Comprehensive pan‑cancer analysis of Potassium Voltage-Gated Channel Q4 (KCNQ4 ) gene expression in human malignant tumors

doi:10.21203/rs.3.rs-2969115/v1

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Comprehensive pan‑cancer analysis of Potassium Voltage-Gated Channel Q4 (KCNQ4 ) gene expression in human malignant tumors

https://doi.org/10.21203/rs.3.rs-2969115/v1

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Background: Malignant tumor is a serious threat to human health and has become the disease with the highest mortality in the world. A large number of studies have shown that KCNQ4 is responsible for nonsyndromic hearing loss, but less has been done on KCNQ4 in cancer, so we analyzed the expression of KCNQ4 gene in different tumor tissues and their different pathological stages, the prognosis, mutation, immune infiltration, immune checkpoint related genes and related pathways were observed. The role and function of KCNQ4 in cancer were revealed through systematic and comprehensive pan-cancer analysis.

Method: The bioinformatics were analyzed by mining databases available to the public and by using R software. Moreover, immunohistochemistry and molecular biology experiments were carried out to prove the cancer-inhibiting effect of KCNQ4 in pan-cancer.

Results: The results showed that KCNQ4 expression was lower than normal tissues in most tumors,and higher in a few tumors. The expression of KCNQ4 gene was closely related to the prognosis of patients with different types of tumors. The expression of KCNQ4 was significantly correlated with different pathological stages in papillary renal cell carcinoma, mixed renal cell carcinoma, hepatocellular carcinoma, rectal adenocarcinoma, pancreatic carcinoma, ovarian serous cystadenocarcinoma and bladder urothelial carcinoma. The mutation rate of KCNQ4 was the highest in patients with cutaneous melanoma.KCNQ4 is significantly associated with immune infiltration and immune checkpoint related genes in most tumors.KCNQ4,stemness analysis shows a positive correlation in most cancer species. KCNQ4 and related genes were significantly enriched in multiple pathways. Moreover, immunohistochemical and molecular biological experiments verified that KCNQ4 has less expression in breast cancer tissues and has anti-tumor effect in breast cancer.

Discussion: Our comprehensive pan-cancer analysis of KCNQ4 proved its potential signature role in cancer，Moreover，KCNQ4 could be used as an auxiliary prognostic marker or as a marker for immunotherapy in some tumor types.

pancancer

KCNQ4

immune infiltration

prognosis

bioinformatics

Malignant tumors pose a serious threat to human health and have become the disease with the highest mortality rate in the world^[1] .Although chemotherapy and immunotherapy prolong patients 'overall survival and quality of life, tumors often recur due to the inefficiency of long-term antitumor responses^{[2, 3]}.Therefore, it is necessary to investigate the pan-cancer analysis of genes in malignant tumors and their role in the corresponding cancer species.

Potassium voltage-gated channel subfamily Q Member 4(KCNQ4) is a homologous tetramer, which is located in the LP34 region of human chromosome 1, it consists of six Transmembrane domain, one pore region and two intracellular domains^[4]. As one of the most commonly mutated genes, KCNQ4 encodes a protein that forms a potassium channel that was originally thought to play a key role in regulating neuronal excitability, particularly in the sensory hair cells of the cochlea^[5–8]. It was later confirmed that KCNQ4 plays an important role in the occurrence and development of human malignant tumors. For example, abnormally low expression in breast cancer, prostate cancer, can inhibit tumor invasion, metastasis and other biological processes. Researchers have found that the up-regulation of KCNQ4 by tumor suppressor gene BC069792 positively correlated with the progression of breast cancer^[9]. Still another study, conducted by other researchers, KCNQ4 is significantly down-regulated in prostate cancer compared with normal tissue and is associated with better prognosis^[10].However, there is no pan-cancer analysis of KCNQ4, public databases such as TCGA (Cancer Genome Atlas) provides functional genomics information for different cancers (Table 1),which allows us to perform pan-cancer analysis on any gene of interest. In this work we used multiple databases to investigate the expression level, gene mutation, prognosis, immune infiltration, stem nature of malignant tumors, and related pathway analysis of KCNQ4 in human malignant tumors. The main objective was to reveal the potential function of KCNQ4 by bioinformatics in a variety of tumors and to verify that KCNQ4 may influence the development of breast cancer, which provides a new idea for taking KCNQ4 as a new therapeutic target for cancer.

