Pan-cancer Analyses Reveal Regulation and Clinical Outcome Association of the PCLAF in Human Tumors

doi:10.21203/rs.3.rs-968646/v1

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Pan-cancer Analyses Reveal Regulation and Clinical Outcome Association of the PCLAF in Human Tumors

https://doi.org/10.21203/rs.3.rs-968646/v1

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published 14 Apr, 2022

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Background: PCLAF, also known as KIAA0101, is overexpressed among some human malignancies and performs distinct biological functions. However, the specific mechanism of PCLAF in cancer remains unclear. Therefore, this study aims to analyze the role of PCLAF in human tumors.

Methods: First, we used TCGA (Cancer Genome Atlas) and GEO (Gene expression Synthesis) data sets to evaluate the expression level of PCLAF in 33 tumors. Secondly, we used GEPIA2 and Kaplan-Meier plotter for survival prognosis analysis. In addition, we use cBioPortal web to analyze the genetic changes of PCLAF and perform immune infiltration analysis. At the same time, we also conducted enrichment analysis of PCLAF-related genes. Finally, we selected the hepatocellular carcinoma (HCC) cell line to verify the function of PCALF.

Results: PCLAF is highly expressed in most cancers, and there is a significant correlation between the expression of PCLAF and the prognosis of tumor patients. The expression of PCLAF is positively correlated with Act CD4 (Activated CD4 T cells) and Th2 (Type 2 T helper cells), suggesting that PCLAF may play a particular role in tumor immune infiltration. In addition, the functional mechanism of PCLAF also involves the mitotic cell cycle process, cell division, DNA replication.

Conclusions: Our first pan-cancer study provides a relatively comprehensive understanding of the carcinogenic effects of PCLAF in different tumors, which provides a solid foundation for studying the role of PCLAF in tumorigenesis and development.

Cancer Biology

PCLAF

Cancer

Prognostic biomarker

Methylation

Immune infiltration

Cell cycle

Pan-cancer analysis can elucidate the common characteristics and heterogeneity of human malignancies by analyzing the molecular abnormalities of various types of cancers[1]. The publicly funded TCGA project and the available GEO database contain functional genomics data sets of different tumors, thus conducting pan-cancer analysis[2-4]. Therefore, pan-cancer analysis is beneficial to the development of combination therapies and targeted therapies to treat various cancer models.

PCLAF (PCNA clamp associated factor), also known as KIAA0101, which was initially identified by a yeast two-hybrid[5]. PCLAF interacts with PCNA through Lys15 and Lys24 sites to recruit DNA replication polymerase[6]. When DNA is damaged, PCLAF regulates the conversion of DNA replication polymerase into translation synthesis polymerase, thereby bypassing the diseased area and continuing DNA replication[7]. PCLAF can promote the proliferation of undifferentiated thyroid cancer and cervical cancer cell lines, the DNA synthesis and cell viability of pancreatic cancer and adrenal cancer cell lines, and reduce the number of G0/G1 cells in adrenocortical cancer cell lines[8-13]. Up-regulating PCLAF can accelerate the repair of UV-induced DNA damage and prevent cell death while reducing PCLAF expression can inhibit DNA replication[7, 14-16]. PCLAF is aberrantly hyperactivated in numerous human malignancies and correlates with poor prognosis[17-19].

Our study used the TCGA (The Cancer Genome Atlas) project and the GEO (Gene Expression Omnibus) database to perform a pan-cancer analysis of PCLAF for the first time. We also incorporated factors such as gene expression, survival status, methylation status, genetic changes, immune infiltration, and related cellular pathways to explore the possible molecular mechanisms of PCLAF in the onset or clinical prognosis of different tumors.

