A prognostic and immunological analysis of 7B-containing Kelch structural domain (KLHDC7B) in pan-cancer: a potential target for immunotherapy and survival

KLHDC7B is a member of Kelch family, with a Kelch domain in the C-terminal half, which plays a role in various cellular events, such as cytoskeletal arrangement, protein degradation, gene expression. Although there is increasing evidence supporting KLHDC7B's vital role in tumorigenesis, a systematic analysis of KLHDC7B in cancers remains lacking. Therefore, we intended to investigate the prognostic value for KLHDC7B across 33 cancer types and explore its potential immunological function. GEO (Gene Expression Omnibus database) and TCGA (The Cancer Genome Atla) database were used to explore the role of KLHDC7B in 33 cancers. TIMER2, GEPIA2 and Kaplan–Meier plotter were utilized to explore the KLHDC7B expression level and prognostic value in different cancers. The pan cancer genetic variation and DNA methylation of KLHDC7B were analyzed by cBioPortal and MEXPRESS. TIMER2 was employed to investigate the correlation between KLHDC7B expression and immune infiltration. The relationship of KLHDC7B expression with TMB (tumor mutational burden) and MSI (microsatellite instability) were evaluated using Spearman correlation analysis. Finally, by GO and KEGG enrichment analysis, the underlying mechanisms of KLHDC7B in tumor pathophysiology were further investigated. KLHDC7B expression level was related to pathological stages, MSI, TMB, immune checkpoint and immune cell infiltration in most cancers. Especially, we found that the KLHDC7B expression was negatively correlated with the immune infiltration of Myeloid derived suppressor cells into TGCT and GBM. Additionally, survival analysis showed that the expression of KLHDC7B was connected with overall survival (OS) in 3 cancers and disease-free survival (DFS) in 5 cancers. Furthermore, the enrichment analysis revealed that the KLHDC7B collecting genes and binding proteins are related to the function of proteins and immune response. KLHDC7B demonstrates strong clinical utility as markers of prognostic and immune response in pan-cancer.


Introduction
A member of the Kelch family, KLHDC7B (7B-containing Kelch structural domain) contains 594 amino acids, located on human chromosome 22q13.33 (https:// www. ncbi. nlm. nih. gov/ prote in/) with a Kelch domain (http:// www. unipr ot. org/ unipr ot/ Q96G42) generally consisting 5-7 repeating motifs. Kelch proteins are engaged in various cellular processes, for instance cytoskeletal arrangement, cell morphology, protein degradation, gene expression (Gupta and Beggs 2014). However, KLHDC7B is unknown to have any cellular function. KLHDC7B is one of the rare examples of hypermethylation, but is upregulated in cancerous tissues (Kim et al. 2010).Accumulating evidence has suggested that Jiatong Ding, Xunhui Ji, and Fei Guo have contributed equally to this work.
KLHDC7B also has an impact on the cell proliferation. A Kelch domain-containing KLHDC7B promotes breast cancer cell proliferation through the interferon signaling pathway (Jeong et al. 2018).Moreover, in breast cancers, KLH-DC7B has been found to be a tumor marker for epigenetic differences (Jeong et al. 2018) and laryngeal cancer . Also, alternative splicing events of KLHDC7B may be involved in the development and progression of cervical squamous cell carcinoma (CSCC) and may serve as a biomarker for the diagnosis and prognosis of the disease (Guo et al. 2015). All these indicate that KLHDC7B may be related to the progression and development of cancers. Thus, it is exceptionally valuable to go deeper to investigate the molecular mechanisms and regulatory functions of KLHDC7B in the pan-cancer dataset so that new directions and strategies can be established for the treatment of cancer in the clinic.
Considering the possible connections between genes and cancers, to understand what genetic factors influence pan cancer incidence and prognosis, as well as underlying molecular mechanisms, it is essential to study genes of interest. A pan-cancer analysis can be performed using The Cancer Genome Atlas (TCGA) project and the Gene Expression Omnibus (GEO) database, where different types of cancer are represented in these functional genomic datasets.
