Medicine ®
OPEN
A comprehensive investigation on pan-cancer impacts of constitutive centromere associated network gene family by integrating multi-omics data
A CONSORT-compliant article
Huimei Su, MSª, Yuchun Fan, MSª, Zhuan Wang, MSª, Lihe Jiang, PhDa,b,c,* İD
Abstract
Background: The constitutive centromere associated network (CCAN) complex played a critical role in connecting the centromere with the mitotic spindle during mitosis and meiosis. Many studies have indicated that CCAN is related to the tumorigenesis and cancer development. Nonetheless, the overview of CCAN gene family in pan-cancer remain incompletely understood.
Methods: We performed a comprehensive investigation on pan-cancer impacts of CCAN by integrating multi-omics data. We comprehensively investigated the expression profile, kyoto encyclopedia of genes and genomes (kegg) pathway, mutation, copy number variation, tumor microenvironment, immune cells infiltration, and drug sensitivity of CCAN in pan-cancer. MRNA expression profiles were collected from the cancer genome atlas, oncomine and ccle, the differential expression and various relevance analysis were performed with R or Perl.
Results: The results showed that the expression of CCAN was different in 33 tumors. Intriguingly, the poor survival in adrenocortical carcinoma, cholangiocarcinoma, kidney chromophobe, mesothelioma, kidney renal clear cell carcinoma, brain lower grade glioma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, thyroid carcinoma, uveal melanoma was most likely related to the kegg single transduction pathway including one carbon pool by folate, proteasome, arachidonic acid metabolism and so on. CENPC, ITGB3BP, APITD1, CENPU, and CENPW were more involved in tumor microenvironment, which more likely related to NK cells resting, T cells follicular helper, T cells CD8, neutrophils, macrophages M0, T cells CD4 memory activated. The relationship of CCAN expression with drug sensitivity showed that chelerythrine, nelarabine, and hydroxyurea maybe be potential drugs.
Conclusions: This multidimensional study provides a valuable resource to assist mechanism research and clinical utility about CCAN.
Abbreviations: CCAN = constitutive centromere associated network, ccle= broad institute cancer cell line encyclopedia, GSVA = gene set variation analysis, HR = hazard ratio, kegg = kyoto encyclopedia of genes and genomes, PCC = pearson correlation coefficient, TCGA = the cancer genome atlas.
Keywords: bioinformatics, constitutive centromere associated network, pan-cancer, TCGA
Editor: Sorush Niknamian.
This manuscript is a unique submission and is not being considered for publication by any other source in any medium. Furthermore, the manuscript has not been published, in part or in full, in any form.
The datasets generated and analyzed during the current study are available in the [UCSC xena] repository, [https://xenabrowser.net/datapages/], the [oncomine] repository, [https://www.oncomine.org/resource/login.html], the [human protein atlas data portal] repository, [https://www.proteinatlas.org/], the [ccle] repository, [Broad Institute Cancer Cell Line Encyclopedia (CCLE)].
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
a Medical College, Guangxi University, Nanning, Guangxi, P.R. China, b School of Basic Medical Sciences, Youjiang Medical University for Nationalities, Baise, Guangxi, P.R. China, “Key Laboratory of Translational Cancer Research, Putian University, Putian, Fujian, P.R. China.
Correspondence: Lihe Jiang, Medical College, Guangxi University, Nanning 530004, School of Basic Medical Sciences, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China (e-mail: jianglihe@ymun.edu.cn).
Copyright @ 2022 the Author(s). Published by Wolters Kluwer Health, Inc.
This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
How to cite this article: Su H, Fan Y, Wang Z, Jiang L. A comprehensive investigation on pan-cancer impacts of constitutive centromere associated network gene family by integrating multi-omics data: A CONSORT-compliant article. Medicine 2022;00(00).Medicine 2022;101:7(e28821).
Received: 17 August 2021 / Received in final form: 25 January 2022 / Accepted: 26 January 2022 http://dx.doi.org/10.1097/MD.0000000000028821
1. Introduction
Precise chromosome segregation during mitosis and meiosis is indispensable to perpetuate genomic stability and prevent aneuploidy in eukaryotes.[1] Constitutive centromere associated network (CCAN) plays a pivotal role in precise chromosome segregation, which is comprised of CENP-A/C/H/I/K/L/M/N/ O/P/Q/R/S/T/U/W/X[2]and required for chromosome congres- sion, proper spindle attachment, mitotic checkpoint activity and mitosis sister chromatids separation, leading to the assembly of a functional kinetochore.[3] CCAN can be divided into two categories: one is monomer protein including CENPA and CENPC; the others are four multisubunit complexes including CENPL-CENPN, CENPH-CENPI-CENPK-CENPM, CENPO-CENPP-CENPQ-CENPR-CENPU, and CENPT- CENPW-CENPS-CENPX. During mitosis, CCAN, as an outer kinetochore platform, was recruited into the Y-shaped revelation of the CCAN, which was mediated by the DNA binding sulcus of the CENPL-CENPN subcomplex, to make CCAN subunits associate with nucleosomal DNA and ambient histone subunits, the CENP-A nucleosome was assembled to maintain the kinetochore structure and the stability of microtubule dynamics.[1] The information of CCAN was sketched in Table 1.
Early studies have confirmed that CCAN was dysregulation in many kinds of tumors, then changed the process and develop- ment of tumorigenesis, finally lead to poor or good prognosis. For instance, CENPA, CENPH, and CENPN were highly upregu- lated in hepatocellular carcinoma,[4-6] colorectal cancer,[7] breast cancer, 8,9) and promoted their progression. On the contrary,
reduced expression of CENPE contributes to the development of hepatocellular carcinoma.[10] Interestingly, a report in 2017 has found that CENPR as a bilateral factor inhibits tumor in early stage but promotes tumor in late stage.[11] These results suggest that the abnormal expression of CCAN may closely relate to various stages of various tumors. The current cancer research of CCAN is not complete, such as the differential expression of CCAN in various cancers has not been clarified, and the regulatory mechanism behind the change of CCAN function has also not been deeply explored. Therefore, the research still needs to be deepened.
Some cancers in different organs have strong molecular similarities, while some cancers take up in the coequal organ but the molecular subtypes are completely different, on the contrary, they are more closely related to histological or anatomical types.[12] These findings supply us a new idea to search the common molecular characteristics of various human cancer through systematic analysis of specific genes in many kinds of cancers. This will provide a targeted basis for clinical comprehensive cancer diagnosis and precision medical treatment in the future.
In this study, we were aimed to explore the expression profile of CCAN, elaborate the signal transduction pathway, mutation, and copy number variation and the immunological character- istics, drug sensitivity. Our analysis attempt to systematize and understand the potential function of CCAN in cancer, and might help in the identification of novel markers providing several valid and testable hypotheses for the next exploration in cancer biology.
| Approved symbol | Approved name | Previous symbols | Aliases | Chromosome |
|---|---|---|---|---|
| CENPA | Centromere protein A | CENP-A, CenH3 | 2p23.3 | |
| CENPC | Centromere protein C | CENPC1 | CENP-C, hcp-4, MIF2 | 4q13.2 |
| CENPH | Centromere protein H | 5q13.2 | ||
| CENPI | Centromere protein I | FSHPRH1 | LRPR1, CENP-I, Mis6 | Xq22.1 |
| CENPK | Centromere protein K | FKSG14, SOLT, CENP-K | 5q12.3 | |
| CENPL | Centromere protein L | Clorf155 | dJ383J4.3, FLJ31044 | 1q25.1 |
| CENPM | Centromere protein M | C22orf18 | Pane1, CENP-M, MGC861 | 22q13.2 |
| CENPN | Centromere protein N | C16orf60 | FLJ13607, FLJ22660, BM039 | 16q23.2 |
| CENPO | Centromere protein O | MGC11266, CENP-O | 2p23.3 | |
| CENPP | Centromere protein P | RP11-19J3.3, CENP-P | 9q22.31 | |
| CENPQ | Centromere protein Q | C6orf139 | FLJ10545, CENP-Q | 6p12.3 |
| ITGB3BP | Integrin subunit beta 3 binding protein | NRIF3, HSU37139, TAP20, CENPR | 1p31.3 | |
| CENPS | Centromere protein S | APITD1, MHF1 | CENP-S, FAAP16 | 1p36.22 |
| CENPT | Centromere protein T | C16orf56 | FLJ13111, CENP-T | 16q22.1 |
| CENPU | Centromere protein U | MLF1IP | CENP-U, KLIP1, CENP-50, PBIP1 | 4q35.1 |
| CENPW | Centromere protein W | C6orf173 | CUG2 | 6q22.32 |
| CENPX | Centromere protein X | STRA13, MHF2 | MGC14480, FAAP10, CENP-X | 17q25.3 |
APITD1 = apoptosis-inducing TAF9-like domain 1, BM039=centromere protein N, C16orf56=centromere protein T, C16orf60=centromere protein N, C22orf18=centromere protein M, C6orf139= centromere protein Q, C6orf173=centromere protein W, CenH3=histone H3-like centromeric protein, CENP-50=centromere protein U, CENPA=centromere protein A, CENP-A=centromere protein A, CENPC=centromere protein C, CENP-C=centromere protein C, CENPC1 =centromere protein C 1, CENPH=centromere protein H, CENPI=centromere protein I, CENP-I=centromere protein I, CENPK= centromere protein K, CENP-K= centromere protein K, CENPL =centromere protein L, CENPM = centromere protein M, CENP-M =centromere protein M, CENPN = centromere protein N, CENPO =centromere protein O, CENP-O=centromere protein O, CENPP=centromere protein P, CENP-P=centromere protein P, CENPQ=centromere protein Q, CENP-Q=centromere protein Q, CENPR=integrin subunit beta 3 binding protein, CENPS = centromere protein S, CENP-S=centromere protein S, CENPT =centromere protein T, CENP-T = centromere protein T, CENPU=centromere protein U, CENP-U=centromere protein U, CENPW=centromere protein W, CENPX=centromere protein X, CENP-X=centromere protein X, Clorf155 =centromere protein L, CUG2 =centromere protein W, dJ383J4.3 =centromere protein L, FAAP10= centromere protein X, FAAP16 = centromere protein S, FKSG14=centromere protein K, FLJ10545 =centromere protein Q, FLJ13111 =centromere protein T, FLJ13607 = centromere protein N, FLJ22660 = centromere protein N, FLJ31044 =centromere protein L, FSHPRH1 =centromere protein I, hcp-4 =HoloCentric chromosome binding Protein, HSU37139 = integrin subunit beta 3 binding protein, ITGB3BP = integrin subunit beta 3 binding protein, KLIP1=natural killer cell-specific antigen KLIP-1, LRPR1=centromere protein I, MGC11266=centromere protein O, MGC14480=centromere protein X, MGC861= centromere protein M, MHF1 =Mhf1p, MHF2=Mhf2p, MIF2=mini zinc finger 2, Mis6 =centromere connector protein mis 6, MLF1IP = centromere protein U, NRIF3 = integrin subunit beta 3 binding protein, Pane1 =proliferation associated nuclear element, PBIP1 = centromere protein U, RP11-19J3.3=centromere protein P, SOLT =centromere protein K, STRA13 =stimulated by retinoic acid 13, TAP20 = integrin subunit beta 3 binding protein.
