Identification of CENPW as a Prognostic Biomarker and Potential Therapeutic Target for Clear Cell Renal Cell Carcinoma

doi:10.21203/rs.3.rs-5255400/v1

Centromere protein W (CENP-W) is essential for chromosome segregation and mitotic assembly and has been recognized as a prognostic marker in several cancers. However, its significance in clear-cell renal cell carcinoma (ccRCC) remains underexplored. To address this, we analyzed ccRCC transcriptomic data from the National Center for Biotechnology Information (NCBI) and The Cancer Genome Atlas (TCGA) to evaluate CENP-W expression and its associations with clinical outcomes, prognosis, and immune-related markers. Kaplan-Meier survival analysis revealed that elevated CENP-W levels are linked to poorer overall survival in ccRCC patients. Further meta- and multivariate analyses confirmed CENP-W as an independent negative prognostic factor. Gene Set Enrichment Analysis (GSEA) revealed the involvement of CENP-W in immune-related pathways, notably PI3K-Akt and Wnt signaling. Pearson correlation analysis demonstrated a significant association between CENP-W expression and immune cell infiltration, cancer-associated fibroblasts (CAFs), CTLA4, and PDCD1. qRT-PCR assays confirmed elevated CENP-W levels in ccRCC samples. Additionally, GSEA and GO enrichment highlighted a relationship between CENP-W and lipid metabolism, where reduced CENP-W expression led to a significant decrease in lipid droplet accumulation. This study identifies CENP-W as a potential biomarker and prognostic indicator in ccRCC, offering insights into personalized therapeutic strategies integrating tumor immunity to enhance immunotherapy efficacy.

ccRCC

CENPW

Immune checkpoint

Lipid metabolism

1.Renal cancer is often late, making early diagnosis crucial.

2.CENPW is highly expressed in tumor and is associated with poor prognosis.

3.CENPW is related to tumor immune infiltration.

4.The link between CENPW, immune checkpoints, and immunotherapy was examined.

Renal cell carcinoma (RCC) is a diverse malignancy that originates from the renal tubular epithelial cell and accounts for about 90 percent of kidney cancers [1]. It is one of the ten most common cancers in the world, with epidemiological statistics showing that there are more than 400,000 new cases each year [2], and the incidence is higher in men than in women [3]. Clear cell renal cell carcinoma (ccRCC) is the dominate subtype of renal cell carcinoma (RCC), representing approximately 70–80% of cases. Papillary renal cell carcinoma (pRCC) and chromophobe renal cell carcinoma (chRCC) rank as the second and third most prevalent subtypes of renal cell cancer, respectively, comprising about 10–15% and 3–5% of RCC cases, respectively.[4]

CENPW, located at chromosome 6q22.32[5], contains a 267bp open reading frame and is regulated by its upstream promoter region [6], resulting in the transcription of a 600bp mRNA. Also referred to as cancer upregulated gene 2 (CUG2), expression of CENPW has been found to be significantly increased in certain malignancies, such as cervical, colon, liver, and lung cancers [7] [8] [9] [10]. Current research identifies CENPW as a critical component of the kinetochore, where it enhances the stability of the kinetochore pre-assembly complex, thereby ensuring the proper progression of mitosis [11]. This stabilization is pivotal in regulating the cell cycle and promoting tumor cell proliferation [12]. Additionally, CENPW has been shown to significantly influence the migratory and invasive capabilities of hepatocellular carcinoma cells, suggesting its potential as a predictive biomarker for this type of cancer [13].

Immune checkpoints are one of the key mechanisms by which tumor cells evade attacks from the immune system [14]. Tumor cells primarily achieve this by inhibiting T cell activity, thereby reducing the immune system's ability to recognize and target them. The most significant immune checkpoint pathways currently identified are the PD-1/PD-L1 axis and CTLA-4[15]. Ongoing research into immune checkpoint molecules and combination therapies continues to offer new strategies for cancer treatment, aiming to improve therapeutic outcomes for patients [16].