We observed the evidence for experimentally determined correlations between KLHDC7B and different cancer types or stages in this article. Here, using the TCGA and the GEO database, a pan-cancer study of KLHDC7B was conducted for the first time. We also investigated the probable KLHDC7B-correlated pathways in the clinical outcomes or pathogenesis in a variety of cancers, including related biological processes, immune infiltration, DNA methylation, genetic change, survival conditions, immune checkpoint and expression difference. Our goal is to recognize potential pathways that could lead to identify how KLHDC7B influences the clinical outcomes and immune infiltration of certain cancers through a relatively comprehensive understanding of its dual role, which may serve as a potential target for immunotherapy and survival.

Analysis of KLHDC7B gene expression in pan-cancer
Firstly, to confirm the difference in KLHDC7B expression in normal and primary tumor tissues, we used the "Gene DE" module of the Tumor Immune Estimation Resource 2nd Edition (TIME2.0) website (http:// timer. cistr ome. org/), and entered "KLHDC7B" to investigate the differential expression of KLHDC7B between tumors and adjacent health tissues in the TCGA database. After identifying tumors with extremely restricted or no normal tissue, such as LGG (brain lower grade glioma), ACC (adrenocortical carcinoma), LAML (acute myeloid leukemia), we accessed the GEPIA2 (Gene expression profiling interactive analysis, version 2) (Tang et al. 2019) website (http:// gepia2. cancerpku. cn/# analy sis), and then selected the "Expression analysis-Box Plots" module to obtain box plots showing the differences in expression between tumor tissue and its corresponding normal organization by GTEx (Genotype-Tissue Expression) (Lonsdale et al. 2013). As for the main parameters, they were set to the following: log2FC (fold change) cutoff of 1 and p-value cutoff of 0.01, and "Match TCGA normal and GTEx data". What's more, in TCGA, we obtained different pathological stages (stage from I to IV) by one-way ANOVA using the "Pathological stage plot" module of GEPIA2. Across all tumor types, we obtained violin plots of KLHDC7B expression. Box plots or violin plots using Box plots or violin plots using log [TPM (Transcripts per million) + 1] transformed expression data with Pr(> F) < 0.05 were considered statistically significant.
Secondly, we selected the CPTAC (Clinical proteomic tumor analysis consortium) dataset for the expression analysis of proteins via UALCAN (http:// ualcan. path. uab. edu/ analy sis-prot. html). Our study examined the total protein expression level of KLHDC7B between normal tissues and Breast cancer, Lung adenocarcinoma, Head and neck squamous carcinoma (HNSC). Moreover, we entered "KLHDC7B" on the HPA (human protein atlas) website (https:// www. prote inatl as. org) to explore the level of KLHDC7B in varying tumor types.
Finally, based on the UCSC (Kent et al. 2002) genome browser (http:// genome. ucsc. edu/), we obtained information on the genomic location of "KLHDC7B". In addition, we performed the analysis of conserved functional domains from KLHDC7B proteins with diverse species by the NCBI (National Center for Biotechnology Information) "HomoloGene" (https:// www. ncbi. nlm. nih. gov/ homol ogene/).

Analysis of KLHDC7B survival prognosis
We applied the "Survival Map" module of GEPIA2 to analyze the OS and DFS expression levels of KLHDC7B in all TCGA tumors and set a cutoff threshold of 50% for high and 50% for low KLHDC7B expression cohorts. Also, a survival plot of KLHDC7B was obtained using the Survival Analysis module of GEPIA2, with the log-rank test used for hypothesis testing. Further, we used Kaplan-Meier plotter web(http:// kmplot. com/ analy sis/) (Lánczky and Győrffy, 2021) server to analyze the OS (overall survival), PFS (progress-free survival), FP (first progression), DMF (distant metastasis-free survival), PPS (post-progression survival) and RFS (relapse-free survival) of KLHDC7B in TCGA tumors. As formerly reported, patients with ovarian, gastric, breast, and lung cancers were classified into high and low expression groups based on the median expression of KLHDC7B, and finally the K-M survival plots were drawn (Lei et al. 2021) based on the calculated log-rank p-values associated with the hazard ratio (HR) and 95% confidence intervals.

