ADAMDEC1 mRNA sequence was discovered in dendritic cells of human tonsils and categorized into the ADAM family (Mueller et al., 1997). However, the protein is short and consists 470 aa. Its structure is untypical since ADAMDEC1 is missing the cytoplasmic tail, transmembrane and cysteine-rich domains, thus consisting of only a signaling peptide, prodomain, metalloprotease and disintegrin-like domains (Fig. 1) (Mueller et al., 1997). Additionally, analyzing the canonical structure of ADAM enzymes-histidine (H) repeat motif (HEXXHXXGXXH) within the zinc-binding site of the metalloprotease domain, in ADAMDEC1, the third histidine is replaced by aspartate (D) serving in the motif sequence of HEXXHXXGXXD (Mueller et al., 1997; Lund et al., 2013). This rare structure was connected with the reduced catalytic activity, diminished substrate specificity and enhanced resistance to the regulation by endogenous inhibitors of metalloproteinases (TIMPs) (Lund et al., 2013; Lund et al., 2015).

Physiologically, the highest expression of ADAMDEC1 was found in the gastrointestinal, expressed predominantly in the macrophages in the lamina propria. The lower intensity of ADAMDEC1 was detected in seen, lymph nodes, tonsils, spleen, urinary bladder and placenta (Mueller et al., 1997; Kumagai et al., 2021).

The whole-genome transcriptomic analysis associated ADAMDEC1 expression deviations with Crohn’s disease (de Bruyn et al., 2014), rheumatoid arthritis (Galligan et al., 2007) and atherosclerosis (Papaspyridonos et al., 2006).

ADAMDEC1 downregulation in the inflamed ileal biopsy samples collected from patients with Crohn’s disease compared to healthy controls was detected. This correlation was independent of the degree of mucosal inflammation, thus underlying ADADEC1 role in the pathogenesis of this disease (de Bruyn et al., 2014).

About 24 times enhanced ADAMDEC1 level was detected in fibroblast-like synovial cells isolated from joint tissue taken from patients with rheumatoid arthritis (Galligan et al., 2007). This observation was confirmed by transcriptional datasets generated from the synovial tissue rheumatoid arthritis and osteoarthritis (Li et al., 2019).

Next, the enhanced presence of ADAMDEC1 in the circulating monocytes was found to have the third significant positive association with the occurrence of atherosclerotic plaques in the common carotid artery. Thus, it can suggest the potential function of ADAMDEC1 in atherosclerosis and plaque instability development (Verdugo et al., 2013).

In the context of cancerogenesis, the drastic reduction of ADADEC1 in the colorectal adenoma compared to the normal colorectal tissues was associated with poor prognosis of patients. However, it was not connected with the loss of macrophages in tissues, verified by CD68 expression (Kalmár et al., 2019).

Analysis of patients with craniopharyngioma revealed accelerated ADAMDEC1 expression in collected specimens compared to normal brain tissues. The primary craniopharyngioma-derived cells treated with an anti-estrogen drug, tamoxifen, reduced tumor cell proliferation and also the expression of ADAMDEC1 mRNA/protein levels (Xu et al., 2012).

A potential mechanistic role for ADAMDEC1 in developing oral squamous cell carcinoma (OSCC) has been shown in experiments based on EGF-receptor (EGFR) activation. An ectopic expression of EGFR is detected in approximately 90% of OSCC, and it is a factor that can drive an aggressive phenotype and contribute to decreased response to radiotherapy. The thrombin-stimulated platelets were shown to secrete ADAMDEC1, resulting in the cleavage of the platelet membrane-bound pro-EGF. The soluble EGF then resulted in a migratory and invasive phenotypic shift in the OSCC via EGFR signaling (Chen et al., 2018).

Finally, Jimenez-Pascual et al. (2019), Vauléon et al. (2012) identified ADAMDEC1 as one of the top up-regulated ADAM genes in glioblastoma multiforme (GBM), which correlates with poor patients prognoses. The study showed that ADAMDEC1 rapidly solubilizes the membrane-bound FGF2 and stimulates FGFR1 expressed on the GBM stem cells. Further, the transcription factor Zinc finger E-box-binding homeobox 1 (ZEB1) was activated and consequently induced ADAMDEC1 expression by inhibiting miR-203 (functioning as the tumor-suppressor) and creating a positive feedback loop. Targeting this signaling pathway decreased self-renewal and tumor growth, which highlighted the potential role of ADAMDEC1 in cancer propagation (Jimenez-Pascual et al., 2019).