Gene expression level analysis. The Cancer Genome Atlas (TCGA),Genotype-Tissue Expression (GTEx) and the “Gene DE” module of Tumor Immune Estimation Resource version 2 (TIMER2) (http://timer.cistrome.org/) were extracted pan-cancer data sets to analyse the expression levels of KCNQ4 in various tumor samples and normal tissues and in different tumor stages^[11-13].The expression data of the ENSG0000117013(KCNQ4) gene in various samples were extracted, log2(x + 0.001) transformation was performed for each expression value and species.Then we excluded less than 3 samples in a single cancer. Finally, the expression data of 34 cancer species were obtained.

Pathological staging analysis. We extracted the expression data of KCNQ4 gene from the UCSC database and further screened samples derived from Primary Blood Derived Cancer-Peripheral Blood, Primary Tumor, further log2(x + 0.001) transformations were performed for each^[14] expression value that we obtained expression data for 30 species. Expression differences of genes in different clinical stage samples in each tumor were calculated using R software (version 3.6.4) , and difference significance analysis between pairings was performed using unpaired Student's t-Test, analysis of variance was used to test for differences among groups of samples.

Genetic alteration analysis. The cBioportal database (https://www.cbioportal.org/) integrates research data from different tumor genomes,from which the visualization and analysis of operational assumptions can be extracted^[15].Through this database,we analyzed the protein structure, mutation type, mutation site information, copy number change (CAN), and the three-dimensional structure frequency“TCGA Pan-Cancer Atlas Studies” was chosen as the cohort. Next, we typed “KCNQ4” into the “Query” module. Alteration sites, types, and numbers forKCNQ4 can be found in the “cancer type summary” and “mutation” modules. Additionally, using the GDC database (https://portal.gdc.cancer.gov/) and MuTect2 software16, we downloaded, processed, and integrated the Simple Nucleotide Variation dataset of all TCGA samples. The structural domain information of the protein was obtained from the R package maftools (version 2.2.10)17. The result was depicted using a lollipop plot. A chi-square test was also performed to examine the frequency of mutations in different cancer types in two groups that were separated by BUB1B expression level (high or low).Finally，in two different databases were intersected the top 10 cancer species with high mutation frequency to analyze the genetic changes of KCNQ4 in different cancer species. Expression levels of KCNQ4 in normal tissues and cancer cell lines identified via the BioGPS database（http://biogps.org/#goto=welcome）.

Survival prognosis analysis. We obtained database from TCGA and GTEx to get high-quality prognostic datasets of different cancer species and from UCSC to get target follow-up data as supplement and samples with follow-up time < 30d were excluded. Cox regression models were constructed using the coxph function of the R software package survival ( version 3.2-7) to analyze the relationship between gene expression and prognosis in each arsinoma, and statistical tests for OS (overall survival) , DSS (disease-specific survival) ,PFI (progression-free) and DFI(disease-free interval) were performed using the log-rank test, were extracted from a published paper^[16].

Immune infiltration analysis

TIMER^[17],CIBERSOR^[18],EPIC, IPS, MCPcounter, Xcell, CIBERSORT,QUANTISEQ algorithms were used to explore correlation between KCNQ4 and immune cell infiltration. We extracted KCNQ4 expression data from UCSC and screened samples from Primary Blood Derived Cancer-Peripheral Blood (TCGA-LAML) , Primary Tumor, Metastatic from TCGA-skcm, Primary Blood Derived Cancer-Bone Marrow, Primary Solid Tumor, Recurrent Blood Derived Cancer-Bone Marrow, the expression profile was mapped onto GeneSymbol. Then, R software package (version 0.99.9) was used to assess the immune cell infiltration score of each patient in each tumor to determine significantly correlated immune infiltration scores.In addition,different algorithms were used to explore the potential correlation between the expression level of the KCNQ4 gene and the infiltration level of cancer-associated fibroblasts across all types.