2.1 Gene expression analysis

ONCOMINE database (www.oncomine.org) was referred to examine the expression levels of PCLAF mRNA in distinct types of cancers (P = 0.001, 1.5fold change were set as significance thresholds). In the Gene_DE module of the TIMER2 (Tumor immune estimation resource, version 2) web (http://timer.cistrome.org/) database, we observe the expression differences between PCLAF tumors and adjacent normal tissues in the TCGA project. For tumors without normal tissues in the TIMER2 database [for example, TCGA-GBM (glioblastoma multiforme), TCGA-LAML (acute myeloid leukemia), etc.], the "Expression analysis-Box Plots" module of the GEPIA2 (Gene Expression Profile Interactive Analysis, Version 2) webserver (http://gepia2.cancer-pku.cn/analysis)[20] was performed to obtain box plots of the PCLAF expression profile between the tumor tissue and corresponding normal tissue from the GTEx (Genotype-tissue expression) database, and the hypothetical value is set to cut off = 0.01, log2FC( Fold change) cutoff = 1, and Match TCGA standard and GTEx data. In addition, we obtained a violin chart of PCLAF expression in all TCGA tumors at different pathological stages (stage I, II, III, IV) through the pathological staging diagram module of GEPIA2. The box or violin chart uses log2 [TPM (Transcripts per million) +1] transformed expression data.

UALCAN portal (http://ualcan.path.uab.edu/analysis-prot.html), a database for analyzing cancer omics data, allowing us to perform protein expression analysis on the CPTAC (The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium) data set[21]. Here, we enter PCLAF to explore total protein expression levels in primary tumors and normal tissues.

2.2 Survival prognosis analysis

The Survival Map module in GEPIA2 is used to obtain OS (Overall Survival) and DFS (Disease-free Survival) saliency map data of PCLAF in all TCGA tumors. The cut-off-high value (50%) and cut-off-low value (50%) were applied to the expression thresholds for dividing the high and low expression cohorts. The "survival Analysis" module of GEPIA2 was used to obtain the p-value by the Log-rank test. Kaplan-Meier Plotter (http://kmplot.com/analysis/) is a powerful online tool capable of assessing the impact of 54,000 genes in 21 cancer types on survival. We analyzed the relationship of PCLAF expression with OS (Overall Survival), DMFS (Distant metastasis-free survival), RFS (Relapse-free survival), DSS (Disease-specific survival), FP (First progression), PPS (Progress-free survival) prognosis for breast cancer, liver cancer, and lung cancer. Hazard ratios with a 95% confidence interval (CI) and log-rank P-values were calculated.

2.3 Genetic Alterations

To study the genetic changes of the PCLAF gene in the pan-cancer cohort, we logged into the cBioPortal web (https://www.cbioportal.org/)[22, 23], we chose the "TCGA Pan-Cancer Atlas Studies" in the "Quick select" section and entered "PCLAF" for queries of the genetic alteration characteristics of PCLAF. The Cancer Types Summary module is used to observe the mutation frequency, mutation type, and CNA (copy number change) results of all TCGA tumors. The mutation site information of PCLAF can be displayed in the protein structure diagram or three-dimensional structure through the mutation module.

2.4 Immune infiltration analysis

The immune gene module in the TIMER2 database was used to explore the relationship between PCLAF expression and immune infiltration in all TCGA tumors. Immune cells of cancer-associated fibroblasts use TIMER, CIBERSORT, CIBERSORT-abs, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC algorithms to estimate immune penetration. The Spearman rank correlation test obtained the p-value and the partial correlation (cor) with purity adjustment. The data is visualized as heat maps and scatter plots.

2.5 Regulatory Networks of Transcription Factors

To study epigenetic alterations of PCLAF, TFs with binding ability to the PCLAF promoter were anticipated using Harmonizom (https://maayanlab.cloud/Harmonizome)[24], including CHEA Transcription Factor Targets. GSCALite is a web-based genomic cancer analysis platform[25].