RCC is originated from the renal epithelium and accounts for >90% of all kidney malignancies. The disease encompasses >10 histological and molecular subtypes, of which KIRC is most common (70%–80%; accounts for most cancer-related deaths), next, papillary (10%–15%), chromophobe (5%–10%) and collecting duct (1%) RCC (Hsieh et al., 2017; Harbin et al., 2015; Muglia and Prando, 2015). These three subtypes are the most frequent histological groups that together represent more than 90% of all RCC (Muglia and Prando, 2015).

Approximately 2%–3% of all RCC are hereditary and several autosomal dominant syndromes (e.g., Hippel–Lindau disease) are described. Well-validated targets, including VHL, VEGFR and mTOR and pathways such as HGF/c-MET and Wnt/β-catenin, are strongly related to RCC pathogenesis (Maher, 2018).

RCC is a disease with complex etiologies, which the combined effect of multiple genes may cause. In this context, VHL (von Hippel-Lindau), p53, p16, p21 and p27 were shown as the primary tumor suppressor genes in RCC, in which VHL and p53 were certified to result in the development of RCC (Liu et al., 2015). Dysfunction of VHL will lead to constitutively aberrant activation of the hypoxic response, like upregulation of vascular endothelial growth factor (VEGF), which is considered to play essential roles in tumor development and angiogenesis (Zhang and Zhang, 2018). p53 is shown to suppress tumor growth and induce cell apoptosis in KIRC (Zhao et al., 2016).

Recently the genetic profiling of human RCC has increasingly been used to identify the potential of epigenetic regulatory mechanisms. It was shown that single-stranded molecules (lncRNA, miRNA) (Trilla-Fuertes et al., 2020; Rudzinska et al., 2021) and DNA methylation (Lasseigne and Brooks, 2018) are deeply involved in renal pathogenesis.

DNA methylation involves the covalent transfer of a methyl group to the C-5 position of the cytosine DNA ring by DNA methyltransferases and causes gene silencing. The genome-wide DNA methylation study in RCC identified increased global methylation in more aggressive cancer types and a potential risk factor associated with malignant transformation. The variations in sequence hypermethylation were dependents on medical aspects, such as aggressive cancer, tumor size, etc.

The potential therapeutic targets, such as small-molecule multikinase inhibitors that target VEGF receptors (sunitinib and sorafenib), the anti-VEGF antibody bevacizumab, and a mammalian target of rapamycin inhibitor temsirolimus, still are not efficient enough in the KIRC currency and chemioresistance is often observed (Larkin et al., 2009; Rudzińska et al., 2021; Penticuff and Kyprianou, 2015). It is connected with the fact that molecular mechanisms underlying this disease have not been fully elucidated. In addition, more than 50% of KIRC are detected incidentally because of a lack of effective diagnostics in the early stages of the disease; it leads to metastasis and increases the mortality rate (Czarnecka et al., 2014). This scenario emphasizes the need to discover new biomarkers for RCC subtypes with the tumor grade-dependent manner. Furthermore, exploring molecular biology of KIRC can deepen the knowledge about its mechanism and identify the novel molecular targets.

The online database Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/index.html; Tang et al., 2017) is a communal network server that includes a transcriptome sequencing dataset of 9736 tumors together with 8587 adjacent normal tissues from TCGA and Genotype-Tissue Expression (GTEx) data sets. Here, the elevated expression of proteases was analyzed in 623 KIRC cases, compared to 100 non-tumoral kidney samples. Correlation analysis was performed on the selected gene pairwise, and ANOVA test was used to calculate the statistical significance (p < 0.05 was considered as significant).

ADAMDEC1 expression level was analyzed in 16 other human tumors at the same way.

Protease list was downloaded from MEROPS The Peptidase Database (https://www.ebi.ac.uk/merops/; Rawlings et al., 2021).

ADAMDEC1 expression level with tumor grade information was collected from Pathology Atlas (https://www.proteinatlas.org/humanproteome/pathology; Uhlen et al., 2017). Data were analyzed using a non-parametric U-Mann–Whitney test (GraphPad, Prism 6.00 for Windows, Graf Pad software, San Diego, CA, USA). Data are reported as violin plots and p-value < 0.05 was considered statistically significant.

By GEPIA the ADAMDEC1 ectopic expression was correlated with survival of KIRC patients. The Kaplan-Meier plots log-rank allowed for identification of predictive value of analyzed gene.