Immune checkpoint analysis.

UCSC was used to extracted to obtain expression data of KCNQ4 and 60 marker genes of two immune checkpoint pathways (Inhibitory(24), Stimulatory(36)) in each sample, we calculated markered pearson correlations between KCNQ4 and marked genes of five immune pathways were calculated, which were extracted from a published literature^[19].

STEMNESS index analysis.

UCSC was used to extracted to obtain expression data of KCNQ4, DNAss tumor stemness score was calculated by methylation signature including DNAss and RNAss. We integrated the STEMNESS index and gene expression data of the samples and excluded those with less than 3 samples in a single cancer species, we ultimately got expression data for 37 cancer species .Then, we intersected KCNQ4 expression data with RNAss or DNAss to calculate the Spearman correlation coefficient. The ggplot2 R package were visualized with a lollipop plot ,which were extracted from a published literature^[20].

Cancer heterogeneity analysis.

UCSC was used to extracted to obtain expression data of KCNQ4，we screened samples from Primary Blood Derived Cancer - Peripheral Blood、Primary Tumor. In addition, GDC was treated by MuTect2 software to obtaine Simple Nucleotide Variation dataset of level4 for TCGA samples. Finally, maftools R package (version 2.8.05) was used to calculate TMB（Tumor Mutational Burden）, and the MSI(Microsatellite Instability) score was extracted from a published article^[21].

Protein network and related gene enrichment analysis.

GENEMANIA (http :/genemania. org/) is a gene-gene interaction prediction software for generating hypotheses about gene function, it can analyse gene lists, and prioritises genes for functional determination[15]. STRING (https://string-db.org/) integrates all known and predictes associations between proteins, including physical interactions and functional associations[16] . DAVID (https://david.ncifcrf.gov/) provides systematic and comprehensive biological function annotation information for large-scale gene and protein lists to help users extract biological information from them. GEPIA2 was used to assess the association of KCNQ4 with selected target genes.We used GENEMANIA database to find genes with similar functions to KCNQ4 according to genomics and proteomics data, downloaded interacting proteins from STRING, and obtained GO including Biological Process（BP），Cellular Component（CC)，Molecular Function（MF）and KEGG enrichment analysis results from DAVIID official website. The PPI network of KCNQ4 was plotted using the Cytoscape software tool CytoHCA plug-in. In addition, GEPIA also used to analse KCNQ4 and the first quarter genes including GNAQ, AKT1,KCNJ14, JAK2, GNAO1, CREB1, MAPK3, PRKACA, MAPK1, PRKACB, AKT3, PRKCA, AKT2, KRAS, HRAS and ADCY6 in PPI network.

Expression analysis of KCNQ4 mRNA.

To study the association of KCNQ4 with malignancy, we exclude cancer species with < 3 samples in a single cancer species to analyze mRNA expression levels of KCNQ4 in different cancers and normal tissues from different databases. The TCGA database result showed that KCNQ4 was significantly up-regulated in 3 tumors such as LIHC, PCPG, CHOL and significantly down-regulated in 13 cancers such as CESC, COAD, COADREAD, BRCA, KIRP, KIPAN, PRAD, UCEC, HNSC, LUSC, THCA, READ, BLCA relative to normal tissues (Figure 1A). Meanwhile we used TIMER 2.0 database, which is based on TCGA database to verify the expression of KCNQ4 in different cancers (Figure 1C).However, due to the lack of normal tissue mRNA-seq data in the TCGA database, so we extracted normal tissue data from the GTEX data set and compared it with TCGA cancer. The result showed that KCNQ4 was significantly up-regulated in 6 cancers such as WT, PAAD, ALL, LAML, PCPG, CHOL, significantly down-regulated in 25 cancers such as GBM、GBMLGG、LGG、UCEC、BRCA、CESC、LUAD、ESCA、STES、KIRP、KIPAN、COAD、COADREAD、PRAD、STAD、HNSC、LUSC、LIHC、SKCM、BLCA、THCA、READ、TGCT、UCS、ACC (Figure 1B) . Overall, KCNQ4 is low expression in most cancer types via the analysis of the data from TCGA and GTEx. In addition, BioGPS database was used to identify the expression of KCNQ4 in normal tissues and cancer cell lines.We displayed 15 normal tissues with the highest expression level of KCNQ4(Figure3D) and 15 cancer cell lines with the highest expression level of KCNQ4 was depicted(Figure1E).and the result showed that KCNQ4 was expressed at relatively low levels in cancer cell lines (Figure. 1D, 1E). The above results indicate that KCNQ4 expression level is higher in immune cells and relatively low in cancer cell lines.