2.1. Data sources

Relating data were collected from TCIA (The Cancer Immunome Atlas; https://tcia.at/home); The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/); The Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/); The Gene Expression Profiling Interactive Analysis (GEPIA, https://gepia.cancer-pku.cn/); Tumor Immune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/); STRING (https://cn.string-db.org/);.

2.2. Human Samples

Tissue samples, comprising ccRCC and non-tumor tissues, were procured from the Department of Urology at the First Affiliated Hospital of Anhui Medical University, during 2022-2023years. Pathological diagnosis confirmed the tumor type of each sample, and informed consents were acquired from all patients. The present investigation implemented the guidelines outlined in the Declaration of Helsinki and received approval from the Ethics Committee of Human Research at the First Affiliated Hospital of Anhui Medical University (PJ2019-14–22).

2.3. Real-time quantitative PCR

It is common to use TRIzol reagent (Invitrogen, Carlsbad, CA) to extract RNA from cells and tissues. First, samples are washed three times with PBS, after adding TRIzol, let samples dissolve at room temperature for 15minutes. Then samples are centrifuged using a high-speed centrifuge. Next, mRNA is extracted using chloroform, and after centrifugation, the upper aqueous phase is discarded while the lower precipitate is retained. The precipitate is then washed and purified with anhydrous ethanol. Finally, the RNA is dissolved in DEPC-treated water, and its purity and concentrations are measured using a spectrophotometer. For cDNA synthesis, mRNA extracted by this method is reverse transcribed using the PrimeScript™ RT Reagent Kit (Takara, Japan) according to the manufacturer's instructions. The cDNA was amplified using SYBR Green Master Mix (Takara, Japan), and mRNA levels in cells or tissues were quantified on the ABI7500 platform (Thermo Fisher Scientific, USA). The primers used are:

GAPDH:

F: 5'-TTGCCCTCAACGACCACTTT-3'

R: 5'-TGGTCCAGGGGTCTTACTCC-3'

CENPW:

F: 5'-AAGCCTCAACTTCGTCTGGAG-3'

R: 5'-CACAAGCGTTTGTCCTGGACT-3'

2.4. Cell migration and invasion assay

The 786-O and CAKI-1 cells treated with si-CENPW were trypsinized, centrifuged, and then resuspended in a medium devoid of serum. The cells were subsequently seeded into the upper chambers of transwell inserts at densities of 3,000 cells/mL for migration assays and 5,000 cells/mL for invasion assays. Following 36 hours of incubation at 37°C in a 5% CO2 atmosphere, the cells were treated with 4% formaldehyde, using crystal violet for staining. Cell counts were then quantified under a microscope.

2.5. Cell proliferation assay

786-O and CAKI-1 cells treated with sh-RNA were seeded into 96-well plates at a density of 2000 cells/mL. After the cells adhered, cell proliferation was assessed at 0, 1, 2, 3, and 4 days using the Cell Counting Kit-8 (GlpBio, #GK10001). Absorbance was measured at a wavelength of 450 nm using a microplate reader.

2.6. Oil Red O staining assay

Seeded onto six-well plates, cells were evaluated for lipid droplet concentration after reaching 70% confluence using the Oil Red O Stain Kit (Solarbio, G1262). The cells were first washed twice with PBS and then fixed with a fixing buffer for 30 minutes. After two rinses with distilled water, the cells were incubated in 60% isopropanol for 5 minutes. Freshly prepared Oil Red O staining solution was applied for 20 minutes, followed by a 2-minute incubation with Mayer's hematoxylin staining solution. The stain was discarded, using PBS to wash cells three times. The results were then documented using a microscope.

2.7. Lipid Droplet Staining and Quantification

Lipid droplet detection was performed using BODIPY 493/503 (Invitrogen, #D2191). Cells were seeded into six-well plates, and when they reached 50% confluence, the detection was carried out. First, wash the cells twice with PBS, then fix them with 4% formaldehyde at room temperature for 20 minutes. Next, incubate the cells with 2 µM BODIPY 493/503 at 37°C in the dark for 15 minutes. Stain the cells with Hoechst 33342 (10 µg/ml, MCE, HY-15559) for 5 minutes. Finally, use a fluorescence microscope to detect the lipid droplet content in the cells.