2. Materials and methods
2.1. Collection of expression profiles and other data
Thirty-three different kinds of human tumor data and survival data were downloaded from genomic data commons the cancer genome atlas (TCGA) cancer gene expression RNAseq-FPKM by UCSC xena (https://xenabrowser.net/datapages/), the download gene expression cohort information can be found in Table 2.
Twenty kinds of patient tumor tissue protein expression data between tumor and corresponding normal tissue were down- loaded from the human protein atlas (https://www.proteinatlas. org/) chapter of pathology atlas. The tumor cellular expression of 16 genes (CENPA, CENPC, CENPH, CENPI, CENPK, CENPL, CENPM, CENPN, CENPO, CENPP, CENPQ, ITGB3BP (also known as CENPR), CENPT, MLF1IP (also known as CENPU), CENPW, STRA13 (also known as CENPX) were recruited from ccle (Broad Institute Cancer Cell Line Encyclopedia [CCLE]). The data of mutation or copy number was download from TCGA MC3 gene-level non-silent mutation or TCGA gistic2 thresh- olded by UCS xena.
2.2. Examination of the expression array of CCAN in pan carcinoma
Seventeen genes of UCSC xena were extracted for analysis: CENPA, CENPC, CENPH, CENPI, CENPK, CENPL, CENPM, CENPN, CENPO, CENPP, CENPQ, ITGB3BP (also known as CENPR), APITD1 (also known as CENPS), CENPT, CENPU, CENPW, and STRA13 (also known as CENPX). Ggpubr package in R was used to distinguish the differential expression of CCAN in 33 kinds of tumors then display in the form of heatmap and box plot. Besides, oncomine (https://www. oncomine.org/resource/login.html) is a powerful cancer micro- array multifunctional database, we can search and visualize data rely on the standardized data. Seventeen genes were extracted for analysis: CENPA, CENPC, CENPH, CENPI, CENPK, CENPL, CENPM, CENPN, CENPO, CENPP, CENPQ, ITGB3BP (also known as CENPR), CENPS, CENPT, CENPU, CENPW, STRA13 (also known as CENPX), then synthesized the pictures using microsoft office powerpoint. The high/medium expression patient ratio data was extracted protein expression summary
| Version | Cancer type | Cancer name | Cancer cases | Normal cases |
|---|---|---|---|---|
| 07-18-2019 | ACC | Adrenocortical carcinoma | 79 | 0 |
| 07-18-2019 | BLCA | Bladder urothelial carcinoma | 411 | 19 |
| 07-18-2019 | BRCA | Breast invasive carcinoma | 1104 | 113 |
| 07-19-2019 | CESC | Cervical squamous cell carcinoma and endocervical adenocarcinoma | 306 | 3 |
| 07-19-2019 | CHOL | Cholangiocarcinoma | 36 | 9 |
| 07-19-2019 | COAD | Colon adenocarcinoma | 471 | 41 |
| 07-19-2019 | DLBC | Lymphoid neoplasm diffuse large B-cell Lymphoma | 48 | 0 |
| 07-19-2019 | ESCA | Esophageal carcinoma | 162 | 11 |
| 07-19-2019 | GBM | Glioblastoma multiforme | 168 | 5 |
| 07-19-2019 | HNSC | Head and neck squamous cell carcinoma | 502 | 44 |
| 07-19-2019 | KICH | Kidney chromophobe | 65 | 24 |
| 07-19-2019 | KIRC | Kidney renal clear cell carcinoma | 535 | 72 |
| 07-19-2019 | KIRP | Kidney renal papillary cell carcinoma | 289 | 32 |
| 07-19-2019 | LAML | Acute myeloid leukemia | 151 | 0 |
| 07-19-2019 | LGG | Brain lower grade glioma | 529 | 0 |
| 07-19-2019 | LIHC | Liver hepatocellular carcinoma | 374 | 50 |
| 07-20-2019 | LUAD | Lung adenocarcinoma | 526 | 59 |
| 07-20-2019 | LUSC | Lung squamous cell carcinoma | 501 | 49 |
| 07-20-2019 | MESO | Mesothelioma | 86 | 0 |
| 07-20-2019 | OV | Ovarian serous cystadenocarcinoma | 379 | 0 |
| 07-20-2019 | PAAD | Pancreatic adenocarcinoma | 178 | 4 |
| 07-20-2019 | PCPG | Pheochromocytoma and paraganglioma | 183 | 3 |
| 07-20-2019 | PRAD | Prostate adenocarcinoma | 499 | 52 |
| 07-20-2019 | READ | Rectum adenocarcinoma | 167 | 10 |
| 07-20-2019 | SARC | Sarcoma | 263 | 2 |
| 07-20-2019 | SKCM | Skin cutaneous melanoma | 471 | 1 |
| 07-20-2019 | STAD | Stomach adenocarcinoma | 375 | 32 |
| 07-20-2019 | TGCT | Testicular germ cell tumors | 156 | 0 |
| 07-20-2019 | THCA | Thyroid carcinoma | 510 | 58 |
| 07-21-2019 | THYM | Thymoma | 119 | 2 |
| 07-21-2019 | UCEC | Uterine corpus endometrial carcinoma | 548 | 35 |
| 07-21-2019 | UCS | Uterine carcinosarcoma | 56 | 0 |
| 07-21-2019 | UVM | Uveal melanoma | 80 | 0 |
ACC=Adrenocortical carcinoma, BLCA=Bladder urothelial carcinoma, BRCA=Breast invasive carcinoma, CESC=Cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL= Cholangiocarcinoma, COAD=Colon adenocarcinoma, DLBC=Lymphoid neoplasm diffuse large B-cell Lymphoma, ESCA=Esophageal carcinoma, GBM =Glioblastoma multiforme, HNSC=Head and neck squamous cell carcinoma, KICH=Kidney chromophobe, 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, SKCM = Skin cutaneous melanoma, STAD = Stomach adenocarcinoma, TGCT = Testicular germ cell tumors, THCA= Thyroid carcinoma, THYM = Thymoma, UCEC=Uterine corpus endometrial carcinoma, UCS =Uterine carcinosarcoma, UVM=Uveal melanoma.
from pathology atlas of the human protein atlas, calculate the high/medium expression patient ratio by microsoft office excel and draw rose diagram by R. The CCAN expression profile in 6 types of cell line was showed with heatmap by R. There are only 8 genes were recruited for analysis: CENPA, CENPH, CENPL, CENPM, CENPP, CENPQ, ITGB3BP (also known as CENPR), CENPT.
2.3. Examination of CCAN expression with risk factors for survival in pan carcinoma
Survival package in R was utilized to count hazard ratio (HR) of UCSC xena tumor gene expression data, the HR was adjusted at log as base 10 logarithm, then draw forest map to show HR fluctuation range.