Expression analysis of KCNQ4 pathological stages in different cancer types

UCSC database was used to analyze the expression levels of cancers in different pathological stages in the cancer species with less than 3 samples in a single cancer species were excluded. It can be found that the expression data of 30 cancer species. We observed that there were 7 cancers are statistically significant differences such as KIRP, KIPAN, LIHC, READ, PAAD, OV, BLCA (Figure2).

Genetic alteration analysis of KCNQ4

The main type of change in KCNQ4 in different types of cancers is "amplification", followed is "mutation"(Figure3A).The frequency of KCNQ4 alterations (>8%) was highest in OV and the predominant type of alteration was "amplification". Through mutation type, mutation site information of KCNQ4（Figure3B）,we found that in the BLCA and ESCA cases with amplification as the main type, the frequency of KCNQ4 changes was the second highest and the third highest respectively (>4%). KCNQ4 mutation frequency is highest in SKCM. "Amplification" is the only variant form in SARC. The only variant of PCPG is "Deep deletion". But the location of the genetic changes seems to be sporadic. For example, the truncation mutation of R589L change in the Synaptobrevin domain waere found only in 2 patients with SKCM, R589W change was found in only one patient with IDC,D593Tfs*23 change was only found in 1 patient with SSTAD and the R589W change was only found in 1 patient with IDC(Figure3C,3D). The top 10 high-frequency mutations in the cBioportal database were SKCM（3.38 %），UCEC（3.02 %），COAD（2.02%），LUAD（1.77%），UCS（1.75%），ESCA（1.64%），STAD（1.36%）,MESO（1.15%）,HNSC（1.15%）,ACC（1.10%）and in the GDC database were COAD（4.30%），UCS（3.50%），COADREAD（3.20%），ESCA（2.20%），SKCM（2.00%），LUAD（1.80%），UCEC（1.70%），KICH（1.50%），ACC（1.30%）,MESO（1.20%） respectively (Figyre3E) . Through the two databases described above, we used a chi-square test to assess differences in gene mutation frequency across tumor types (UCS, Coad, Esca, UCEC, SKCM, ACC, LUAD, and Meso) shared by the top 10 high-frequency mutated cancer species(Figure4).

Survival prognosis analysis of KCNQ4.

KCNQ4 was analysed by OS,DSS,DFI and PFI, it can be found that KCNQ4 was high expression with poor prognosis in 9 cancer types such as GBMLGG，LGG，KIPAN，LIHC，SKCM， SKCM-M、ALL，ACC、ALL-R through OS analysis(Figure5A).KCNQ4 was high expression with poor prognosis in 6 cancer types such as GBMLGG，LGG、KIRP、KIPAN、SKCM、ACC through DSS analysis(Figure5B). KCNQ4 was high expression with poor prognosis in 3 cancer types such as KIRP、KIPAN、HNSC through DFI analysis(Figure5C). KCNQ4 was high expression with poor prognosis in 5 cancer types such as GBMLGG,LGG,KIRP,KIPAN,ACC through PFI analysis(Figure5D). Together, these results provide important insights into the prognosis of KCNQ4 in several specific cancers.

Immun infiltration analysis of KCNQ4.

