was used to calculate the cell viability.The 786-O cell line was obtained from the Central Laboratory of Tumor Hospital . RPMI 1640 containing 10% fetal bovine serum was used to culture the cell line at 37°C in a humidified atmosphere containing 5% COSpearman's rank correlation coefficient was utilized to discover the strength of a link between two independent sets of data. The Wilcoxon rank-sum test was used to calculate the significance of the differences in methylation, transcription, protein, and phosphorylation levels between normal and tumor groups. All analyses were conducted in R version 3.6.3, and data visualization was accomplished by ggplot2. In this study, p < 0.05 is regarded as statistically significant .2+ was transported into the cell by its carrier, it could be reduced to Cu1+ by FDX1. Meanwhile, FDX1 could regulate the acylation of dihydrolipoamide S-succinyl transferase (DLAT), an indispensable component of the TCA cycle-related pyruvate dehydrogenase complex. Increased Cu1+ caused cuproptosis by binding to DLAT and decreasing Fe-S cluster proteins (FDX1 in the 33 tumors (FDX1 was highly expressed in 15 tumors: colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), glioblastoma multiforme (GBM), acute myeloid leukemia (LAML), brain lower-grade glioma (LGG), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) (p < 0.05). FDX1 levels in 11 other tumors were lower compared to normal tissues: adrenocortical carcinoma (ACC), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), KIRC, kidney renal papillary cell carcinoma (KIRP), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), testicular germ cell tumor (TGCT), and thyroid carcinoma (THCA) (p < 0.05). While in bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), and sarcoma (SARC), FDX1 showed no significant expression difference compared to normal tissues.When CuFDX1 in 10 tumors , while it was lower in COAD, KIRC, LUAD, HNSC, PAAD, GBM, and LIHC (p < 0.05).We also used the CTPAC to analyze the protein level of FDX1 was also assessed based on CTPAC datasets , while FDX1 in CHOL, KIRP, PCPG, TGCT, and THCA was lowly expressed. The FDX1 promoter in KIRC, LIHC, and PRAD was lowly methylated (p < 0.05), but only PRAD had a higher expression of FDX1. We also analyzed the relation between six FDX1 promoter methylation probes and the expression of FDX1  = 2.92, p < 0.001, FDX1 in KIRC  and ACC , while a high expression of FDX1 could predict favorable PFS in mesothelioma (MESO) , KIRC , and THCA . Furthermore, based on the optimal cutoff value instead of 50%, FDX1 played a prognostic role in OS in ACC, COAD, LGG, HNSC, KIRC, LIHC, KIRP, and LAML (FDX1 could work as a prognostic factor of PFS in nine types of tumors (We divided the patients into the high (the top 50%) and low (the bottom 50%) FDX1 expression and TMB, MSI, and stemness. TMB represents levels of gene mutations in their tumor cells  or VEGFA . However, in ACC, most of the inhibitory and stimulatory genes were negatively related to FDX1  - like malignant cells were the highest FDX1-expressing cells. The visualization of single-cell FDX1 expression in GBM and KIRC could be seen in To be specific, immune cells including CD4 T cells, CD8 T cells, regulatory T cells (Tregs), natural killer (NK) cells, and dendritic cells (DCs) expressed FDX1 and immunotherapy, the correlation analysis between FDX1 and CD274 was done. It indicated that FDX1 was positively related to CD274 in BRCA, GBM, KICH, KIRC, KIRP, LGG, OV, PAAD, PCPG, PRAD, SARC, SKCM, TGCT, UCEC, and UVM, especially in KICH, KIRC, KIRP, LGG, PCPG, and UVM  were included to illustrate that the high-FDX1 group had a higher OS rate after the PD-1 blockade treatment  showed that the high-FDX1 group had a more favorable prognosis  and KEGG_NOTCH_SIGNALING_PATHWAY (47 genes)] between the high- and low-FDX1 groups. It appeared that the high-FDX1 KIRC group had higher APH1A, DLL1, MAML2, PSEN2, JAG1, NOTCH1, and NOTCH2 and lower LFNG, DTX1, and DTX2  dinucleotides sites in the promoter region, and then the average value was taken to represent the degree of methylation in the region. This approach ignores the cellular heterogeneity in the cells or tissue samples used for gene sequencing, that is, the methylation status of a locus may vary at the single-cell level  played a role in controlling intracellular copper levels and the oxidation of lipoproteins  was conducted. It revealed that FDX1 was positively related to CD274 in most tumors. TIDE scores showed that higher FDX1 in KIRC would have a better response to immunotherapy. Established cohorts of KIRC verified the prediction as shown in FDX1 may have attractive potential as a biomarker for tumor immunotherapy.To further analyze the role of FDX1 was downregulated in the KIRC group than normal tissues at the transcription and protein levels. The analysis revealed that the phosphorylation level of FDX1 was upregulated in KIRC. Several studies demonstrated that phosphorylation of FDX1 could inhibit its function, which may facilitate tumor growth . Another study also proved the lower expression level of FDX1 in KIRC  inhibitors could enhance the effect of cancer treatment compared to UPS itself  can be found in the article/JX wrote the manuscript. ZH finished the whole analysis. QC and JL designed the study and revised the manuscript. HC, HZ, PL, JZ and XW conducted the microarray experiments and helped the writing of the manuscript. All authors contributed to the article and approved the submitted version.