2.8. Statistical Analysis

Statistical analysis was conducted with R-4.3.1 and GraphPad Prism 10.0. For statistical differences analysis, students' t-tests or ANOVA analyses were utilized.

3.1 CENPW is abnormally upregulated in ccRCC.

First, using the TIMER database, the author investigated the changes in CENPW expression levels in cancer tissues compared to normal samples. The results showed that CENPW is significantly upregulated in most cancer types, including ccRCC (Fig.1. a-b). CENPW expression profiles from the GSE36895 and GSE53757 datasets also confirmed our conclusion (Fig.1. c-d). To further validate the results based on high-throughput sequencing and gene microarray, samples collected from clear cell renal cell carcinoma patients were subjected to an RT-qPCR experiment. There was a significant upregulation of CENPW mRNA expression in clear cell renal cell carcinoma tissues (Fig.1. e). Perform a statistical analysis of the sample information used in the experiments (Table. 1)

3.2 High expression of CENPW is associated with clinical characteristics.

The objective of this work is to explore the role of CENPW in ccRCC pathogenesis by analyzing the correlation between CENPW expression and clinical features of ccRCC. In the TCGA-KIRC dataset, the author found that the expression level of CENPW was significantly associated with T-stage, N-stage, M-stage, clinical stage, and patient’s vital status but had no correlation with gender and age (Table. 2). Besides, the expression level of CENPW was higher in high levels of T-stage (Fig.1.f), N-stage (Fig.1. g), and M-stage (Fig.1. h). Also, high expression of CENPW was correlated to worse grade and stage (Fig.1. i-j).

3.3 High expression of CENPW predicts poor clinical outcomes.

From the above analysis, the author has shown the elevating expression of CENPW in ccRCC and the association between CENPW expression levels and clinical characteristics. However, whether CENPW is positively associated with ccRCC malignancy is still largely unknown. To address this question, the author stratified patients into high and low CENPW expression groups based on the median CENPW expression level in the TCGA-KIRC dataset. Then KM survival analysis was conducted, and the findings indicated that the high expression group had a notably lower overall survival rate compared to the low expression group (Fig.2. a), and the disease-free survival of the high expression group was markedly lower than that of the low expression group (Fig.2. b). The author performed a ROC curve analysis to confirm the ability of CENPW to predict survival rates. The results demonstrated that CENPW was an accurate diagnostic indicator for patients with ccRCC (Fig.2. a-b). In order to confirm the reported findings, Cox regression analysis was employed to ascertain if CENPW was a risk factor for ccRCC patient outcomes. The results showed that in addition to Age, Grade, Stage, T-stage, and M-stage, CENPW was a significant risk factor for the prognosis of ccRCC patients (Fig.2. c). Furthermore, the multivariate analysis also identified CENPW as an adverse factor (Fig.2. d). It turned out that Age, Grade, and Stage were also deleterious factors in the TCGA-KIRC dataset (Fig.2. d). Furthermore, three independent datasets validated that CENPW influenced prognosis for patients with ccRCC (Fig.2. e).

3.4 Investigate the function of CENPW in ccRCC.

Knowing that CENPW is a deleterious factor for ccRCC patients, this study further analyzed the possible signal pathways in which CENPW may be involved. The author first classified the data from the TCGA-KIRC dataset into high and low expression groups according to the median CENPW expression value. Gene expression profiles from these two groups showed significant differences (Fig.3. a). Then, KEGG and Gene Ontology analysis were performed to identify the significant pathways enriched in these two groups. The results showed that several pathways often abnormally upregulated in malignancy, such as the PI3K-Akt and Wnt signaling pathways, were enriched (Fig.3. b). Surprisingly, cholesterol metabolism, high-density lipoprotein particle pathways and fat digestion and absorption were also enriched (Fig.3. b-c). High-density lipoprotein is the main carrier involved in cholesterol transport, which indicates that CENPW may be involved in cholesterol metabolism in ccRCC. The author also found enriched cytokine-cytokine receptor interaction (Fig.3. b), B cell-mediated immunity, antigen binding, and immunoglobulin receptor binding (Fig.3. c). These results indicated that CENPW may be involved in immune cell infiltration and tumor microenvironment changes in ccRCC. GSEA was also performed to complete GO and KEGG enrichment results. After analyzing the results, the author found that CENPW may promote ccRCC development by activating the P53 signal pathway and participating in DNA replication. Significant enrichment of the cytokine-cytokine receptor interaction pathway was seen in the group with high CENPW expression. (Fig.3. d), which was consistent with KEGG enrichment results (Fig.3. b).