Analysis of KLHDC7B genetic alteration and methylation modification
Using cBioPortal web (https:// www. cbiop ortal. org/) (Gao et al. 2013), we selected the "TCGA Pan Cancer Atlas Studies" under the "Quick select" for the purpose of querying KLHDC7B's genetic alteration characteristics, then entering "KLHDC7B". According to the module of "Cancer Types Summary", copy number alteration, mutation type and the frequency of alteration were exhibited across TCGA tumors. Also, through the "Mutation" module, you can view information about mutation sites in KLHDC7B's protein structure, as well as the schematic diagram of the 3D structure. After that we employed the module of "Comparison" to attain data about OS, PF (Progression-Free), DF (Disease-Free) and DFS in TCGA cancer of KLHDC7B. In Kaplan-Meier plots, P log-rank values and p values less than 0.05 were considered significantly. What's more, our study examined the KLHDC7B DNA methylation level of multiple probes in the TCGA dataset using the MEXPRESS website (https:// mexpr ess. be/) (Koch et al. 2015). We calculated beta, Pearson correlation coefficient (R), Benjaminite-Hochberg-adjusted p-value based on these results. Finally, we explored the relationship between the expression of DNA methyltransferase and KLHDC7B, including DNMT1, DNMT2 (TRDMT1), DNMT3B and DNMT3A by TIMER2.0 (Yan et al. 2021).Colors on the heatmap represent partial Spearman's rho adjusted for purity.  (Bonneville et al. 2017). In MMR, mismatched nucleotide bases are repaired to their correct locations through DNA repair (Dan et al. 2019). The uniformly normalized pancancer dataset was downloaded from TCGA Pan-Cancer at UCSC (https:// xenab rowser. net/) database, where the expression data of ENSG00000130487 (KLHDC7B) gene in each sample were extracted. We further selected the samples from Primary Blood Derived Cancer-Peripheral Blood, and Primary Tumors. To visualize the results, a Lollipop map was created using the web of http:// sange rbox. com/ Tool (Cui et al. 2021). Spearman's rank correlation test was carried out, and a p-value and a partial correlation (cor) value were also obtained. Moreover, based on TCGA expression profile data, we assessed the levels of MMR gene expression in different cancers, including MLH1(MutL homolog), MSH2(MutS homolog), EPCAM (epithelial cell adhesion molecule) and PMS2 (post-meiotic segregation increase) and to determine the relationship between KLHDC7B with MMR gene expression levels. The results were visualized as a heatmap and these colors indicated a partial Spearman's rho value that has been adjusted to take into account purity.

Analysis of KLHDC7B immune infiltration
With TIMER2.0, we examined the connection between immune infiltration and KLHDC7B expression across all tumor types in TCGA with CIBERSORT, xCell, TIMER, quanTIseq, EPIC, MCP-counter and CIBERSORT-ABS algorithms (Dan et al. 2019), which were applied to evaluate the immune infiltration data across all immune cells in tumors, including cancer-associated fibroblasts, monocytes, DCs (dendritic cells), mast cells, macrophages, regulatory T cells (Tregs), CD8 + T cells, CD4 + T cells, NK cells, NK T cells, follicular helper T cells, neutrophils, hematopoietic stem cells, common lymphoid progenitors, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, eosinophils, and myeloid-derived suppressor cells. P-values and sectional correlations were calculated using Spearman's rank correlation with pure adjustments. Then, a heatmap created to visualize the immune cells data and cancer-associated fibroblasts were plotted using scatterplot. Lastly, we used the online platform "SangerBox" (http:// sange rbox. com/ Tool) to examine the relationship between KLHDC7B expression and immune checkpoint genes in the tumor microenvironment (TME). It was considered statistically significant when the p-value was greater than 0.05.

KLHDC7B-related gene enrichment analysis
We visited STRING web server (https:// string-db. org/) by inputting the name of "KLHDC7B" and then selecting organism of Homo sapiens (Szklarczyk et al. 2015). The main parameters were set as following: meaning of network edges ("evidence"), active interaction sources ("experiments"), minimum required interaction score ["Low confidence (0.150)"] and max number of interactors to indicate (in the 1st shell, there is "no more than 50 interactors"). Then, a network of KLHDC7B-binding proteins was constructed using 50 available experimentally verified proteins and their interactions. With Cystoscope software (Shannon et al. 2003), we obtained the protein-protein interaction (PPI) network of these proteins. Subsequently, in the "Similar Gene Detection" module of GEPIA2, we retrieved the top 100 genes related to KLHDC7B expression from the TCGA project. A Pearson correlation analysis was performed on the paired genes to research the relationship between KLHDC7B and the chosen genes. Correlation coefficients (R) and p-values were displayed for each pair of genes in the Scatter plots with log2 (TPM) drawn. After that, the heatmap data of selected gene was created using the TIME2 module "Gene Cor". Following purity adjustments, heat maps are used to depict the P-values in Spearman's rank correlation tests as well as partial correlation values. We utilized the interactive Venn diagram viewer Jvenn (http:// bioin forma tics. psb. ugent. be/ webto ols/ Venn/) in intersection analysis to compare KLHDC7B correlating and interacting genes (Bardou et al. 2014). Afterwards, KEGG (Kyoto encyclopedia of genes and genomes) and GO (Geneontology) enrichment analyses were conducted to further investigate the functions of these genes. Concretely, data for the functional annotation charts was first obtained using the DAVID online tool. (Annotation, Visualization, Integrated Discovery and Database in https:// david. ncifc rf. gov). "ClusterProfiler" (version3.14.3) and "ggplot2" R packages (https:// www.r-proje ct. org/) (Yu et al. 2012) were used to visualize the results, with the two-tailed p value < 0.05 considered statistically significant.