2.6 Tumor-Immune System Interaction Database (TISIDB) and Tumor Immune Single Cell Hub Database (TISCH)

TISIDB is an online database of tumor-immune system interactions[26]. In this study, we used TISIDB to determine the expression of PCLAF and tumor-infiltrating lymphocytes (TILs) in human cancers. Based on the gene expression profile, gene set variation analysis is used to infer the relative abundance of TILs. Spearman test was used to determine the correlation between PCLAF and TILs. The Tumor Immune Single-Cell Center (TISCH) is an online database focusing on the tumor microenvironment (TME). It collects 76 tumor data sets for 27 cancers, including single-cell transcriptome profiles of nearly 2 million cells.

2.7 PCLAF-related gene enrichment analysis

We first searched the STRING website (https://string-db.org/) using the query of a single protein name ("PCLAF") and organism ("Homo sapiens"). Subsequently, we set the following main parameters: minimum required interaction score ["Low confidence (0.150)"], the meaning of network edges ("evidence"), max number of interactors to show ("no more than 50 interactors" in 1st shell). Finally, the available determined PCLAF-binding proteins were obtained. We used the "Similar Gene Detection" module of GEPIA2 to receive the top 100 PCLAF-correlated targeting genes based on the datasets of all TCGA tumors and normal tissues. We also applied the "correlation analysis" module of GEPIA2 to perform a pairwise gene Pearson correlation analysis of PCLAF and selected genes. The log2 TPM was applied for the dot plot. The P-value and the correlation coefficient (R) were indicated. Moreover, we used the "Gene_Corr" module of TIMER2 to supply the heatmap data of the selected genes, which contains the partial correlation (cor) and P-value in the purity-adjusted Spearman's rank correlation test.

2.8 Cell culture and lentivirus-mediated silencing of PCLAF

HepG2 cell lines was purchased from the Chinese Academy of Sciences, which was cultured in DMEM medium supplemented with 10% heat-inactivated fetal bovine serum (Gemini, USA) and was incubated at 37°C in a humid incubator with air containing 5% CO2. Lentivirus, including complementary oligonucleotide sequences (5′-CATGGTGCGGACTAAAGCA-3′) and non-target control shRNA (NC), were performed and synthesized by Genomeditech (Shanghai, China). At 72h post-transfection, 2 µg/ml puromycin (Cat. #ST551; Beyotime, China) was used to select for the stably transfected cell lines.

2.9 Cell viability analysis

Cell Counting Kit-8 (CCK-8) (Yeasen, Shanghai, China) was used to analyze cell viability. The cells were seeded in 96-well plates at a density of 5x 10³ per well and cultured 4 days. After cells were adherent to the bottom of wells, CCK-8 assay was then performed according to the manufacturer's instructions. The absorbance of each well was determined with a microplate reader set at 450 nM and 630 nM.

2.10 EDU incorporation test

For flow cytometry analysis of the proliferating cells, a Cell-Light EdU Apollo 488 In Vitro Flow Cytometry Kit (RiboBio, Guangzhou, China) was used to examine EdU-positive cells according to the manufacturer's protocol. The fluorescence signal at 488 nm was performed with a flow cytometer.

2.11 Cell cycle detection

According to the protocol of the Cell Cycle and Apoptosis Analysis kit (C1052), the cells were harvested and stained with propidium iodide. Then the cell cycle was measured by flow cytometry.

2.12 Annexin V-FITC/propidium iodide (PI) flow cytometry

HepG2 cells were plated in a 6-well plate and transfected with shPCLAF after 24 h. After 48 h, an Annexin V-FITC kit (BD Biosciences, San Diego, CA, USA) was used to evaluate cell apoptosis according the instructions of the manufacturer's.

2.13 Western blotting

Western blotting was implemented as mentioned previously[27]. Primary antibodies against PCLAF (81533S; 1:1000), GAPDH (2118s; 1:2000), β-actin (3700S; 1:1000), cyclin D1 (2978s; 1:1000), cyclin A2 (4656S; 1:1000), cyclin B1 (12231S; 1:1000), cyclin E2 (4132S; 1:1000), CDK2 (18048S; 1:1000), CDK6 (13331S; 1:1000), BAX (14796S; 1:1000), and BCL-2 (15071S; 1:1000) were purchased from Cell Signaling Technology (Beverly, USA). Apoptosis Antibody Sampler Kit (9915T; 1:1000) was also purchased from Cell Signaling Technology.