3.5 PPI network and co-expression analysis of CENPW.

The PPI network was established by using the STRING database (Fig.4. a). Then, Degree and Maximum Neighborhood Component (MNC) algorithms were carried out to recognize the hub genes from this network. The top 10 genes calculated by these two methods showed significant consistency (Fig.4. b). The GO enrichment of these hub genes showed that these genes were related to protein-DNA complex, chromosome, and kinetochore (Fig.4. c). The results of this study indicate that CENPW mainly associates with DNA replication and cell division-related genes to promote proliferation of ccRCCs. The co-expressed genes with CENPW were determined based on Spearman correlation analysis across the whole transcriptome sequences of TCGA-KIRC. These are the top five positively correlated genes based on correlation coefficients (UBE2C, PTTG1, AURKB, CDC20, BIRC5) and the top 5 negatively correlated genes (HOOK1, PRKAA2, OSBPL1A, SPATA18, EMX2OS) were selected under the criterion of P-value less than 0.01.

3.6 CENPW is involved in immune cell infiltration of ccRCC.

GO enrichment analysis data pointed out that CENPW may be involved in tumor immunology. Given that tumor immune cell infiltration is essential in tumor development, CENPW and immune infiltration need to be investigated.

First, the author used the TIMER database to analyze the association between diverse immune cell infiltration and CENPW expression levels. A negative association was seen between the expression of CENPW and tumor purity, whereas a positive correlation was found between CENPW expression with the quantification of CD8+ cells, CD4+ T cells, B cells, Neutrophils, Macrophages, and Dendritic cells infiltrating the tumor (Fig.5. a).

Then, the tumor-associated stroma cell and Infiltration of immune cells level differences between the two groups were assessed by the ESTIMATE algorithm based on TCGA-KIRC expression profiles. According to (Fig.5. b), as reported by TIMER, there was a significant decrease in tumor purity in the CENPW-overexpressed group. The CENPW high-expressed group exhibited higher immune cell and tumor-associated stroma cell abundance, there was a higher Immune Score and Stroma Score in the group with high CENPW expression (Fig.5. b). ESTIMATE also generated an ESTIMATE Score, which comprehensively investigates immune and stroma cell infiltration levels. The ESTIMATE Score was higher in the CENPW high-expressed group contrasts to the CENPW low-expressed group (Fig.5. b).

Next, to compensate for the data from the TIMER database, CIBERSORT algorithms were used to analyze the difference in numbers of immune cells infiltrated between groups with high and low CENPW expression, respectively. In agreement with (Fig.5. a), CD8+ T cells and macrophages M0 infiltrated more heavily in the group with high CENPW expression (Fig.5. c). The author also found that activated, follicular helper T cells, CD4 memory T cells, NK cells, γδ T cells and Dendritic cells were differentially elevated in the high CENPW expression group (Fig.5. c).

At last, as immune checkpoint therapy has become increasingly important in cancer therapy, an exploration of the co-relation between immune checkpoints and CENPW expression is necessary. It founded out that 27 immune checkpoints were elevated in the CENPW high-expressed group and were positively associated with CENPW expression level to varying degrees (Fig.5. d-e). CTLA4, a target of Ipilimumab for immunotherapy, was associated with the expression level of CENPW. Another gene, PDCD1 encoding PD1, was also positively correlated with CENPW. While for CD274 and IDO1, CENPW was negatively correlated with (Fig.5. f).