2.4. Examination of CCAN expression related signal transduction pathway in pan carcinoma
The gene set variation analysis (GSVA) (http://www.gsea- msigdb.org/gsea/index.jsp) is one of the gene set enrichment analysis methods which can evaluate the variation of pathway activity in a simple population with an unsupervised way. In this paper, we elected MsigDB KEGG gene sets in gene set enrichment analysis to analyze. GSVA and gseabase packages in R were utilized to change the amount of gene expression to the amount of signal pathway in the patient sample. The relationship between CCAN expression and pathway activity was calculated by Pearson correlation coefficient (PCC). We selected the CCAN intersection pathway with P <. 05 to draw the correlation coefficient table by R, and selected the pathway with P <. 05 and |Cor|>0.3 to draw the histogram of relate pathway enrich number by R and Perl.
2.5. Examination of CCAN expression with mutation in pan carcinoma
We used Microsoft office excel to calculate the frequency of mutation then display in the form of heatmap by R.
2.6. Examination of CCAN expression with copy number variation in pan carcinoma
We used Microsoft office excel to calculate the frequency of copy number variation, reshape2 and rcolorbrewer packages in R were utilized to draw the histogram. The upper left and lower right represent deletion and amplification, respectively.
2.7. Examination of CCAN expression with tumor microenvironment in pan carcinoma
Estimate package in R was utilized to change the amount of gene expression to the score of stromal cell or immune cell. Then the PCC was calculated and shown in the relevant picture.
2.8. Examination of CCAN expression with tumor immune cell infiltration in pan carcinoma
Cibersort (CIBERSORTx [stanford.edu]) is a tool to deconvolute the expression matrix of human immune cell subtypes. We calculated the PCC with P <. 05 between CCAN expression and immune infiltration cell. Vegan, dplyr, and corrplot packages in R were utilized to draw the correlation heatmap.
2.9. Examination of CCAN expression with drug sensitivity in pan carcinoma
Cellminer (CellMiner-Analysis Tools [nih.gov]) is a drug screening tool that covers the genomic target information of thousands of drugs. Ggpubr package in R was utilized to gain gene related drugs then display in the form of linear correlation graph.
2.10. Ethical review
The databases involved in this study are open and can be used by researchers for unlimited times. Their source data have been published and approved by the local medical ethics committee. Therefore, there is no need to provide the approval documents of the ethics committee, and there are no moral problems and other conflicts of interest.
3. Results
3.1. CCAN expression profile at mRNA level
From the UCSC xena database of TGCA, we extracted 17 CCNA gene family members expression profiles from 33 tumors tissues sample, classified tumor group and normal group, and explored the differential expression. According to cluster results, CCAN was divided into three groups: CENPW, CENPH, CENPA, CENPU, CENPM were high expression group, they were strongly expressed in breast invasive carcinoma, uterine corpus endometrial carcinoma, lung adenocarcinoma, head and neck squamous cell carcinoma, stomach adenocarcinoma, liver hepatocellular carcinoma, bladder urothelial carcinoma, esoph- ageal carcinoma, cholangiocarcinoma, glioblastoma multiforme, lung squamous cell carcinoma. CENPL, CENPI, CENPO, CENPK, CENPN, and STRA13 were medium expression group, they were slightly highly expressed in those tumor tissues. APITD1, CENPP, ITGB3BP, CENPQ, CENPT, and CENPC were low expression group, they were lowly expressed in almost all tumor tissues (Fig. 1A). After excluding the cancer types of <5 corresponding normal samples, the specific distinctive expression of CCAN gene in other cancers were visualized in Figure 1B.
To verify our TGCA finding, we examined the expression of CCAN from the perspective of oncomine. The results showed that except CENPC, CENPP, ITGB3BP, CENPS, and CENPT present almost medium expression level, others gene appear to highly expressed across the 20 kinds of cancers (Fig. 1C).
3.2. CCAN expression profile at protein and cell level
Limited by incomplete data of gene and cancer type, we used the count data by the protein atlas. The high/medium expression patient ratio of some CCAN was summarized in Figure 2A. We selected 6 kinds of tumors included in immunohistochemistry to observe the mRNA expression of CCAN in corresponding tumor cells. Preliminary results showed that STRA13, CENPH, CENPN, CENPA, CENPO, MLF1IP, and CENPW were highly expression in cancers, and the expression of CENPP, CENPC, ITGB3BP, CENPI, CENPL, CENPT, CENPK, and CENPQ were lower (Fig. 2B).
3.3. Analysis of the characteristics of CCAN
We used cox regression analysis to analyze the relationship between CCAN and prognosis in different cancers. Most genes
Su et al. Medicine (2022) 101:7
8L CA £ 0 £ a CHOL 1 - A - BLCA CENPM CENPT 1 COAD ¿ + .. - BRCA 10 2 · Gate - O ITGB3BP 3 CENPQ - CHOL D N APITDI BLCA ESCA 3 1 - 2 COAD 0 1 BRCA PIVSC 1 O - 0 1 Type Type Į . CENPP express · CENPU O 2 Type e i ESCA 1 MOC CENPP CENPM expression : 75 . .. CENPW CENPW expression Į BRCA 4 … APITD1 Cancer type Canner tvne 6 PORC A 4 GBu : CHOL 6 represent P <. 001, P <. 01, P <. 05 individually. (C) CCAN expression in 20 types of cancer and normal tissues of Oncomine. - - STRA13 RARD # HNSC .. 0 CORD 00 . BLCA Į # KICH LHC Type Type CENPN : ESCA BRCA Cancer type Cancer type 0 # Gay 0 CRC 2 COMO CENPK - CHOL U 0 i # KOMer i LUSC CENPO CORD : HNSC 0.946530631327508 V KICH LIHC PRAD 0.94819813010337 CENPI 0.9059541279485 Type Type ESCA - 1.22093809648765 .. 6 - NIPC . LUAD . READ 1.20235403708106 Cancer type Cancer typ . GBM LUSC i CENPL 1.04371098349882 STAD A .. i -a ” AMRp NONSC LIHC 1 PRAD - THICA 4 NOCH READ 1 UCEC LUND Cancer type Cancer type NURC LUSC STAD A 1 gp 1 # PRAO 1 THCA CENPN expression - a - LINC 0 LUND READ UCEC A LUSC . STAO BLCA 0 PRAD ៛ THCA CENPQ expression ¿ BRCA N READ : UCEC - O CHOL SAD i ELCA . CORD - A - Tumor BLCA - … BRCA THICA ESCA CENPT expression - U - O CENPK expression Ce BRCA UCEC - C · CHOL Got N . 4 CHOL BLCA COD HNSC ® 1 COLD CORD ® Type e BLCA STRA13 expression - BRCA ESCA KOCH .. ESCA Type 1 N CHOL Gau I KIRC BRCA # Gour HNSC CHOL BLCA App .. - i HNSC ESCA V LHC į COAD KOCH Į Type BRICA NOCH ESCA Typ CHOL Glu MURC Į LUAD Cancer type LUSC . COM 00 MIRC i COMO HINSC TARP .. KIRP # PRAD HINSC ESCA 0 O NOCH F LHC UNC KICH Gou 1 PORC A 1 READ Cancer type LUND CUAD THCA Cancer type STAD KIRC MINSC KRP LUSC LUSC W V NICH C LINC PRAD * 1 UCEC POR 0 PRAD 2 D LMC LUND READ - ₮ LUSC 1 - # READ KURC .. LUAD LUSC STAD Up STAD ₹ U 1 PRAD PRAD LIMC THCA CENPO expression N LUAD F UCEC · THCA # READ # A # UCEC READ 8 8 LUSC STAD # BLCA PRAD . 2 STAD I B INCA - . 1 BRCA READ ITGB3BP expression THCA UCEC ! 0 CHOL 0 STAD UCEC # BLCA CORD - BL.CA THCA A A A CENPL expression N S UCEC CENPU expression L - i BRCA ESCA CHOL CHOL . \= 0 GBM 0 9 CORD % BICA 1 COND HNSC BLCA A Type ARCA BRCA 4 4 BRCA ESCA KOICH + KÆRC CHOL CENPA CENPC CHOL COM M · CENPE CORD CORD HNSC KRp 0 CENPAM Cancer type ESCA .. NOCH 1 LIC ” A a O . H # Gau 1. MURIC 8 LUND CENPAI 0 I . F M HINSC KRp LUSC F . DO 1 ” 0 A - A 4 KICH UHC F - 1 LUAD 1 . KIRC A 4 CINA 1 . KRØ LUSC 1 . CINPM APC PRAD . W 4 - . E 0 200 LUND 4 1 CENPN . 0 a . LUSC .. 1 - PRAD 1 O . . - 3 O · 1 . M A 3 * 1 4 3 I E A V · 1 A 6 . ₾ 3 A
value, the expression decreased from red to blue. (B) CCAN expression in 17 types of cancers between cancer and normal tissues of TCGA. The ” *** ”, ” ** ” ”*”
Figure 1. CCAN expression profile at mRNA level. (A) CCAN expression in different cancer and normal tissues of TCGA. The color represented the log2 fold change
1.16130064722769
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0.708355753113954
1.86351336736702
1.96572495005
1.59536414141667 0.222517498265382
0.160268835146795
0.840181417426946
0.13801760150713
0.777050162827639
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m
-
A
.