By USC Xena Browser (https://xenabrowser.net/; Goldman et al., 2020), the hypermethylation status of ADAMDEC1 and somatic mutations were analyzed. DNA methylation profile was detected using Infinium Human Methylation 450 and Human Methylation 27 techniques, whereas the somatic mutations were detected using the MuTect2 tool.

The potential interactions of ADAMDEC1 with other molecules was visualized by The Search Tool for the Retrieval of Interacting Genes - STRING database (https://string-db.org/; Szklarczyk et al., 2021). PPI map was constructed, with the active interaction sources, such as experiments, databases, textminig, co-expression, gene fusion, and co-occurrence.

Using the GEPIA database, all significantly up-regulated genes in 623 KIRC cases compared to the healthy cohort (100 samples) were listed. Next, by MEROPS The Peptidase Database, all changed proteases were selected and a shortlist of twentieth strongest changed proteases is included below (Table 1).

The strongest changed ADADEC1 was taken for further analysis and firstly, the significant (p < 0.05) evaluation of ADAMDEC1 expression in KIRC samples compared to normal kidney tissues was confirmed (Fig. 2A). Following, the higher ADAMDEC1 expression was detected in 14 other human tumors (Fig. 2B), including: invasive breast carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), cutaneous skin melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT) and uterine corpus endometrial carcinoma (UCEC). Interestingly, ADAMDEC1 was not elevated in kidney chromophobe (KICH) and kidney renal papillary cell carcinoma (KIRP) samples to serve as distinguishing features of the KIRC subtype.

Further, ADADEC1 expression was correlated with clinical outcomes of KIRC cases. As a result, the accelerating ADAMDEC1 expression level tendency corresponded to higher tumor stages; from I to IV stages (Fig. 3A). Significant different (*p < 0.05) was noticed between I and III and IV grades. In addition, its enhanced expression notably (p = 0.046) corresponded to a worse survival prognosis of patients (Fig. 3B).

To investigate the possible relationship between hypermethylation and ADAMDEC1 expression, an analysis of methylation among KIRC cases was performed using USC Xena browser (Fig. 4C). The hypermethylation was detected in part of both tumors and non-tumor controls.

Additionally, the somatic mutations were checked and missense variant p.W23R/substitution T to C (hg38 ch8:24,384,571) in one case was detected (Fig. 4D).

Proteins and protein-protein interactions form the backbone of the cellular complex machinery. Thus, for the full understanding of biological phenomena, the full network needs to be considered. The Search Tool for the Retrieval of Interacting Genes-STRING database collected scored and integrated all publicly available sources of protein-protein interaction information, and to complemented these with computational predictions. The STRING database uses protein-protein co-occurrence as one type of text mining evidence for protein interactions. Text mining (text data mining) transforms unstructured text into a structured format to identify meaningful patterns and new insights. Text mining systems are designed to extract information from the text in a domain-oriented manner (Franceschini et al., 2013).

Here, STRING was used to seek potential interactions between ADAMDEC1 with other molecules (Fig. 5A). As a result, PPI network was constructed, with the active interaction sources, including experiments, databases, textminig, co-expression, gene fusion, and co-occurrence (Fig. 5B). In the matrix, the nodes correspond to the proteins, and the edges represent the interactions. Using an interaction score threshold of 0.7 (high confidence), the string PPI analysis yielded a highly clustered network (clustering coefficient: 0.73) containing 11 nodes with 19 edges (enrichment p-value < 0.008), false discovery rate = 1.42E-0.5.

As the outcome, the co-expression was found for CXCL9 and SLAMF9. Text mining systems showed the interactions with proteins, including DUOXA1, ZNF511, CXCL9, KNDC1, GPR123, DUOX2A, SMPDL3A, SLAMF8, ERIN1, TACSTD2, with a different score value.

Recently, ADAMDEC1 has been connected with the development of varied human diseases, including cancers. Herein, the presented review with database analysis demonstrates the first evidence for positive relations of ADAMDEC1 expression with KIRC cases and its worse outcomes. It can implicate the potential role of ADAMDEC1 in the pathogenesis of KIRC. The presented findings support the further experimental analysis of ADAMDEC1 as a KIRC biomarker and suggest its essential function in diagnosing and promoting KIRC. If validated, this biomarker can significantly facilitate the introduction of new therapies based on protease inhibition that profoundly affects the efficient treatment of KIRC patients. Additionally, this knowledge can be helpful in research based on specific protease inhibitors against KIRC development.