3.7 Immunotherapy and chemotherapy responsibility of CENPW.

To better evaluate the value of immune checkpoint inhibitors targeting CTLA4 and PD1 and find potential chemotherapy molecules for ccRCC, the author performed immunophenoscore (IPS) analysis and IC50 analysis based on the TCIA database and the ‘pRRophetic’ R package. The results revealed that the CENPW high-expressed group showed greater IPS-CTLA4-pos-PD1-neg and IPS-CTLA4-pos-PD1-pos values but Statistical analysis did not reveal a significant difference between the IPS_CTLA4_neg_PD1_pos and IPS_CTLA4_neg_PD1_neg values (Fig.6. a), which indicates that high CENPW group responded better to CTLA4 inhibitor alone or CTLA4 inhibitor combined with PD-1 inhibitor. High CENPW expression patients may be more responsible for CTLA4 inhibitor treatment. As for chemotherapy, patients with high CENPW expression were more susceptible to first-line chemotherapy drugs, such as 5-Fluorouracil, Cisplatin, and Doxorubicin. In addition, the author also found other candidate drugs for high CENPW expression patients (Fig.6. b).

3.8 Verification of the function of CENPW in ccRCC.

The author also identified the expression level of CENPW in several renal cell carcinoma cell lines and selected 786-O and CAKI-1 cell lines, which were highly expressed CENPW compared to other cancer cells, for further experimental verification (Fig.7. a). After knocking down the expression of CENPW by siRNA (Fig.7. b), the cell viability (Fig.7. c), migration, and invasion abilities (Fig.7. d) were significantly reduced.

3.9 CENPW Regulates Lipid Metabolism in ccRCC

Through GO pathway enrichment analysis of the CENPW dataset, we found that CENPW might be associated with lipoprotein activity, which can enhance the uptake of lipid synthesis precursors through the cell membrane, thereby promoting the accumulation of lipid droplets in ccRCC (Fig.8. a). GSEA enrichment analysis revealed that when CENPW is highly expressed, the adipocytokine signaling pathway and fatty acids metabolism are activated, thus regulating lipid metabolism in ccRCC (Fig. 8b-c). To investigate the impact of CENPW on lipid metabolism in ccRCC, we performed an Oil Red O staining experiment. In this experiment, we found that knocking down CENPW significantly reduced the amount of lipid droplets in the 786-O cell line. Then, we conducted BODIPY 493/503 staining on 786-O cells and observed that neutral lipid droplets in renal cell carcinoma were markedly reduced after CENPW knockdown. Therefore, CENPW can influence the content of lipid droplets in ccRCC.

The treatment of ccRCC is still difficult considering the high metastasis rate and chemoresistance occurrence, so the discovery of biomarkers for early diagnosis and targeted therapies is of great importance [17]. With the fast development of next-generation sequencing (NGS), identifying new biomarkers has become easier. For example, Pu et al. identified TREM-1 as a potential biomarker for ccRCC diagnosis, which was also associated with immune infiltration by bioinformatics analysis [18]. In this study, the author identified CENPW as a biomarker and therapeutic target for ccRCC.

CENPW, a key protein involved in the formation of centromeric nucleosomes [10], has been recognized to be upregulated in various cancers. Wang et al. reported the abnormal expression level of CENPW in breast cancer, and high expression of CENPW was correlated with adverse clinical features [19]. Similarly, this study revealed that CENPW was elevated in ccRCC patients based on various datasets and laboratory experiments, our study also found a positive correlation between CENPW expression and multiple malignant features, such as 8^th edition of TNM stage and histologic grade. The results also point out that patients with high expressions of CENPW have poor survival rates. Taken together, these results indicate that CENPW is an adverse factor to the patient's prognosis and may serve as a diagnostic therapeutic target and biomarker for ccRCC patients.

Zhou et al. found that CENPW could promote hepatocellular carcinoma progression by activating the E2F signal pathway⁹. Still, the exact mechanism by which CENPW contributes to the malignancy of ccRCC remains unclear.