CENPQ
F
1
A
241
J
4
A
A
A
F
..
.
.
CENPS
-
.
..
CENPT
.
C
S
#
10
CINEPU
4
.
-
.
1
V
A
CENPW
4
A
A
STRA13
1
A
A
U
A
0
A
.
E
M
4
.
Type
Type
Breast
STRA13
6
Colorectal
Endometrial
Urothelial
Breast
CENPH
4
Liver
250
Thyroid
Carcinoid
Lung
CENPN
2
Prostate
200
Testis
Cervical
CENPM
0
150
CENPA
high/medium expression patient ratio
Stomach
Colorectal
-2
100
CENPO
MLF1IP
50
Skin
Endometrial
CENPW
0
CENPP
Renal
Glioma
CENPC
ITGB3BP
Prostate
Head and neck
CENPI
Pancreatic
Liver
CENPL
CENPT
Ovarian
Lung
Melanoma
Lymphoma
CENPK
CancerType
CENPQ
A
Gene
CENPA CENPH
CENPL CENPM
CENPP CENPQ
ITGB3BP CENPT
B
are high risk genes in adrenocortical carcinoma, cholangiocarci- noma, kidney chromophobe, mesothelioma, brain lower grade glioma, kidney renal clear cell carcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, thyroid carcino- ma, uveal melanoma, except for CENPC, CENPM, CENPP, CENPT, CENPU, CENPX (Fig. 3).
The relationship of kyoto encyclopedia of genes and genomes (kegg) single transduction pathways and CCAN were analyzed GSVA. We counted the number of each CCAN associated with activation and inhibition pathways, and found that CENPO and APITD1 were more likely to be interrelated to the positive pathway, whereas CENPT interrelated to the negative pathway (Fig. 4 A). Combined with all the CCAN gene members and significantly related pathways, a total of 74 intersection pathways were obtained (Table 3), including one carbon pool by folate,
proteasome, cell cycle, arachidonic acid metabolism, metabolism of xenobiotics by cytochrome p450, linoleic acid metabolism, primary bile acid biosynthesis, pyrimidine metabolism, basal transcription factors, and so on.
Through the extraction and summary of all the samples of TCGA of UCSC xena, we counted the mutation frequency of CCAN in 33 kinds of cancer (Fig. 4B). From the results, CCAN rarely changed the overall mutation rate, the mutation was mainly concentrated in colon adenocarcinoma, rectum adeno- carcinoma and uterine corpus endometrial carcinoma. Especially, CENPI and CENPC in uterine corpus endometrial carcinoma were more significant.
In addition, as for copy number variation of CCAN in 33 types of cancer, we counted homozygous deletion and single copy deletion to copy number deletion, and counted diploid normal
CENFM
CENPC
CENPH
CENPI
CENPK
CENPL
CENPM
CENPN
CENPO
CENPP
CENPO
ITGBBP
APITDI
CENPT
CENPU
CENPW
STRA13
ACC
ILCA
BRICA
CESC
CHOL
COAD
ESCA
HINSC
KOCH
LAML
LOG
LUAD
LUSC MESO
FRAD
PCPG
PRAD
READ
SARC
SHCM
STAD
TOCT
THCA
UCEC
UCS
UM
Hazard Ratio
BRCA
ACC
RICA BLCA
0.08
50
CESC
CHOL
COAD
NumberOfPathway
DLBC
ESCA GBM
0.06
HNSC
KICH
Regulation
KIRC
KIRP
LGG
positive
LIHC
0.04
0
LUAD
negative
LUSC
MESO
OV
PAAD
PCPG PRAD READ
0.02
SARC
SKCM
STAD
TGCT
0
THCA THYM
-50
UCEC
UCS
UVM
STRA13
CENPA
CENPW
CENPM
ITGB3BP
APITD1
CENPP
CENPN
CENPU
CENPT
CENPH
CENPL
CENPK
CENPO
CENPQ
CENPC
CENPI
APITD1
CENPA
CENPC
CENPH
CENPI
CENPK
CENPL
CENPM
CENPN
CENPO
CENPP
CENPQ
CENPT
CENPU
CENPW
ITGB3BP
STRA13
A
Gene
B
STRA13
CENPW
CENPT
APITD1
ITGB3BP
CENPQ
CENPP
CENPO
CENPN
CENPM
CENPL
CENPK
CENPI
CENPH
CENPC
CENPA
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
n MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
Cancer types
C
0
Gain
0.9
0
LOSS
0.9
copy and low-level copy number amplification to copy number amplification (Fig. 4C). On the whole, there were slight copy number changes in varying degrees, especially in CENPA, CENPL, CENPQ, CENPT, and STRA13 of amplification and CENPC, CENPH, CENPM, CENPN, CENPP, and CENPW of deletion.
3.4. Analysis of immune and drug correlation with CCAN
In order to understand the immune characteristics of CCAN, we first investigated the immune microenvironment. The outcomes showed that CENPC, CENPI, CENPP, CENPW were most positively related to the stromal cell, and CENPC, ITGB3BP,
| Pathway | Correlation coefficient | |||||
|---|---|---|---|---|---|---|
| CENPA | CENPC | CENPH | CENPI | CENPK | CENPL | |
| Alanine aspartate and glutamate metabolism | -0.37 | -0.09 | 0.1 | -0.13 | -0.01 | 0 |
| Aldosterone regulated sodium reabsorption | -0.33 | -0.25 | -0.12 | -0.13 | -0.04 | -0.45 |
| Aminoacyl trna biosynthesis | 0.02 | 0.15 | 0.02 | 0.04 | 0.01 | 0.05 |
| Antigen processing and presentation | -0.24 | -0.23 | 0.27 | 0.05 | 0.27 | -0.38 |
| Arachidonic acid metabolism | -0.34 | -0.48 | -0.09 | -0.36 | -0.2 | -0.22 |
| Arrhythmogenic right ventricular cardiomyopathy arvc | 0.11 | 0.05 | 0.03 | 0.06 | 0.07 | -0.02 |
| Ascorbate and aldarate metabolism | -0.36 | -0.09 | -0.04 | -0.06 | 0.01 | -0.15 |
| Axon guidance | 0.09 | 0.28 | 0.01 | -0.04 | 0 | 0.03 |
| Basal cell carcinoma | -0.04 | 0.23 | -0.02 | -0.12 | -0.04 | -0.03 |
| Basal transcription factors | 0.25 | 0.08 | 0.29 | 0.34 | 0.24 | 0.3 |
| Base excision repair | 0.04 | 0.19 | 0.14 | 0.12 | 0.04 | 0.07 |
| Butanoate metabolism | -0.23 | 0.19 | -0.04 | -0.18 | 0.02 | -0.09 |
| Cell adhesion molecules cams | -0.14 | -0.31 | 0.19 | -0.01 | 0.02 | -0.2 |
| Cell cycle | 0.42 | 0.1 | 0.27 | 0.34 | 0.74 | 0.29 |
| Chronic myeloid leukemia | 0.02 | 0.02 | 0.05 | 0 | 0.01 | 0.17 |
| Colorectal cancer | 0.1 | 0.09 | 0.02 | 0.11 | 0.04 | 0.36 |
| Dna replication | 0.31 | -0.16 | 0.18 | 0.17 | 0.11 | 0.14 |
| Dorso ventral axis formation | 0.02 | 0.07 | 0.01 | 0.04 | 0 | -0.08 |
| Drug metabolism cytochrome p450 | -0.47 | -0.18 | -0.21 | -0.44 | -0.21 | -0.28 |
| Fatty acid metabolism | -0.13 | 0.11 | -0.04 | -0.11 | -0.19 | 0.02 |
| Gap junction | -0.02 | 0.1 | 0.02 | -0.05 | 0.02 | -0.07 |
| Glutathione metabolism | -0.25 | 0.16 | -0.11 | -0.09 | -0.03 | 0.01 |
| Glycosphingolipid biosynthesis ganglio series | -0.01 | -0.03 | -0.16 | -0.04 | 0.06 | 0.1 |
| Glycosphingolipid biosynthesis lacto and neolacto series | -0.05 | 0.05 | -0.17 | -0.07 | -0.14 | -0.22 |
| Gnrh signaling pathway | -0.04 | -0.04 | 0.02 | -0.09 | -0.06 | -0.08 |
| Histidine metabolism | -0.2 | 0.22 | -0.14 | -0.21 | -0.15 | -0.19 |