KLHDC7B gene expression analysis
The aim of this research was to survey the oncogenic potential of human KLHDC7B (NP-612442.3 for protein or NM-138433.5 for mRNA, Fig. S1a). There is a conservation structure of the KLHDC7B protein among species. As shown in Fig. S1b, KLHDC7B usually composed of the Kelch-3 (cl02701) domain in species including H. sapiens, M. musculus, P. troglodytes, B. taurus, R. norvegicus and X. tropicalis. With the combination of the FANTOM5 (Function annotation of the mammalian genome 5), HPA and GTEx datasets, the expression pattern of KLHDC7B was examined in different cell types and non-tumor tissues.
As shown in Fig. S2, KLHDC7B is most expressed in the Parathyroid gland, followed by Thymus, kidney, and spleen. For detection, KLHDC7B mRNA is detectable among a variety of immune cells with a relatively low cell specificity (Fig. S2b).
Furthermore, KLHDC7B expression was compared among tumor pathological stages using the "Pathological Stage Plot" module of GEPIA2. As seen in Fig. 2b, there is a significant difference in KLHDC7B expression within the tumor stages of cancers including KIRC, PAAD, OV, STAD (Stomach Cancer), BRCA, ESCA, THCA, but not others (Fig. S3a). Moreover, we also identified differences expression of KLHDC7B in breast invasive carcinomas at varying stages, as showed in Table 1

Survival analysis data of KLHDC7B
We classified the cancer patients into high and low KLHDC7B-expressed groups and examined the correlation between KLHDC7B level and prognosis of patients across TCGA tumor types respectively. Higher expressed KLHDC7B was related to lower OS for cancers such as LGG (P = 0.011), KIRC (Kidney Clear Cell Carcinoma, P = 0.00033) (Fig. 3a). For DFS (Fig. 3b), high KLHDC7B expression negatively correlated with poor prognosis in GBM (P = 0.0016), STAD (P = 0.0075), and THYM a The expression status of the KLHDC7B gene in different cancers or specific cancer sub-types was analyzed through TIMER2. *P < 0 0.05; **P < 0 0.01; ***P < 0 0.001. b For the type of BLCA, DLBC, CESC, ESCA, HNSC, THCA, THYM and PAAD in the TCGA project, the corresponding normal tissues of the GTEx database were included as controls. the box plot data were supplied **P < 0 0.01 ◂ (P = 0.041). In addition, lower KLHDC7B level was associated with poor OS for SKCM (Melanoma, P = 9.4e-0.7) and poor DFS for HNSC ( Fig. 3b, P = 0.023) and SKCM (P = 0.041). Also, we analyzed survival data on other cancer types using the Kaplan-Meier plotter (http:// kmplot. com/ analy sis/) (Győrffy, 2021). The results indicated a correlation between low KLHDC7B expression and poor RFS (Fig. 4a, P = 4.31e−0.5) for breast cancer and a connection between low expression of KLHDC7B and poor gastric cancer prognosis in OS (P = 0.019), FP (0.013), and PPS (P = 0.005). On the other hand, higher KLH-DC7B expression was negatively associated with PFS (P = 0.0022) for ovarian cancer, FP (P = 0.0056) and OS (P = 0.00015) for Lung cancer (Fig. 4a). Our forest map data (Fig. S3c) was further visual the relationship between KLHDC7B expression and breast cancer, gastric cancer, lung cancer and ovarian cancer prognoses, but not liver cancer. On the basis of our results, we also investigated how KLHDC7B expression affects clinical outcomes in various cancers, including OS and RFS Based on the data above, various types of tumors express KLHDC7B differently and are associated with different prognoses (Győrffy 2021). More details about the correlation between  Table 2 Genetic alteration and DNA methylation analysis data of KLHDC7B Through cBioPortal, we detected KLHDC7B gene