KCNQ4 was analysed through different methods such as CIBERSORT,EPIC，IPS, MCPcounter, QUANTSEQ, TIMER, xCELL to study the collection KCNQ4 and Immun cell infiltration. We found that KCNQ4 expression was significantly correlated with immune infiltration in 40 cancer species such as GBM、GBMLGG、LGG、UCEC、LAML、BRCA、CESC、LUAD、ESCA、STES、SARC、KIRP、KIPAN、COAD、COADREAD、PRAD、STAD、HNSC、KIRC、LUSC、THYM、LIHC、SKCM-P、SKCM、BLCA、SKCM-M、THCA、NB、MESO、OV、UVM、PAAD、TGCT、UCS、LAML、ALL、PCPG、ACC、ALL-R、KICH by CIBERSORT method(Figure6A).Through EPIC method, we found that KCNQ4 expression was significantly correlated with immune infiltration in 36 cancer species such as GBM、GBMLGG、LGG、LAML、BRCA、CESC、LUAD、ESCA、STES、SARC、KIRP、KIPAN、COAD、COADREAD、PRAD、STAD、HNSC、KIRC、LUSC、LIHC、WT、SKCM、BLCA、SKCM-M、THCA、NB、READ、OV、UVM、TGCT、UCS、LAML、ALL、ALL-R、KICH、CHOL(Figure6B).Through IPS mehod, we found that KCNQ4 expression was significantly correlated with immune infiltration in 31cancer species such as LGG、BRCA、CESC、LUAD、STES、SARC、KIRP、KIPAN、COADREAD、PRAD、STAD、HNSC、KIRC、LUSC、LIHC、SKCM-P、SKCM、BLCA、SKCM-M、THCA、TARGET-NB、MESO、OV、PAAD、TGCT、UCS、LAML、PCPG、ACC、DLBC、KICH(Figure6C). Through MCPcounter method, we found that KCNQ4 expression was significantly correlated with immune infiltration in 40 cancer species such as GBM、GBMLGG、LGG、LAML、BRCA、CESC、LUAD、ESCA、STES、SARC、KIRP、KIPAN、COAD、COADREAD、PRAD、STAD、HNSC、KIRC、LUSC、LIHC、WT、SKCM-P、SKCM、BLCA、SKCM-M、THCA、NB、MESO、READ、OV、UVM、PAAD、TGCT、UCS、LAML、ALL、ACC、ALL-R、DLBC、CHOL(Figure6D).Through QUANTSEQ method, we found that KCNQ4 expression was significantly correlated with immune infiltration in 41 cancer species such as GBM、GBMLGG、LGG、UCEC、LAML、BRCA、CESC、LUAD、ESCA、STES、SARC、KIRP、KIPAN、COAD、COADREAD、PRAD、STAD、HNSC、KIRC、LUSC、THYM、LIHC、TARGET-WT、SKCM-P、SKCM、BLCA、SKCM-M、THCA、NB、READ、OV、PAAF、TGCT、UCS、LAML、ALL、PCPG、ACC、ALL-R、DLBC、KICH (Figure6E). Through TIMER method, we found that KCNQ4 expression was significantly correlated with immune infiltration in 34 cancer species such as BLCA， BRCA、CESC、CHOL、COAD(、COADREAD、ESCA、GBM、GBMLGG、HNSC、KICH、KIPAN、KIRC、KIRP、LGG、LIHC、LUAD、LUSC、OV、TCGA-PAAD、PRAD、READ、SARC、SKCM-M、SKCM-P、SKCM、STAD、STES、TGCT、THCA、THYM、UCEC、UCS、UVM (Figure6F). Throug xCELL method, we found that KCNQ4 expression was significantly correlated with immune infiltration in 44 cancer species such as GBM、GBMLGG、LGG、UCES、LAML、BRCA、CESC、LUAS、ESCA、STES、SARC、KIRP、KIPAN、COAD、COADREAD、PRAD、STAD、HNSC、KIRC、LUSC、THYM、LIHC、WT、SKCM-P、SKCM、BLCA、SKCM-M、THCA、NB、MESO、READ、OV、UVM、PAAD、TGCT、UCS、LAML、ALL、、PCPG、ACC、ALL-R、DLBC、KICH、CHOL (Figure6G).In addition, through the study of KCNQ4 and cancer-related fibroblasts, we found that there were negatively correlation with them in BLCA,BLCA-LumA,CESC,HNSC,HNSC-HPV-,KIRC,PRAD and STAD（Figure7）

Immune checkpoints analysis of KCNQ4

we performed correlation analyses between KCNQ4 expression and immune checkpoint-associated genes in tumors. The results showed that for most types of cancer, there was a significant correlation between KCNQ4 expression and the levels of immune checkpoint-associated(Figure8A).