Lipid metabolic reprogramming plays a critical role in ccRCC, significantly impacting tumor initiation, progression, and malignant behavior [20]. Key characteristics of metabolic reprogramming in tumors include increased lipid storage, alterations in fatty acid metabolism, and cholesterol metabolism dysregulation [21] [22]. This reprogramming influences ccRCC progression by affecting tumor growth, migration, invasion, and immune evasion [23]. Li et al. discovered that CTBP1-DT regulates lipid synthesis in clear cell renal cell carcinoma (ccRCC), thereby promoting the progression of renal cancer. Additionally, it may serve as a potential immunological marker for ccRCC[24] Our study found that CENPW can influence the adipocytokine signaling pathway and fatty acid metabolism, thereby affecting lipid droplet accumulation in ccRCC.

In conclusion, the author investigated the role of CENPW in ccRCC. CENPW was upregulated in tumors compared to normal renal tissues. CENPW was also associated with worse clinical outcomes. In addition, CENPW was involved in immune cell infiltration and could serve as a potential target for immunotherapy and chemotherapy. Finally, by knocking down the expression of CENPW in RCC cells, the author found that CENPW was associated with cancer cell viability, migration, and invasion abilities.

ccRCC: clear cell renal cell carcinoma; CENP-W: Centromere protein W; NCBI: National Center for Biotechnology Information; TCGA: The Cancer Genome Atlas; GSEA: Gene Set Enrichment Analysis; CAFs: cancer-associated fibroblasts; CTLA4: Cytotoxic T-Lymphocyte Associated Protein 4; PDCD1: Programmed Cell Death 1; qRT-PCR: Real-time quantitative PCR; MNC :Maximum Neighborhood Component; UBE2C: Ubiquitin-Conjugating Enzyme E2 C; PTTG1:Tumor-Transforming Protein 1; AURKB :Aurora/IPL1-Related Kinase 2; CDC20: Cell Division Cycle Protein 20 Homolog; BIRC5: Baculoviral IAP Repeat Containing 5 ; HOOK1: Hook Homolog 1; PRKAA2: AMPK Subunit Alpha-2; OSBPL1A:Oxysterol Binding Protein Like 1A; SPATA18: Spermatogenesis Associated 18; EMX2OS: Empty Spiracles Homeobox 2 Opposite Strand.

competing Interests

The author declares no conflicts of interest.

Ethical approval

The present investigation implemented the guidelines outlined in the Declaration of Helsinki and received approval from the Ethics Committee of Human Research at the First Affiliated Hospital of Anhui Medical University (PJ2019-14–22).

Funding supports

This research was funded by the National Natural Science Foundation of China (81902584) and the Natural Science Research Project of Anhui Universities (2022AH051184).

Acknowledgments

We express sincere appreciation n to all the patients who took part in this study. for their invaluable contributions. Sincere appreciation to the experts and editors for making this study better.

Author contributions

XHB, FMT and LHL: conception and design this study; XQL, WBJ and GY: Gathering and assembling of data; XHB, XQL: analyzing and interpreting data; XHB and FMT: writing and reviewing manuscripts. Manuscript final approval: All authors.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Availability of Data and Materials

All the data and materials generated in this study could be acquired from the responsible authors for appropriate reasons.