changes in several TCGA tumor cases, with the highest frequency of changes (> 8%) in ovarian serous cystadenocarcinoma by a predominant type of "Deep Deletion". Notably, the "Deep Deletion" composed of all types of the diffuse large B-cell lymphoma (with a frequency of > 4%), mesothelioma, testicular germ cell tumors, uveal melanoma, adrenocortical carcinoma, thyroid carcinoma and pheochromocytoma and paraganglioma (Fig. 5a). In Fig. 5b, we present the types, sites, and cases number of the KLHDC7B genetic alteration. The genetic alteration was primarily caused by missense mutations of KLHDC7B and R502W. R502W was induced by a frame shift mutation, translation from R (arginine) to W (tryptophan) at the site of 502 in KLHDC7B protein, and following the truncation of KLHDC7B. R502W was identified in Brain Lower Grade Glioma, Colorectal Adenocarcinoma and Stomach Adenocarcinoma, each has one case. In Fig. 5c, we show the 3D structure of KLHDC7B protein. Furthermore, we investigated whether genetic alterations of KLHDC7B were connected with clinical survival among cancers. The Fig. 5d indicates that all TCGA tumor cases without altered KLHDC7B showing favorable prognosis in terms of in disease-specific (P = 0.0128), disease-free (P = 3.241e−3), overall (P = 0.0780) and progression-free (P = 4.272e−4) survival, comparing to those with KLH-DC7B alteration.
Then, using TIMER2.0, we observed the relationship between KLHDC7B levels and DNA methyltransferases, including DNMT3B, DNMT3A, DNMT2 (TRDMT1) and DNMT1, as shown in Fig. 6a. The results revealed that DNA methyltransferases in LIHC were positively correlated with KLHDC7B expression, while in TGCT (Testicular Cancer) they were negatively correlated. We used MEXPRESS to investigate the potential relationship between DNA methylation and KLHDC7B in the TCGA project to investigate differential pathogeneses. The KLHDC7B DNA methylation and gene expression were significant negatively correlated within the KIRP(Kidney Papillary Cell Carcinoma) cases at multiple probes in both non-promoter region (such as cg 07,833,467 in KIRP, P < 0.001, R = − 0.658) and promoter region(such as cg11344005 in KIRP, P < 0.001, R = − 0.552),as shown in Fig S4. Moreover, we supervised to find that the probes cg07833467 were the greatest negative association with DNA methylation of KLHDC7B between HNSC (r = -0.511), KIRP (r = − 0.658), and BRCA (r = − 0.604) among multiple probes (Fig. S4).

TMB, MSI, and MMR analysis data of KLHDC7B
A significant correlation exists between KLHDC7B expression and TMB in eleven cancers, including GBMLGG (Glioma), LGG, LUAD, CESC, COAD, COADREAD (colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma), BRCA, SARC, UCEC (Endometrioid Cancer), HNSC, and BLCA (Fig. 6b). In contrast, KLHDC7B expression was negatively related to TMB in three cancers, including ESCA, KIPAN (Pan-kidney cohort KICH + KIRC + KIRP) and DLBC. Also, KLHDC7B and MSI were found to have similar relationships. The KLHDC7B gene was significantly related to ten types of cancer (Fig. 6c), among which the association was positive for four tumor types, including COAD, COADREAD, KIPAN, THCA, and negative with six tumor types, including GBMLGG, ESCA, LIHC, TGCT, CHOL (Bile Duct Cancer), and DLBC. It is noteworthy that the expression of KLHDC7B was negatively correlated with both TMB and MSI in DLBC, but the opposite was true in GBMLGG. TMB and MSI exact data can be found in Table 3. As for the MMR genes, a significantly positive association between KLHDC7B expression and LIHC, LUAD depending on 3 genes (MSH2, MSH6, and PMS2) (Fig. 6d).