| Homologous recombination | 0.12 | 0.05 | 0.13 | 0.12 | 0.09 | 0.16 |
| Inositol phosphate metabolism | 0.12 | 0.32 | 0.03 | -0.11 | 0 | 0.01 |
| Intestinal immune network for iga production | -0.03 | -0.11 | 0.25 | -0.22 | 0.08 | -0.2 |
| Leukocyte transendothelial migration | -0.03 | -0.13 | -0.15 | 0.07 | 0.04 | -0.25 |
| Linoleic acid metabolism | -0.28 | -0.04 | -0.22 | -0.34 | -0.19 | -0.49 |
| Long-term depression | -0.09 | -0.2 | -0.03 | 0 | 0.08 | -0.25 |
| Long-term potentiation | 0.05 | 0.11 | -0.02 | 0.07 | 0.05 | -0.17 |
| Lysine degradation | -0.15 | 0.08 | -0.22 | -0.11 | -0.14 | -0.03 |
| Melanogenesis | 0.04 | 0.31 | 0.01 | -0.03 | 0.08 | 0.02 |
| Melanoma | 0.01 | -0.02 | -0.01 | 0.03 | 0.05 | 0 |
| Metabolism of xenobiotics by cytochrome p450 | -0.43 | -0.23 | -0.48 | -0.43 | -0.34 | -0.27 |
| Mismatch repair | 0.11 | 0 | 0.16 | 0.08 | 0.06 | 0.14 |
| Neuroactive ligand receptor interaction | -0.16 | -0.34 | -0.03 | -0.16 | -0.03 | -0.12 |
| Neurotrophin signaling pathway | -0.1 | 0.11 | -0.02 | -0.06 | 0.04 | -0.25 |
| Non homologous end joining | 0.31 | 0.09 | 0.11 | 0.15 | 0.14 | 0.07 |
| Nucleotide excision repair | 0.14 | 0.07 | 0.16 | 0.16 | 0.14 | 0.24 |
| Olfactory transduction | -0.11 | -0.14 | -0.13 | -0.16 | -0.16 | -0.14 |
| One carbon pool by folate | 0.62 | 0.24 | 0.13 | 0.06 | 0.16 | 0.2 |
| Oocyte meiosis | 0.24 | 0.03 | 0.2 | 0.18 | 0.13 | 0.13 |
| P53 signaling pathway | 0.16 | -0.09 | 0.15 | 0.19 | 0.18 | 0.15 |
| Pancreatic cancer | 0.1 | -0.01 | 0.06 | 0.05 | 0.03 | 0.22 |
| Pantothenate and coa biosynthesis | 0.14 | -0.03 | 0.13 | 0.09 | 0.13 | 0 |
| Pathogenic escherichia coli infection | 0.18 | -0.14 | 0.13 | 0.18 | 0.13 | 0.05 |
| Pathways in cancer | 0.08 | 0.29 | 0.05 | 0.1 | 0.08 | 0.2 |
| Primary bile acid biosynthesis | -0.5 | -0.01 | -0.24 | -0.26 | -0.02 | -0.25 |
| Primary immunodeficiency | -0.25 | -0.22 | 0.14 | -0.19 | 0.11 | -0.11 |
| Progesterone mediated oocyte maturation | 0.13 | 0.02 | 0.31 | 0.12 | 0.1 | 0.18 |
| Propanoate metabolism | -0.11 | 0.09 | -0.15 | -0.18 | -0.19 | -0.15 |
| Proteasome | 0.53 | 0.03 | 0.12 | 0.1 | 0.26 | 0.12 |
| Pyrimidine metabolism | 0.2 | 0.12 | 0.14 | 0.23 | 0.2 | 0.14 |
| Regulation of actin cytoskeleton | 0 | -0.17 | 0 | 0.14 | 0.11 | -0.08 |
| Renin angiotensin system | 0.01 | -0.08 | 0.1 | -0.07 | 0.08 | -0.1 |
| Riboflavin metabolism | 0.21 | -0.12 | 0.23 | 0.09 | 0.02 | -0.17 |
| Rna degradation | 0.36 | 0.24 | 0.19 | 0.25 | 0.18 | 0.14 |
| Rna polymerase | 0.05 | 0.15 | 0.14 | 0.17 | 0.1 | 0.11 |
(continued)
| Pathway | Correlation coefficient | |||||
|---|---|---|---|---|---|---|
| CENPA | CENPC | CENPH | CENPI | CENPK | CENPL | |
| Small cell lung cancer | 0.09 | 0.36 | 0 | 0.11 | 0.04 | 0.25 |
| Spliceosome | 0.23 | 0.09 | 0.25 | 0.1 | 0.41 | 0.25 |
| Steroid hormone biosynthesis | -0.33 | -0.1 | -0.03 | -0.09 | -0.39 | -0.47 |
| Thyroid cancer | 0.38 | -0.19 | -0.09 | 0.33 | 0.03 | 0.16 |
| Tryptophan metabolism | -0.28 | -0.01 | -0.08 | -0.17 | -0.22 | -0.08 |
| Type II diabetes mellitus | -0.28 | 0.03 | -0.09 | -0.08 | 0.06 | -0.25 |
| Type I diabetes mellitus | -0.33 | -0.09 | 0.06 | -0.23 | 0.2 | -0.09 |
| Tyrosine metabolism | -0.41 | 0.09 | -0.14 | -0.21 | -0.27 | -0.21 |
| Ubiquitin mediated proteolysis | 0.19 | 0.22 | 0.09 | 0.15 | 0.07 | 0.24 |
| Valine leucine and isoleucine degradation | -0.1 | 0.23 | -0.02 | -0.12 | -0.16 | -0.04 |
| Vasopressin regulated water reabsorption | 0.2 | 0.04 | 0.11 | 0.08 | 0.1 | -0.1 |
| Viral myocarditis | 0.11 | -0.24 | 0.14 | 0.17 | 0.15 | -0.34 |
| Wnt signaling pathway | 0.09 | 0.46 | 0.02 | -0.01 | 0.08 | 0.13 |
| Pathway | Correlation coefficient | |||||
|---|---|---|---|---|---|---|
| CENPM | CENPN | CENPO | CENPP | CENPQ | ITGB3BP | |
| Alanine aspartate and glutamate metabolism | -0.16 | -0.11 | -0.13 | 0.01 | -0.12 | 0.12 |
| Aldosterone regulated sodium reabsorption | -0.22 | -0.09 | -0.21 | -0.51 | -0.05 | -0.12 |
| Aminoacyl trna biosynthesis | 0.19 | 0.05 | 0.09 | 0.15 | 0.01 | -0.02 |
| Antigen processing and presentation | 0.2 | -0.34 | -0.16 | -0.27 | -0.16 | 0.24 |
| Arachidonic acid metabolism | -0.28 | -0.6 | -0.47 | -0.25 | -0.08 | -0.14 |
| Arrhythmogenic right ventricular cardiomyopathy arvc | 0.06 | 0.02 | 0.03 | -0.08 | 0.04 | -0.1 |
| Ascorbate and aldarate metabolism | -0.18 | -0.4 | -0.11 | -0.25 | -0.12 | -0.15 |
| Axon guidance | 0.02 | 0.01 | 0.17 | -0.08 | 0.09 | -0.08 |
| Basal cell carcinoma | -0.08 | -0.04 | 0.03 | 0.29 | -0.02 | -0.08 |
| Basal transcription factors | 0.3 | 0.59 | 0.32 | 0.22 | 0.25 | -0.01 |
| Base excision repair | 0.15 | 0.15 | 0.1 | 0.07 | 0.18 | -0.09 |
| Butanoate metabolism | -0.23 | -0.12 | -0.11 | -0.03 | -0.11 | 0.02 |
| Cell adhesion molecules cams | -0.04 | -0.14 | -0.23 | -0.22 | 0.11 | 0.09 |
| Cell cycle | 0.35 | 0.34 | 0.43 | 0.21 | 0.25 | 0.05 |
| Chronic myeloid leukemia | -0.11 | 0.07 | 0.07 | -0.08 | 0.09 | -0.07 |
| Colorectal cancer | 0.1 | 0.13 | 0.46 | 0.27 | 0.15 | -0.08 |
| Dna replication | 0.29 | 0.34 | 0.15 | 0.22 | 0.24 | 0.03 |
| Dorso ventral axis formation | -0.04 | -0.07 | 0.14 | 0.13 | 0.02 | -0.15 |
| Drug metabolism cytochrome p450 | -0.24 | -0.41 | -0.25 | -0.22 | -0.12 | -0.14 |
| Fatty acid metabolism | -0.35 | 0.03 | 0.11 | 0.09 | -0.11 | -0.04 |
| Gap junction | -0.02 | -0.05 | 0.1 | -0.08 | 0.04 | -0.2 |
| Glutathione metabolism | -0.26 | -0.17 | -0.04 | 0.07 | -0.13 | -0.11 |
| Glycosphingolipid biosynthesis ganglio series | -0.14 | -0.04 | 0.12 | 0.08 | -0.1 | -0.16 |
| Glycosphingolipid biosynthesis lacto and neolacto series | -0.14 | 0 | -0.08 | -0.14 | 0.12 | -0.2 |
| Gnrh signaling pathway | -0.11 | -0.01 | -0.13 | -0.09 | 0.1 | -0.08 |
| Histidine metabolism | -0.2 | -0.27 | -0.15 | 0.02 | -0.13 | -0.12 |
| Homologous recombination | 0.19 | 0.18 | 0.18 | 0.11 | 0.13 | 0.03 |
| Inositol phosphate metabolism | 0.3 | -0.08 | -0.1 | 0.11 | 0.08 | 0 |