Correlations analysis of KCNQ4 between and DNAss /RNAss

Cancer stem cells have been one of the most important targets in the field of cancer research, and their stem characteristics affect the occurrence, treatment resistance and recurrence of cancer^[24]. Previous studies have shown that stemness index is related to immune infiltration in the tumor microenvironment^[20]. The potential relationship between stemness index and immune cells was considered,KCNQ4 was analysed in each tumor via DNAss and RNAss STEMNESS scores that calculated by methylation signature. We found that KCNQ4 was positively correlated with GBMLGG, LGG, CESC, LAML, BRCA, KIRP, KIPAN, Prad, HNSC, LUSC, PCPG, CHOL, DLBC and negatively correlated in ESCA, SARC, LIHC and BLCA via DNAss(Figure8B).KCNQ4 was positively correlated with CESC、BRCA、UCEC、OV and negatively correlated in GBMLGG、LGG、LUAD、STES、KIPAN、STAD、PRAD、HNSC、KIRC、LIHC、THCA、MESO、BLCA via RNAss（Figure8C）.

Correlations analysis of KCNQ4 between and TMB/MSI.

Tumor heterogeneity plays an important role in clinical immunotherapy^[14]，so that TMB and MSI of KCNQ4 were analysis. We found that KCNQ4 was positively correlated in ACC and KICH, and negatively correlated in KIRP via TMB analysis (Figure9A). KCNQ4 was positively correlated in LGG、LUSC、READ、BLCA、ACC, and negatively correlated in LAML、PAAD、DLBC via MSI analysis(Figure9B). KCNQ4 was positively correlated in LGG、LUSC、READ、BLCA、ACC, and negatively correlated in LAML、PAAD、DLBC via MSI analysis(Figure9C).

Enrichment analysis of KCNQ4

Previous findings suggest that DEF6 expression may play a key role in cancer, and using enrichment analysis we expect to elucidate the pathways and activities. We utilized the GENEMINA database to screen genes with which it might interact (Figure10A) . String is used to download related proteins and then we plotted PPI protein network interaction map (Figure10B). Conduct GO(Figure10C) and KEGG enrichment(Figure10D) analysis by combining the possible interacting genes mentioned above ,we found these genes are related to the Biological Process (BP) , such as potassium transport, regulation of ion transport and calcium transport.Cellular Component (CC) is related to plasma membrane, membrane assembly and voltage-gated potassium channel complex.The Molecular Function (MF) is related to voltage-gated potassium channel activity, delayed rectifier potassium channel activity and ion channel binding.KEGG enrichment analysis showed that these genes were associated with cholinergic synapses, cGMP-PKG signaling pathway, axonal guidance, salivary secretion, and circadian entrainmen. GEPIA2 was used to assess the association of KCNQ4 with selected target genes(Figure11).

Cancer is originated from a disease that accumulates genetically mutated somatic cells in the human body. It grows uncontrollably and invades adjacent tissues. Sometimes it is transferred or spread to other parts of the body through lymph or blood^[25].The mutation of cells changes their phenotype, allowing them to escape the steady-state regulation of maintaining a normal number of cells. Nowadays, cancer has become one of the most concerned public health problems in the world. Although chemotherapy and immunotherapy prolong the overall survival and quality of life of patients, tumors tend to recur due to the inefficiency of long-term anti-tumor response[^{[26, 27]}. Therefore, it is particularly important to find new targets for diagnosis and treatment.