Immune infiltration analysis data of KLHDC7B
Tumor-infiltrating immune cells were closely linked to the initiation, progression, or metastasis of cancer (Fridman et al. 2011;Steven and Seliger 2018). In tumor  S5). Furthermore, we found a significant positive association between KLHDC7B expression and CD8 + T-cells in tumors of SKCM-metastasis, SKCM, LUSC, KIRC, CESC (Fig. 3c,  d). Moreover, expression levels of KLHDC7B exhibited a statistically significant positively correlated and observed the estimated in filtration value of cancer-associated fibroblasts for the TCGA tumors of COAD, LGG, LUSC, READ, and STAD, but noted a negative correlation for CECS, BRCA, BRCA-Basal (Fig. 7). We next investigated the correlation between KLHDC7B level and 60 common ICP(immune checkpoint) gene expressions, including 36 stimulatory and 24 inhibitory. As shown in Fig. 3e, there is a positive correlation between KLHDC7B expression level and 60 ICP genes in most tumor types, including COAD, OV, LAML, UCEC, LGG. It has been suggested that KLH-DC7B coordinates the activity of specific ICP genes in the tumor immune microenvironment, which further confirmed the link between KLHDC7B and immune cells.

Enrichment analysis of KLHDC7B-related partners of KLHDC7B
To further understand the molecular mechanism underlying the role of KLHDC7B gene in oncogenesis, an enrichment pathway analysis was performed to identify the target KLHDC7B-correlated genes and the KLHDC7B-binding proteins. With the STRING tool, we identified 50 proteins that bind KLHDC7B, which were confirmed by experiments. We can see the interaction network of these proteins in Fig. 8a. The GEPIA2 tool was employed to combine TCGA tumor expression data to identify the top 100 genes that correlated with KLHDC7B expression. According to Fig. 8b, KLHDC7B expression was positively correlated with CTA-384D8.31 (R = 0.77), CTA-384D8.35 (R = 0.48), ETV7 (R = 0.39), AIM2 (R = 0.38), TAP2(R = 0.37), TYMP (R = 0.37) and TAP1 (R = 0.35) genes (all P < 0.001). A positive correlation was also shown between KLHDC7B and the top five genes in the corresponding heatmap, including ETV7, AIM2, TAP2, TYMP, TAP1 in most types of cancer (Fig. 9c). Based on the above two groups intersection analysis, EPHX3 was found to be a shared member of both (Fig. 9d). With the combined datasets, we performed GO and KEGG enrichment analyses, as well as GO and KEGG analyses with the KLHDC7B-related proteins. Figure 9 shows the top three significant terms in CCs (cellular components), BPs (biological processes) and MFs (molecular functions), as well as KEGG pathways. An overview of the GO and KEGG analyses can be found in Table 4. In the GO analysis, target proteins were mostly enriched in response to type I interferon, cellular responses to type 1 interferons, type I interferon signaling pathway, interferon-gammamediated signaling pathway in the BP enrichment analysis; As a result of the CC analysis, we identified the proteasome core complex, peptidase complex, endopeptidase complex and proteasome complex; adenylyl transferase activity, threonine-type peptidase activity threonine-type endopeptidase activity and double-stranded RNA binding in MF. Target proteins were significantly enriched in pathways related to Herpes simplex virus 1 infection, Epstein-Barr virus infection, NOD-like receptor signaling pathway, Influenza A antigen and processing and presentation according to KEGG analysis. The majority of these genes are associated with protein function and cell immunity, according to GO and KEGG enrichment analyses, which shows that tumorigenesis and development may be affected by KLHDC7B.

Discussion
The whole-genome analysis of human pan-cancer has provided new insights into tumorigenesis and mutations, copy number alterations, tumor purity and driver genes also revealed a close relationship between them, which plays an important role in diagnosing and treating cancers (Ma et al. 2018;Priestley et al. 2019;Saghafinia et al. 2018 hypermethylation and upregulation in breast cancer (Kim et al. 2010), and functional associations between KLHDC7B and some clinical diseases have been demonstrated (Papic et al. 2012;Praveen et al. 2022).In spite of this, it is largely unknown how it contributes to the development and progression of these cancers. We evaluated KLHDC7B expression across cancers for the first time, which is clinically relevant to tumor studies. Our objective was to explore the potential immunological function and prognostic value of KLHDC7B across 33 different cancer types. Therefore, our study examined 33 types of tumors for KLHDC7B expression and the related molecular properties of DNA methylation, genetic alterations, expression level, MMR, MSI and TMB in the GEO and TCGA database. We found different types of cancer and pathological stages of disease expressed KLHDC7B differently. Based on Kaplan-Meier survival analysis, we discovered that abnormal KLHDC7B expression may be a prognostic marker in some types of cancer, such as lung cancer, breast cancer, ovarian cancer and gastric cancer. Further, we predicted that the levels of KLHDC7B expressed within tumor microenvironments would be closely correlated. The KLHDC7B-related biological mechanisms were recognized using GO and KEGG analytic approaches. According to the findings of this investigation, KLHDC7B appears to play a key role in the immunization of tumors and might act as an essential biomarker.