| Intestinal immune network for iga production | -0.25 | -0.01 | -0.22 | -0.2 | 0.3 | 0.18 |
| Leukocyte transendothelial migration | -0.01 | 0.02 | -0.17 | -0.21 | 0.12 | -0.05 |
| Linoleic acid metabolism | -0.22 | -0.52 | -0.56 | -0.16 | -0.06 | -0.16 |
| Long term depression | 0.06 | 0.03 | 0 | -0.16 | -0.07 | -0.05 |
| Long term potentiation | -0.1 | 0.06 | 0.02 | -0.09 | 0.04 | -0.08 |
| Lysine degradation | -0.18 | 0.01 | 0.02 | 0.28 | -0.04 | -0.1 |
| Melanogenesis | -0.04 | 0.05 | -0.04 | -0.1 | -0.03 | -0.09 |
| Melanoma | -0.02 | -0.02 | 0.02 | -0.08 | -0.01 | -0.1 |
| Metabolism of xenobiotics by cytochrome p450 | -0.44 | -0.63 | -0.39 | -0.19 | -0.22 | -0.09 |
| Mismatch repair | 0.16 | 0.17 | 0.14 | 0.23 | 0.24 | -0.06 |
| Neuroactive ligand receptor interaction | -0.21 | -0.08 | -0.22 | -0.22 | 0.1 | 0.05 |
| Neurotrophin signaling pathway | -0.09 | 0.11 | 0.04 | -0.09 | 0.12 | -0.13 |
| Non homologous end joining | 0.21 | 0.19 | 0.15 | 0.13 | 0.1 | 0.03 |
| Nucleotide excision repair | 0.24 | 0.33 | 0.23 | 0.21 | 0.24 | 0.02 |
| Olfactory transduction | -0.3 | -0.17 | -0.08 | -0.17 | -0.1 | -0.19 |
| One carbon pool by folate | 0.13 | 0.07 | 0.36 | 0.15 | 0.15 | -0.04 |
(continued)
| Pathway | Correlation coefficient | |||||
|---|---|---|---|---|---|---|
| CENPM | CENPN | CENPO | CENPP | CENPQ | ITGB3BP | |
| Oocyte meiosis | 0.16 | 0.17 | 0.24 | 0.37 | 0.22 | 0 |
| P53 signaling pathway | 0.21 | 0.2 | 0.41 | 0.34 | 0.12 | 0.03 |
| Pancreatic cancer | 0.08 | 0.25 | 0.28 | 0.23 | 0.26 | -0.05 |
| Pantothenate and coa biosynthesis | 0.14 | 0.11 | 0.14 | 0.17 | 0.19 | 0.06 |
| Pathogenic escherichia coli infection | 0.12 | 0.01 | 0.18 | -0.13 | 0.29 | 0.1 |
| Pathways in cancer | 0.07 | 0.11 | 0.13 | -0.08 | 0.08 | -0.04 |
| Primary bile acid biosynthesis | -0.47 | -0.05 | -0.22 | -0.11 | -0.06 | -0.03 |
| Primary immunodeficiency | 0.09 | -0.07 | -0.23 | -0.1 | 0.06 | 0.18 |
| Progesterone mediated oocyte maturation | 0.22 | 0.26 | 0.08 | 0.17 | 0.12 | -0.12 |
| Propanoate metabolism | -0.22 | -0.21 | -0.16 | -0.1 | -0.15 | -0.11 |
| Proteasome | 0.17 | 0.41 | 0.56 | 0.1 | 0.29 | 0.23 |
| Pyrimidine metabolism | 0.28 | 0.67 | 0.22 | 0.41 | 0.09 | 0.04 |
| Regulation of actin cytoskeleton | 0.13 | 0.09 | 0.17 | -0.09 | 0.17 | -0.08 |
| Renin angiotensin system | -0.08 | 0.04 | -0.25 | -0.27 | -0.2 | 0.13 |
| Riboflavin metabolism | 0.13 | 0.05 | 0.2 | -0.04 | 0.05 | 0.19 |
| Rna degradation | 0.35 | 0.21 | 0.38 | 0.15 | 0.18 | 0.05 |
| Rna polymerase | 0.21 | 0.55 | 0.07 | 0.25 | 0.1 | 0.09 |
| Small cell lung cancer | 0.13 | 0.13 | 0.15 | -0.08 | 0.18 | -0.07 |
| Spliceosome | 0.22 | 0.26 | 0.21 | 0.17 | 0.09 | -0.07 |
| Steroid hormone biosynthesis | -0.19 | -0.29 | -0.08 | -0.27 | 0.02 | -0.13 |
| Thyroid cancer | 0.1 | 0.02 | 0.2 | 0.23 | 0.11 | -0.14 |
| Tryptophan metabolism | -0.29 | -0.19 | -0.18 | -0.22 | -0.09 | 0.01 |
| Type II diabetes mellitus | -0.16 | -0.03 | -0.11 | -0.1 | 0.12 | -0.18 |
| Type I diabetes mellitus | -0.09 | -0.41 | -0.29 | -0.23 | -0.09 | 0.07 |
| Tyrosine metabolism | -0.3 | -0.27 | -0.42 | -0.12 | -0.15 | -0.08 |
| Ubiquitin mediated proteolysis | -0.08 | 0.18 | 0.14 | 0.27 | 0.14 | -0.12 |
| Valine leucine and isoleucine degradation | -0.21 | -0.19 | -0.01 | -0.16 | -0.14 | -0.12 |
| Vasopressin regulated water reabsorption | 0.14 | 0.14 | 0.09 | 0.05 | -0.05 | -0.18 |
| Viral myocarditis | 0.11 | -0.23 | -0.15 | -0.18 | 0.01 | 0.11 |
| Wnt signaling pathway | 0.05 | 0.04 | 0.17 | 0.27 | 0.05 | -0.02 |
| Pathway | Correlation coefficient | ||||
|---|---|---|---|---|---|
| APITD1 | CENPT | CENPU | CENPW | STRA13 | |
| Alanine aspartate and glutamate metabolism | 0.05 | -0.13 | 0.01 | 0.09 | 0.04 |
| Aldosterone regulated sodium reabsorption | 0.1 | -0.35 | -0.05 | -0.23 | -0.11 |
| Aminoacyl trna biosynthesis | 0.02 | 0.1 | -0.12 | -0.13 | 0 |
| Antigen processing and presentation | 0.23 | -0.25 | -0.18 | 0.17 | 0.2 |
| Arachidonic acid metabolism | 0.04 | -0.15 | -0.19 | -0.29 | -0.15 |
| Arrhythmogenic right ventricular cardiomyopathy arvc | 0.02 | -0.1 | 0.14 | 0.03 | 0.02 |
| Ascorbate and aldarate metabolism | 0.15 | -0.18 | -0.04 | -0.04 | -0.3 |
| Axon guidance | 0.15 | 0.02 | 0.06 | 0 | -0.02 |
| Basal cell carcinoma | -0.06 | 0.28 | 0.11 | -0.14 | -0.06 |
| Basal transcription factors | -0.09 | -0.02 | 0.05 | 0.23 | 0.14 |
| Base excision repair | 0.01 | 0.12 | 0.03 | 0.03 | 0.05 |
| Butanoate metabolism | 0.05 | -0.21 | -0.12 | -0.21 | -0.12 |
| Cell adhesion molecules cams | 0.02 | -0.15 | -0.11 | -0.09 | 0.07 |
| Cell cycle | 0.04 | 0.09 | 0.19 | 0.28 | 0.19 |
| Chronic myeloid leukemia | 0.16 | 0.02 | 0.03 | 0.01 | 0.08 |
| Colorectal cancer | 0.12 | 0.03 | 0.17 | 0.01 | 0.03 |
| Dna replication | 0.37 | 0.05 | 0.03 | 0.07 | 0.03 |
| Dorso ventral axis formation | 0.1 | -0.18 | 0.15 | -0.02 | -0.12 |
| Drug metabolism cytochrome p450 | -0.12 | -0.13 | -0.49 | -0.15 | -0.24 |
| Fatty acid metabolism | 0.06 | 0 | 0.03 | -0.21 | -0.13 |
| Gap junction | 0.1 | -0.12 | 0.12 | 0.12 | -0.12 |
| Glutathione metabolism | 0.04 | -0.33 | 0.01 | -0.34 | -0.33 |
| Glycosphingolipid biosynthesis ganglio series | 0.12 | -0.2 | 0.1 | -0.13 | 0.09 |
| Glycosphingolipid biosynthesis lacto and neolacto series | 0.05 | -0.1 | -0.05 | -0.14 | -0.12 |
| Gnrh signaling pathway | 0.18 | -0.11 | 0.04 | -0.04 | -0.05 |
| Histidine metabolism | -0.08 | -0.32 | -0.14 | -0.18 | -0.12 |
| Homologous recombination | 0.13 | 0.12 | 0.11 | 0.16 | 0.36 |
(continued)
| Pathway | Correlation coefficient | ||||
|---|---|---|---|---|---|
| APITD1 | CENPT | CENPU | CENPW | STRA13 | |
| Inositol phosphate metabolism | 0.05 | -0.13 | 0.25 | 0.32 | 0.13 |
| Intestinal immune network for iga production | 0.05 | -0.14 | -0.35 | 0.23 | 0.22 |
| Leukocyte transendothelial migration | 0.09 | -0.1 | 0.01 | -0.1 | -0.06 |
| Linoleic acid metabolism | 0.03 | -0.18 | -0.12 | -0.09 | -0.26 |