In this study, we utilized different databases such as Oncomine，TCGA and GTEx for a comprehensive pan-cancer analysis to provide a rationale for KCNQQ4 as a broad-spectrum immunotherapeutic target in oncology. There is no literature to analyze the role of KCNQ4 in pancarcinoma from a holistic perspective. At present, there is no literature to analyze the role of KCNQ4 in pan-cancer from a holistic point of view. In this study, we used the database of Oncomine, to analyze the expression level, mutation frequency, prognosis, immune infiltration and immune checkpoint of KCNQ4 in pan-cance tissues for the first time. KCNQ4 was found to be low-expressed in most tumors and high-expressed in a few tumors. It has been demonstrated that KCNQ4 can be upregulated by BC069792 to inhibit the activity of JAK2 and p-AKT. It also plays a role in inhibiting breast cancer growth[9]. However, survival prognostic analysis for KCNQ4 showed that high expression of KCNQ4 in 9 types of tumors was associated with poor prognosis. These include GBMLGGHR, LGG, Kipan, LIHC, SKCM, SKCM-m, TARGET-ALL, ACC, TARGET-ALL-R. This needs to be verified by further molecular biology experiments.

The special biological characteristics of tumor microenvironment and various biological factors bring opportunities and challenges for the treatment of tumor. In this special environment,Immune infiltration plays an important role in the development and prognosis of tumor^[28].

Tumor cells and immune cells regulate each other in the tumor microenvironment, and the diversity is also different in different types of cancer, where immune cells are closely related to immune checkpoints and the treatment of their cancers^{[29, 30]}.In this study, we also investigated the relationship between KCNQ4 expression and immune cells and immune cell checkpoints. The results showed that KCNQ4 was significantly correlated with immune infiltration score in most tumors, with a positive correlation in 11 types of tumors, namely LGG, STES, KIRP, KIPAN, PRAD, STAD, HNSC, THYM, LIHC, BLCA, THCA. Negative correlation was found in 13 tumors, namely GBM, UCEC, TARGET-LAML, BRCA, CESC, SARC, LUSC, SKCM, SKCM-M, NB, OV, ALL, ALL-R. We further explored the relationship between KCNQ4 expression and immune checkpoint-related genes and found that it was associated with immune genes in tumors, which also required clinical validation due to the different characteristics of different tumors; It also provides a new idea for tumor immunotherapy and further establishes the value of KCNQ4 in tumor diagnosis and treatment.

Cancer stem cells have characteristics associated with normal stem cells, and can give rise to all cell types in a given cancer sample. These cells usually have a stronger ability to proliferate, invade and metastasize, leading to cancer recurrence and treatment resistance, especially with cancer metastasis, can produce new types of cancer^[31]. Understanding the effect of cancer cell stemness on cancer initiation, proliferation, metastasis and drug resistance will greatly improve the clinical treatment of cancer. Therefore, in different cancer types ,KCNQ4 was also performed a stemness correlation analysis. The results showed that KCNQ4 showed significant positive correlation in 13 tumors, including GBMLGG, LGG, CESC, LAML, BRCA, KIRP, KIPAN, PRAD, HNSC, LUSC, PCPG, CHOL, DLBC. There was a significant negative correlation in 4 kinds of tumors, including ESCA, SARC, LIHC, BLCA.

Gene enrichment analysis can effectively reveal the relationship between biological processes and gene expression profiling (RF),it can provides a reliable basis for researchs. In order to identify pathway enrichment across the affected sample groups, we performed GO and KEGG enrichment analyses for KCNQ4-related genes. It was found that KCNQ4 was enriched in potassium ion transmembrane transport, cholinergic synapse and other signaling pathways, which provided some evidence for subsequent molecular biology research. It is also expected to be an effective method for biological researchers and clinicians to identify and diagnose specific diseases.