To improve the chances of a cancer cure, cancer-specific target molecules should be explored to identify the genes that are expressed differently in tumors (Andre et al. 2014). Thus, it is valuable to analyze the expression differences and potential molecular mechanisms of KLHDC7B across cancer types. There is prior knowledge that KLHDC7B is able to adjust the expression levels of certain cancer-related genes across cancer types, which explains why KLHDC7B upregulation was observed in most cancer types. In this work, we observed that KLHDC7B was upregulated in 18 cancers. Interestingly, the expression of KLHDC7B in OV was also upregulated compared to adjacent normal tissues, however, as the tumor progressed, the expression level gradually decreased. In tumorigenesis, it has recently been discovered that KLHDC7B regulates breast and ovarian cancer development, as well as cervical cancer to lymph nodes (Guo et al. 2015;Khirade et al. 2015;Malouf et al. 2016;Zhang et al. 2014). What's more, our study showed that KLHDC7B expression levels vary in different pathological stages of tumors, and KLHDC7B expression levels were significantly different between grade 2 and grade 3 of breast invasive carcinoma, which showed KLHDC7B expression levels were the highest at grade 2. However, a recent study showed that KLHDC7B expression is grade-related and just increases remarkably in grade 3 tumors of breast cancer and found fascinating outcomes in grade 1 and 2 tumors, that in moderately-differentiated and well-differentiate tumors, KLHDC7B was downregulated. These different outcomes may be induced by differences in tumor phenotyping and samples, and suggests that KLHDC7B has a dual role in tumor progression and more studies are needed to analyze it in the future, and the tumor grade should be considered when KLHDC7B expression is used as a breast cancer marker, otherwise the diagnosis will be inaccurate. Although the sample size of our study may limit the understanding of KLHDC7B's role in breast cancer, the results of this study may contribute to an improved understanding and a larger sample size should be used in future studies.
Furthermore, based on Kaplan-Meier survival data from Affymetrix 2362285_at and 1552639_at microarrays (Györffy et al. 2010), in gastric cancer, high expression of KLHDC7B was related to poor OS, PPS and FP prognoses, which indicates that KLHDC7B has a great correlation with the survival prognosis of gastric cancer and is likely to be one of the targets for gastric cancer treatment in the future. Our study demonstrates time the relationship between survival prognosis and KLHDC7B expression in gastric for the first. Yet, an in-depth molecular analysis is still needed to confirm whether high KLHDC7B expression is crucial to tumor progression, or does it simply result from normal tissues resisting tumor growth. A previous study identified KLHDC7B as a promising biomarker for detecting BCa at an early stage (Huang et al. 2021). It is clear from these findings that KLHDC7B may be utilized as a biomarker to define the prognosis of cancer. Even though immunotherapy has shown increased therapeutic effects in tumors, little research has been done on the connection between tumor immunity and KLHDC7B expression. Therefore, through the use of TIMER2, we investigated the possible relationship between KLHDC7B expression and immune cell infiltration levels. Consequently, a positive correlation is detected between the KLHDC7B expression and the immune infiltration level of Tregs in THCA, BRCA, OV, PRAD, and READ; endothelial cell in COAD (Colon Cancer); NK cell in READ, CESC, and HNSC; follicular helper T cells in UVM (Ocular melanomas), STAD and KIRC. However, a negative  structure of KLHDC7B protein (c). We also analyzed the potential correlation between mutation status and overall, disease-specific, disease-free and progression-free survival of all TCGA tumors (d) using the cBioPortal tool most algorithms. This is evidence that fibroblasts associated with tumors play a significant role in tumor growth, metastasis, and prognosis (Houthuijzen and Jonkers 2018;Paauwe et al. 2018). Moreover, almost all cancers in our study exhibited a significant positive relationship between KLHDC7B expression level and immune checkpoint gene expression level. According to these evidences, KLHDC7B might play a crucial character in the immune response to tumors. As a result of MMR, the genome of normal cells retains its integrity and stability, which is mainly composed of MLH/PMS (MutLhomologs) (Fishel 