A. Znaor, J. Lortet-Tieulent, M. Laversanne, A. Jemal, and F. Bray, ‘International variations and trends in renal cell carcinoma incidence and mortality’, Eur Urol, vol. 67, no. 3, pp. 519–530, Mar. 2015, doi: 10.1016/j.eururo.2014.10.002.
H. Sung et al., ‘Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries’, CA Cancer J Clin, vol. 71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660.
R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, ‘Cancer statistics, 2023’, CA Cancer J Clin, vol. 73, no. 1, pp. 17–48, Jan. 2023, doi: 10.3322/caac.21763.
B. I. Rini, S. C. Campbell, and B. Escudier, ‘Renal cell carcinoma’, Lancet, vol. 373, no. 9669, pp. 1119–1132, Mar. 2009, doi: 10.1016/S0140-6736(09)60229-4.
S. Lee et al., ‘Molecular cloning and functional analysis of a novel oncogene, cancer-upregulated gene 2 (CUG2)’, Biochemical and Biophysical Research Communications, vol. 360, no. 3, pp. 633–639, Aug. 2007, doi: 10.1016/j.bbrc.2007.06.102.
H. Kim, S. Lee, B. Park, L. Che, and S. Lee, ‘Sp1 and Sp3 mediate basal and serum-induced expression of human CENP-W’, Mol Biol Rep, vol. 37, no. 7, pp. 3593–3600, Oct. 2010, doi: 10.1007/s11033-010-0008-3.
N. Yawut et al., ‘Translocalization of enhanced PKM2 protein into the nucleus induced by cancer upregulated gene 2 confers cancer stem cell-like phenotypes’, BMB Rep, vol. 55, no. 2, pp. 98–103, Feb. 2022, doi: 10.5483/BMBRep.2022.55.2.118.
W. Malilas et al., ‘Cancer upregulated gene 2, a novel oncogene, enhances migration and drug resistance of colon cancer cells via STAT1 activation’, Int J Oncol, vol. 43, no. 4, pp. 1111–1116, Oct. 2013, doi: 10.3892/ijo.2013.2049.
Y. Zhou et al., ‘Knockdown of CENPW Inhibits Hepatocellular Carcinoma Progression by Inactivating E2F Signaling’, Technol Cancer Res Treat, vol. 20, p. 15330338211007253, May 2021, doi: 10.1177/15330338211007253.
S. Kaowinn, N. Yawut, S. S. Koh, and Y.-H. Chung, ‘Cancer upregulated gene (CUG)2 elevates YAP1 expression, leading to enhancement of epithelial-mesenchymal transition in human lung cancer cells’, Biochem Biophys Res Commun, vol. 511, no. 1, pp. 122–128, Mar. 2019, doi: 10.1016/j.bbrc.2019.02.036.
L. Prendergast et al., ‘The CENP-T/-W complex is a binding partner of the histone chaperone FACT’, Genes Dev, vol. 30, no. 11, pp. 1313–1326, Jun. 2016, doi: 10.1101/gad.275073.115.
P. Zhang et al., ‘CENPW knockdown inhibits progression of bladder cancer through inducing cell cycle arrest and apoptosis’, J Cancer, vol. 15, no. 3, pp. 858–870, 2024, doi: 10.7150/jca.90449.
Z. Zhou, Z. Zhou, Z. Huang, S. He, and S. Chen, ‘Histone-fold centromere protein W (CENP-W) is associated with the biological behavior of hepatocellular carcinoma cells’, Bioengineered, vol. 11, no. 1, pp. 729–742, doi: 10.1080/21655979.2020.1787776.
H. Gonzalez, C. Hagerling, and Z. Werb, ‘Roles of the immune system in cancer: from tumor initiation to metastatic progression’, Genes Dev, vol. 32, no. 19–20, pp. 1267–1284, Oct. 2018, doi: 10.1101/gad.314617.118.
S. C. Wei, C. R. Duffy, and J. P. Allison, ‘Fundamental Mechanisms of Immune Checkpoint Blockade Therapy’, Cancer Discov, vol. 8, no. 9, pp. 1069–1086, Sep. 2018, doi: 10.1158/2159-8290.CD-18-0367.
R. T. Manguso et al., ‘In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target’, Nature, vol. 547, no. 7664, pp. 413–418, Jul. 2017, doi: 10.1038/nature23270.
V. Schiavoni et al., ‘Recent Advances in the Management of Clear Cell Renal Cell Carcinoma: Novel Biomarkers and Targeted Therapies’, Cancers (Basel), vol. 15, no. 12, p. 3207, Jun. 2023, doi: 10.3390/cancers15123207.
‘TREM-1 as a potential prognostic biomarker associated with immune infiltration in clear cell renal cell carcinoma - PubMed’. Accessed: Aug. 25, 2024. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/37217993/
L. Wang et al., ‘Investigating CENPW as a Novel Biomarker Correlated With the Development and Poor Prognosis of Breast Carcinoma’, Front Genet, vol. 13, p. 900111, 2022, doi: 10.3389/fgene.2022.900111.
L. Sainero-Alcolado et al., ‘Targeting MYC induces lipid droplet accumulation by upregulation of HILPDA in clear cell renal cell carcinoma’, Proc Natl Acad Sci U S A, vol. 121, no. 7, p. e2310479121, doi: 10.1073/pnas.2310479121.
H. Xiao et al., ‘HIF-2α/LINC02609/APOL1-mediated lipid storage promotes endoplasmic reticulum homeostasis and regulates tumor progression in clear-cell renal cell carcinoma’, J Exp Clin Cancer Res, vol. 43, p. 29, Jan. 2024, doi: 10.1186/s13046-023-02940-6.
X. Hua et al., ‘MED15 is upregulated by HIF-2α and promotes proliferation and metastasis in clear cell renal cell carcinoma via activation of SREBP-dependent fatty acid synthesis’, Cell Death Discov., vol. 10, no. 1, p. 188, Apr. 2024, doi: 10.1038/s41420-024-01944-1.
T. Manzo et al., ‘Accumulation of long-chain fatty acids in the tumor microenvironment drives dysfunction in intrapancreatic CD8+ T cells’, J Exp Med, vol. 217, no. 8, p. e20191920, Aug. 2020, doi: 10.1084/jem.20191920.
H. Li et al., ‘Identify CTBP1-DT as an immunological biomarker that promotes lipid synthesis and apoptosis resistance in KIRC’, Gene, vol. 914, p. 148403, Jul. 2024, doi: 10.1016/j.gene.2024.148403.