| Long term depression | 0.02 | -0.3 | 0.02 | -0.03 | 0.09 |
| Long term potentiation | 0.13 | 0.02 | 0.02 | -0.02 | 0.06 |
| Lysine degradation | -0.1 | -0.11 | -0.06 | -0.1 | -0.1 |
| Melanogenesis | 0.08 | -0.12 | 0.09 | -0.04 | 0.01 |
| Melanoma | -0.01 | -0.11 | -0.03 | -0.14 | -0.04 |
| Metabolism of xenobiotics by cytochrome p450 | -0.11 | -0.14 | -0.47 | -0.11 | -0.2 |
| Mismatch repair | -0.06 | -0.02 | 0.04 | -0.03 | 0.16 |
| Neuroactive ligand receptor interaction | 0.03 | -0.39 | -0.01 | -0.05 | -0.16 |
| Neurotrophin signaling pathway | 0.02 | -0.02 | 0.02 | -0.03 | -0.09 |
| Non homologous end joining | 0.1 | 0.19 | 0.19 | 0.17 | 0.23 |
| Nucleotide excision repair | 0.04 | 0.06 | 0.03 | 0.1 | 0.17 |
| Olfactory transduction | -0.11 | -0.07 | -0.16 | 0.02 | -0.14 |
| One carbon pool by folate | -0.25 | 0.1 | 0.16 | 0.28 | 0.26 |
| Oocyte meiosis | -0.04 | -0.15 | 0.15 | 0.18 | 0.1 |
| P53 signaling pathway | -0.05 | -0.04 | 0.16 | 0.3 | 0.15 |
| Pancreatic cancer | 0.08 | -0.03 | 0.12 | 0.12 | 0.03 |
| Pantothenate and coa biosynthesis | 0.23 | -0.12 | 0.16 | 0.07 | 0.11 |
| Pathogenic escherichia coli infection | 0.16 | -0.01 | 0.13 | 0.12 | 0.13 |
| Pathways in cancer | 0.1 | -0.12 | 0.1 | 0 | 0.02 |
| Primary bile acid biosynthesis | 0.03 | -0.11 | -0.21 | -0.41 | -0.15 |
| Primary immunodeficiency | 0.23 | -0.13 | 0.02 | 0.16 | 0.18 |
| Progesterone mediated oocyte maturation | -0.01 | -0.13 | 0.2 | 0.08 | 0.16 |
| Propanoate metabolism | 0.25 | -0.05 | -0.16 | -0.21 | -0.2 |
| Proteasome | 0.23 | -0.22 | 0.23 | 0.37 | 0.12 |
| Pyrimidine metabolism | 0.14 | -0.19 | 0.02 | 0.19 | 0.05 |
| Regulation of actin cytoskeleton | 0 | -0.12 | 0.13 | 0.11 | 0.06 |
| Renin angiotensin system | 0.2 | -0.18 | 0.03 | 0.05 | -0.23 |
| Riboflavin metabolism | 0.22 | 0.01 | -0.06 | 0.22 | 0.21 |
| Rna degradation | 0.03 | 0.1 | 0.09 | 0.39 | 0.13 |
| Rna polymerase | 0.12 | 0.06 | 0.07 | 0.05 | 0.08 |
| Small cell lung cancer | 0.12 | -0.12 | 0.1 | 0 | 0.06 |
| Spliceosome | -0.05 | 0.08 | 0.04 | 0.09 | 0.12 |
| Steroid hormone biosynthesis | 0.1 | -0.12 | -0.06 | -0.05 | -0.24 |
| Thyroid cancer | 0.09 | -0.15 | 0.14 | -0.1 | 0.17 |
| Tryptophan metabolism | 0.09 | -0.11 | -0.21 | -0.4 | -0.08 |
| Type II diabetes mellitus | 0.02 | -0.28 | 0.07 | -0.04 | -0.12 |
| Type I diabetes mellitus | 0.01 | -0.26 | -0.21 | 0.11 | 0.13 |
| Tyrosine metabolism | -0.25 | -0.1 | -0.26 | -0.46 | -0.17 |
| Ubiquitin mediated proteolysis | 0.08 | 0.03 | 0.09 | -0.1 | 0.23 |
| Valine leucine and isoleucine degradation | 0.22 | -0.35 | -0.02 | -0.15 | -0.13 |
| Vasopressin regulated water reabsorption | 0.05 | -0.2 | 0.22 | 0.31 | 0.09 |
| Viral myocarditis | 0.13 | -0.25 | 0.04 | 0.09 | 0.07 |
| Wnt signaling pathway | 0.12 | 0.03 | 0.22 | -0.01 | -0.07 |
APITD1 = apoptosis-inducing TAF 9-like domain 1, CENPA =centromere protein A, CENPC=centromere protein C, CENPH =centromere protein H, CENPI =centromere protein I, CENPK = centromere protein K, CENPL=centromere protein L, CENPM=centromere protein M, CENPN=centromere protein N, CENPO=centromere protein O, CENPP=centromere protein P, CENPQ=centromere protein Q, CENPT= centromere protein T, CENPU=centromere protein U, CENPW=centromere protein W, ITGB3BP=integrin subunit beta 3 binding protein, STRA13=stimulated by retinoic acid 13.
APITD1, CENPT, CENPU, and STRA13 were most negative related to the stromal cell. In the same measure, CENPW, CENPH, CENPA, CENPU, and CENPM were most positive related to the immune cell, and ITGB3BP, APITD1, CENPP, CENPQ, CENPT, and CENPC were most negative related to the immune cell (A and B).
Next, the connection between CCAN expression and immune cells infiltration was further be explored. We purposely selected the three genes with the ultimate positive correlation score of
immune cells in high expression CCAN and the three genes with the ultimate negative correlation score of immune cells in low expression CCAN as the research objects. The results showed that high expression CCAN mainly related to macrophages M0, macrophages M1, mast cells activated, mast cells resting, monocytes, neutrophils, T cells regulatory in pan-cancer, besides, low expression CCAN mainly related to NK cells resting, T cells follicular helper, T cells CD8, neutrophils, macrophages M0, T cells CD4 memory activated (C and D).
GUCA
HNSC
KICH
KIRC
KIRP
LAML
LGG LINA
LUAD LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
THCA THYM
UCEC
UCS
UVM
BRCA
CESC
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG LIHC
LUAD
LUSC
MESO
PX
PCPG PRAD
READ
SARC
SKCM
STAD
THCA
THYM
UCEC
UCS
UVM
CENPA
☒
1
CENPA
1
CENPC
CENPC
CENPH ☒
0.8
CENPI
☒
☒
☒
0.8
CENPH
☒
☒
0.6
CENPI
0.6
CENPK
0.4
CENPK
CENPL
☒
CENPL
.
0.4
CENPM ☒
CENPN
0.2
CENPM
CENPN
0.2
CENPO CENPP
0
CENPO
CENPO
☒
CENPP
☒
0
-0.2
CENPQ
-0.2
ITG83BP
APITD1
☒
☒
-0.4
ITGB3BP
.
☒
☒
APITO1
-0.4
CENPT
CENPT
CENPU
-0.6
CENPW
☒
CENPU
-0.6
-0.8
CENPW
☒
☒
-0.8
STRA13
A
-1
STRA13
-
·
B
-1
T cels regulatory (Tregs)
Monocytes
Macrophages MO
Macrophages Mt
Mast cells resting
Mast cells activated
T cells CD4 memory activated
Neutrophils
T cells COB
T cells follicular helper
NK cels resting
Macrophages MO
Neutrophils
P-value
T cells regulatory (Tmpgs)
1
0.8
T cells CO8
1
CENPA
Monogytes
0.6
0.8
0.6
P-value
CENPC
3 CD4 memory altreated
· ⇐ 0.001
Macrophages MO
0.4
0.2
P-value
1 cel’s folicular bylper
0.4
0.001 < x ⇐ 0.01
Machenhagen M1
· ⇐ 0.001
0.2
0
0.01 < x ⇐ 0.05
0.001 < x ⇐ 0.01
Karls
ing
0
phyt cells testing
-0.2
> 0.05
CENPW
0.01 < x ⇐ 0.05
Ophages MO
-0.2
-0.4
Mantel’s r
CENPM
Maut mets actinshed
> 0.05
0.4
-0.6
>=0.5
0.25 - 0.5
< 0.25
Mantel’s r
APITD1
CENPQ
Neutrophils
-0.6
Tocunophils
-0.8
>= 0.5
0.25 - 0.5
< 0.25
Povalue
0.8
-1
C
D
-1
CENPP, Chelerythrine Cor=0.622, p<0.001
CENPH, Nelarabine Cor=0.537, p<0.001
CENPW, Chelerythrine Cor=0.520, p<0.001
CENPH, AM-5992 Cor=0.511, p<0.001
CENPU, Chelerythrine Cor=0.507, p<0.001
2
5.0
2
2
2
0
..