2015) and MSH (MutS homologs). Mutations in tumor cells caused by a deficiency of MMR genes are accompanied by the presence of a biomarker called MSI (Russo et al. 2019). In previous studies, MMR deficiency and MSI have been shown to be highly sensitive in predicting many types of cancer and the MSI can also be used to evaluate the effectiveness and sensitivity of immunotherapy (Baretti and Le 2018;Dudley et al. 2016;Hause et al. 2016). Cancer cells can also be measured by TMB for the number of complete mutations they contain, which can be used as a new therapeutic target (Chan et al. 2019). Furthermore, it appears to be an effective method for predicting cancer patient prognosis and response to ICIs  (Baretti and Le 2018;Chan et al. 2019;Yarchoan et al. 2017). A novel predictor of tumorigenesis is DNA methylation, an epigenetic mechanism. Based on the previous studies, KLHDC7B has been demonstrated to be a oncology marker for epigenetic differences in laryngeal cancer (Guo et al. 2015) and breast cancer (Jeong et al. 2018). Based on this study, KLHDC7B expression was positively correlated with 3 MMR (MSH2, MSH6, PMS2) genes in LIHC, LUAD, and MSI in 10 cancers. Furthermore, they found that KLHDC7B expression was strongly related to TMB in 14 cancers and to DNA methyltransferases in 2 cancers (LIHC, TGCT). In addition, DNA methylation impaired KLHDC7B expression, affecting its cancer-related function. We detected a poor correlation between promoter and non-promoter DNA methylation and KLHDC7B expression in KIRP patients, indicating that KLHDC7B DNA methylation might affect KLHDC7B expression on KIRP progression. Besides, we found that aberrant KLHDC7B expression across cancers may contribute to the progression and development of cancer for the first time, possibly related to DNA methylation, MMR, MSI and TMB.
Using the CPTAC dataset, we first investigated KLH-DC7B's molecular mechanism in breast cancer, Lung adenocarcinoma, HNSC from a total protein perspective. As shown in these results, primary tumor tissues are more highly expressed than normal tissues in total KLHDC7B protein. Due to the lack of clinical data, we did not explore the molecular mechanism of KLHDC7B in cancer from a phosphoprotein perspective. Thus, further molecular studies will be needed to determine the potential role KLHDC7B phosphorylation plays in tumor initiation and progression. For an enrichment analysis, we integrated information about KLHDC7B expression-related genes and KLHDC7Bbinding components across all tumor types and identified the possible role that proteins and the immune response of cells may play in cancer pathogenesis and etiology. Our KEGG and GO enrichment analyses indicated that the majority of the 50 KLHDC7B-binding proteins and the top 100 KLHDC7B-collecting genes are related to protein function and the immune response of cells, which provides a further explanation for the role played by KLHDC7B.
The expression landscape of KLHDC7B in human pancancer is shown in this study, indicating for the first time that KLHDC7B may serve as a predictor for tumor immunity and revealing that KLHDC7B is associated with immune therapy. The study, however, has some limitations: first, a variety of databases were used for the analysis, which may have resulted in systematic deviations. Due to a lack of available data, survival analysis was not assessed in several cancers, such as liver cancer. Therefore, in the future, it will be necessary to conduct more detailed and specific studies. Second, with the use of bioinformatic tools, the research examined KLHDC7B expression in human pan-cancers and its prognosis. While performing in vitro/in vivo experiments simultaneously may be challenging, a clinical trial or even an experiment will be more persuasive. A bioinformatic analysis of the data from this study can be used to conduct future studies investigating the precise mechanism of KLHDC7B at molecular or cellular levels. But it is unclear exactly how KLHDC7B affects tumor immunity and prognosis in patients. Also, it may be possible to provide a more convincing viewpoint on this topic in the future by looking at tumor immunity and KLHDC7B expression in specific cancers. In spite of this, the study results provided valuable insights into the prognostic and immunological features of KLHDC7B in human pan-cancer patients. We can take advantage of the research and investigate the exact mechanisms by which KLHDC7B impacts the immunology and survival outcomes of multiple cancers in future studies.