Table 1 The samples information used in this study

	Pathological pattern	Gender	Age(y)	Position	Size	TNM
Case 1	ccRCC， Fuhrman 2	Male	59	Right	4.23.94.2cm	T1bN0M0

Case 2	ccRCC， Fuhrman 2	Female	65	Right	124.04.0cm	T2bN0M0

Case 3	ccRCC， Fuhrman 2	Male	57	Right	64.54.5cm	T1bN0M0

Case 4	ccRCC， Fuhrman 2	Male	52	Left	1099cm	T2bN0M0

Case 5	ccRCC， Fuhrman 2	Male	68	Left	864cm	T3bN1M0

Case 6	ccRCC， Fuhrman 2	Male	59	Left	5.55.04.5cm	T1bN0M0

Case 7	ccRCC， Fuhrman 1-2	Male	61	Left	3.22.21.1cm	T1aN0M0

Case 8	ccRCC， Fuhrman 2	Female	71	Left	332.5cm	T1aN0M0

Case 9	ccRCC， Fuhrman 2	Male	64	Right	5.72.92.7cm	T1bN0M0

Case 10	ccRCC， Fuhrman 2	Male	54	Right	54.54cm	T1bN0M0

Table 2 The characteristic of CENPW in clear cell renal cell carcinoma.

Characteristic		Total (n = 514)	CENPW		p Value
			High(257)	Low(257)
Gender	Male	330	169	161	0.5195
	Female	184	88	96
Age (median [IQR])		60.500 [52.000, 69.750]	60.000 [51.000, 69.000]	61.000 [52.000, 70.000]	0.2505
T	T1	257	113	144	0.0066*
	T2	66	30	36
	T3	180	106	74
	T4	11	8	3
N	N0+NX	498	244	254	0.0223*
	N1	16	13	3
M	M0+MX	435	208	227	0.0277*
	M1	79	49	30
Stage	I	251	109	142	0.0008*
	II	54	21	33
	III	123	72	51
	IV	83	52	31
	Not Report	3	3	0
Grade	1	13	4	9	0.0004*
	2	217	95	122
	3	201	101	100
	4	75	54	21
	X	8	3	5
Vital Status	Alive	357	159	198	0.0006*
	Dead	154	97	57
	Not Report	3	1	2

No competing interests reported.

Identification of CENPW as a Prognostic Biomarker and Potential Therapeutic Target for Clear Cell Renal Cell Carcinoma

Status:

Version 1

Abstract

Figures

Highlights

1. Introduction

2. Materials and Methods

2.1. Data sources

2.2. Human Samples

2.3. Real-time quantitative PCR

2.4. Cell migration and invasion assay

2.5. Cell proliferation assay

2.6. Oil Red O staining assay

2.7. Lipid Droplet Staining and Quantification

2.8. Statistical Analysis

3. Results

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

Version 1