2.5
0
1
0
0
:
-2
0.0
…
-2
-2
1
2
-1
N.
3
4
5
2
4
6
2
3
4
5
2
4
6
CENPP, Nelarabine Cor=0.501, p<0.001
CENPH, Chelerythrine Cor=0.491, p<0.001
CENPP, Hydroxyurea Cor=0.489, p<0.001
CENPP, AM-5992 Cor=0.487, p<0.001
CENPM, Hydroxyurea Cor=0.484, p<0.001
6
3
2
4
2
3
2
·
1
2
2
0
1
0
1
:
0-
0
…
-2
0
-1
-1
-1
1
2
2
3
4
5
1
2
1
2
3
4
5
6
7
CENPP, Imexon Cor=0.475, p<0.001
CENPH, Methylprednisolo Cor=0.474, p<0.001
ITGB3BP, Ifosfamide Cor=0.472, p<0.001
CENPH, Sapacitabine Cor=0.470, p<0.001
CENPM, S-63845 Cor=0.468, p<0.001
4
10-NWA
2
4
5.0
3
3
2
2
·
2.5
1
1
0
-1
0.0
…
0
0
-1
-1
1
2
2
3
4
5
-1
2
4
6
2
3
4
5
3
4
5
6
7
CENPM, Pipobroman Cor=0.467, p<0.001
APITD1, Chelerythrine Cor=0.464, p<0.001
CENPM, Nelarabine Cor=0.462, p<0.001
ITGB3BP, Chelerythrine Cor=0.461, p<0.001
CENPP, Ifosfamide Cor=0.460, p<0.001
2
1
2
5.0
2
2
0
0
2.5
0
1
-1
-2
0.0
-2
0
3
4
5
6
7
1
2
3
4
5
3
4
5
6
7
2
4
6
-1
2
CENPM, Chelerythrine Cor=0.455, p<0.001
CENPC, Chelerythrine Cor=0.454, p<0.001
STRA13, 5-Fluoro deoxy Cor=0.449, p<0.001
CENPI, LCL-161 Cor=0.448, p<0.001
CENPH, Palbociclib Cor=0.446, p<0.001
2
·
2
1
4 -O-NWA
2
0
1
0-
0
-1
0
-2.
-2
-2
E
3
-1
-1
4
5
6
7
0
1
2
3
3
4
5
6
7
0
1
2
3
2
3
4
5
Using the cellminer database, we inspected the association between CCAN expression and drug sensitivity. There are 632 drugs associated with these 17 CCAN genes, of which the highest correlation coefficient combinations are listed in Figure 5E, including chelerythrine, nelarabine, hydroxyurea, and so on.
4. Discussion
At metaphase of mitosis, the chromosomes oscillate regularly and harmoniously to the metaphase plate to form the equator. The kinetochore is located on both sides of the centromere. By connecting with the k-fiber, it generated the plus-end directed microtubule-motor forces to draw the chromosome to the spindle on both sides. Protein complex CCAN is the core conserved part of centromere and engaged a crucial role in this system. On the one hand, CCAN is composed of a variety of proteins, which are connected with each other and serve as a scaffold between the microtubule plus ends and the centromeric DNA. On the other hand, CCAN, as the core regulator of kinetochore-microtubule dynamics, can specifically identifies spreading microtubules, reduced the transition of kinetochore microtubule and terminal, that is, prevent the incontrollable and speedy dynamic fluctuation of kinetochore microtubule.[13] In our study, we revealed the panoramic picture of CCAN in pan-cancer and established a new understanding of CCAN.
First of all, we explored the different expression profile of CCAN in pan-cancer from mRNA, protein and cell level. The UCSC xena results were divided into three groups by the mRNA expression. There were CENPW, CENPH, CENPA, CENPU, and CENPM in the high expression group, CENPL, CENPI, CENPO, CENPK, and CENPN, STRA13 in the medium expression group and APITD1, CENPP, ITGB3BP, CENPQ, CENPT, and CENPC in the low expression group. Without divergence, the difference of oncomine expression was implicit, but the trend of differential expression of CCAN was consistent with our finding in UCSC xena. In the protein level, the trend of expression was quite lower than that of mRNA, it may be because the sample size is small, or the amount of protein will change dynamically according to the needs of mitosis, which needs further study. In the cell level, we analyzed the cell expression profile of CCAN in breast, colorectal, endometrial, liver, lung, and prostate. The results were basically consistent with the mRNA expression in tissues. Previous clinical studies have shown that CENPA, CENPK, CENPL, CENPM, CENPN, CENPU, and CENPW was consis- tently overexpressed in many human cancers, which proved our results.[14]
Cox analysis showed that some CCAN were associated with descending survival. Therefore, we try to speculate on the potential causes of CCAN in the aspects of signaling pathway, gene variation and immune environment. By analyzing the interrelationship between CCAN expression and kegg pathway, it was concluded that CCAN was involved in 184 enrichment pathways, 74 of which are common pathways. According to the kegg classification, the 74 pathways involved are cellular processes (10.6%), environmental information processing (10.1%), genetic information processing (17.6%), human diseases (18.9%), metabolism (32.4%), and organismal systems (20.3%). These results indicated that CCAN complexes not only cooperated with each other to complete signal transduction, but also played their own roles independently. According to the early literature, CENPN could promote oral cancer by strengthening cell cycle. On the contrary, G1 phase stagnated when CENPN
decreased, accompanied with augmentation of p21 CIP1, p27 kip1 and abatement of cyclin D1, CDK2, CDK4.[15] In addition, CENPA promoted chromosome instability and tumorigenesis by phosphorylation on serine 18.[16] Further research confirmed that CENPK promote hepatocellular carcinoma with very poor prognosis through activated the AKT and MDM2 protein into phosphorylation in the position of tyrosine, but the TP53 protein tyrosine out of phosphorylation.[17] In general, the results mentioned above indicated that some CCAN directly participat- ed in cancer signaling pathway, which leaded to poor prognosis.
It is all to know that loss of CCAN integrity can lead to centromere dysfunction, resulting in relocation of microtubules minus-ends, dispersion of pericentriolar material during mitosis, and chromosome separation errors.[18] However, in the study of mutation frequency and copy number variation frequency, CCAN family genes are relatively conservative and stable. Only CENPI and CENPC mutations are obvious in uterine corpus endometrial carcinoma, and copy number changes are obvious in kidney chromophobe and uterine carcinosarcoma. This showed the importance of CCAN in epigenetics from another point of view. It is inferred that the variation of CCAN gene is not the main cause but the important cause of cancer.
At present, the research on CCAN immunity mainly focuses on autoimmune systemic sclerosis, however, the reports about CCAN in the field of tumor immunity were deficiency. In this study, we analyzed the association between CCAN expression and tumor microenvironment, the results showed significant correlation in stromal cell and immune cell, which indicated the potential metastasis capability of tumor. Furthermore, in order to identify specific immune cells that regulate tumor microenviron- ment, we selected the high expression group and low expression group respectively for immunocyte infiltration analysis, the results show that the two groups of genes are associated with immune cells, but the types of immune cells are mostly different. Previous studies have shown that CD4+ T cell were regulated by CENPC and CENPB were in a resting state before cell division, the content of CENPA was very low at the same time. After entering the cell cycle, T cells were activated and CENPA was increased by reloading.[19] Combined with our progressive studies, we have reason to believe that there is compact linkage across CCAN and tumor immune cell infiltration, and it may be a factor to affect the prognosis of tumor treatment, but the dependence of them is worth further exploration.
The correlation examination between CCAN expression and drug sensitivity in pan carcinoma was listed. Chelerythrine had the highest correlation with multiple genes, which was investigated the effects as a cancer treatment in several studies.[20-22] In terms of signal transduction pathway, cheler- ythrine was involved with the pathways of Wnt, NF-KB, PI3K/ BAD, and so on.[23-25] In terms of tumor immune cell infiltration, chelerythrine was involved with CD4+ and CD8+ T cell infiltration.[26] The above results are highly coincident with our analysis results, which reflects the reliability of the system, and also help to improve the precision treatment of cancer caused by CCAN.
5. Conclusion
To summarize, our study comprehensively analyzed the expres- sion, pathway, immunity and treatment of CCAN in pan-cancer, described the cancer network of CCAN, and revealed the possibility of CCAN as a diagnostic, prognostic biomarker or
therapeutic target in the future. However, there are still many problems to be further explored, such as the asymmetry of protein expression and mRNA expression profile, the correlation with tumor stem cells, hypoxia, and autophagy and so on.
Acknowledgments
This research was supported by: Key laboratory of translational cancer research (Putian university), (Grant No. 2018KF001). We declare that we have no conflict of interest.
Author contributions
Conceptualization: huimei su, Li-he Jiang.
Data curation: huimei su.
Formal analysis: huimei su.
Investigation: huimei su.
Methodology: huimei su.
Project administration: Li-he Jiang.
Resources: Li-he Jiang.
Software: huimei su. Supervision: Li-he Jiang.
Validation: huimei su.
Visualization: huimei su, Zhuan Wang.
Writing - original draft: huimei su, Yuchun Fan.
Writing - review & editing: Li-he Jiang.
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