This study KCNQ4 was explored the expression and prognosis of in human malignant tumors. Many studies have shown that KCNQ4 is a member of voltage-gated potassium channel, and the decrease of KCNQ4 activity caused by gene mutation is the cause of non-syndromic hearing loss^[32]. In the nervous system, the KCNQ4 subunit is present in a subpopulation of cholinergic neurons in the peduncular pontine nucleus, suggesting that it contributes to the regulation of the sleep-wake cycle^[33]. Furthermore, studies in malignancies have shown that KCNQ4 in prostate cancer is associated with death and enriched in immune and cancer-related pathways, promising as a potential biomarker for informative organisms predicting risk of death^[34]

For example, in breast cancer and prostate cancer,KCNQ4’s expression in normal tissues was significantly higher than that in tumor tissues.The results suggest that KCNQ4 is a tumor suppressor gene. This also verified the expression of KCNQ4 in pan-cancer analysis compared with normal breast cancer, prostate cancer, of course, need further clinical and cellular experiments to verify.

In summary, through a pan-cancer analysis, we found that KCNQ4 was widely differentially expressed between tumor and normal tissues and revealed a correlation between KCNQ4 expression and clinical prognosis in some tumor types.KCNQ4 gene alterations, including mutations, duplications,and amplifications, were identified in a wide variety of cancer types.KCNQ4 expression showed a significant association with the infiltration of some immune cells into the tumor microenvironment and cancerr-related fibroblasts multiple cancers. Therefore,KCNQ4 may be a potential diagnostic biomarker and predictive biomarker of immunotherapeutic responses.

Acknowledgements

The authors acknowledge Dr. Zhang yunxiang for providing R language support.

Author contributions

ZQ contributed to drafting the manuscript and collected the data and performed the data analysis to this article. ZYX revised the manuscript. All authors read and approved the fnaL manuscript.

Funding

This study was supported by Weifang Science and Technology Development Plan Project (2022YX001).

Availability of data and materials.

The datasets analyzed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), GTEx (https://www.gtexportal.org/home/index.html), UCSC database(http://genome.ucsc.edu/), GEO (https://www.ncbi.nlm.nih.gov/geo/), TIMER2(http://timer.cistrome.org/), cBioportal database (https://www.cbioportal.org/), GDC database (https://portal.gdc.cancer.gov/),BioGPS database(http://biogps.org/#goto=welcome),GENEMANIA (http :/genemania.org/), STRING（https://string-db.org/）, DAVID（https://david.ncifcrf.gov/）and GEPIA 2(http://gepia.cancer-pku.cn/index.html).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no conficts of interes.

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TABLE 1 Cancer abbreviations and the corresponding full name of abbreviations.

ACC	Adrenocortical carcinoma
BLCA	Bladder Urothelial Carcinoma
BRCA	Breast invasive carcinoma
CESC	Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL	Cholangiocarcinoma
COAD	Colon adenocarcinoma
COADREAD	Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma
DLBC	Lymphoid Neoplasm Diffuse Large B-cell Lymphoma
ESCA	Esophageal carcinoma
FPPP	FFPE Pilot Phase II
GBM	Glioblastoma multiforme
GBMLGG	Glioma
HNSC	Head and Neck squamous cell carcinoma
KICH	Kidney Chromophobe
KIPAN	Pan-kidney cohort (KICH+KIRC+KIRP)
KIRC	Kidney renal clear cell carcinoma
KIRP	Kidney renal papillary cell carcinoma
LAML	Acute Myeloid Leukemia
LGG	Brain Lower Grade Glioma
LIHC	Liver hepatocellular carcinoma
LUAD	Lung adenocarcinoma
LUSC	Lung squamous cell carcinoma
MESO	Mesothelioma
OV	Ovarian serous cystadenocarcinoma
PAAD	Pancreatic adenocarcinoma
PCPG	Pheochromocytoma and Paraganglioma
PRAD	Prostate adenocarcinoma
READ	Rectum adenocarcinoma
SARC	Sarcoma
STAD	Stomach adenocarcinoma
SKCM	Skin Cutaneous Melanoma
STES	Stomach and Esophageal carcinoma
TGCT	Testicular Germ Cell Tumors
THCA	Thyroid carcinoma

No competing interests reported.

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Comprehensive pan‑cancer analysis of Potassium Voltage-Gated Channel Q4 (KCNQ4 ) gene expression in human malignant tumors

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