2.14 Quantitative real-time PCR

We extracted total RNA from cells using TRIzol reagent (Invitrogen, USA). Reverse transcription reactions were performed with a reverse transcription kit (Cat. RR036A; TAKARA, Japan). SYBR Green Master Mix (11198ES03; Yeasen, Shanghai, China) was used for quantitative real-time PCR (qRT-PCR). Primer sequences were as follows: PCLAF forward 5′-GGCAAGGAGGACAAATACGCA-3′, reverse 5′- TGTGCCCACCATGATTCTATCC-3′; Relative mRNA expressions levels were determined with the internal control GAPDH using the ^2−ΔΔCT method.

2.15 Statistical Analysis

The results generated by Oncomine are presented by p-values determined by t-tests, folding changes, and gene rankings. The Kaplan-Meier method was used to estimate the survival curve. In order to compare survival curves, we used log-rank test to calculate the HR and log-rank p values of Kaplan-Meier Plotter and GEPIA. The univariate Cox regression model was used to calculate the hr and Cox P values of the prognostic scan. Spearman correlation was used to evaluate the correlation of gene expression. The results were expressed as the mean ± standard error of the mean (SEM). A p-value of < 0.05 was considered to indicate a statistically significant difference. Significance was expressed as: *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

3.1 mRNA Expression Level of PCLAF in Pan-Cancer

The aim of this study investigates the carcinogenic effects of human PCLAF (NM_014736.6for mRNA or NP_055551.1 for protein). First, we investigated the PCLAF expression pattern in different cells and non-tumor tissues. As shown in the Supplemental Figure1A, combined with the HPA (Human Protein Atlas) dataset, the GTEx (Genotype-Tissue Expression) dataset, and the FANTOM5 (The Functional Annotation of the Mammalian Genome 5) dataset, PCLAF is predominantly expressed in the Thymus, followed by Bone Metastasis and T-cells. However, PCLAF has low RNA tissue specificity at the tissue level. Furthermore, we explored the PCLAF RNA expression in cell lines and blood cells in the HPA/Monaco/Schmiedel data sets and found that low RNA specificity also appeared (Supplemental Figure1B, C). To explore the expression level of PCLAF in different human tumors and adjacent normal tissues, the PCLAF mRNA expression levels were analyzed by the ONCOMINE database (Figure1A). The results showed that PCLAF was differentially expressed between tumors and normal tissues, such as bladder, brain and CNS, breast, cervical, colorectal, esophageal, gastric, head and neck, kidney, liver, lung, lymphoma, melanoma, ovarian, pancreatic cancer, prostate cancer, as well as other cancer. At the same time, low PCLAF expression was only found in one leukemia data set. To further explore the expression level of PCLAF in pan-cancer, we used the TIMER database to identify the RNA sequencing data in TCGA. The expression difference of PCLAF in tumor and adjacent normal tissues is shown in Figure 1B. PCLAF expression was significantly expressed in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, HNSC-HPV, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, PRAD, READ, HNSC, SKCM, STAD, THCA, and UCEC than the normal tissues. After including the normal tissue of the GTEx dataset as controls, we further evaluated the expression difference of PCLAF between the normal tissues and tumor tissues of DLBC, GBM, LGG, TGCT, SKCM, THYM, ACC, CESC OV, PAAD, and UCS as shown in Figure 1C. The CPTAC database results showed a highly expressed PCLAF protein in primary tissues of breast cancer, ovarian cancer, colon cancer, clear cell RCC, UCEC, and LUAD, compared with normal tissues (Figure1D).

We also used the "pathological staging diagram module" of GEPIA2 to evaluate the correlation between the expression of PCLAF and the pathological stage of cancer. Interestingly, we found that the expression of PCLAF increased with the clinical stage from stage I to stage IV, including ACC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, and PAAD (Supplemental Figure2A).

3.2 Multifaceted Prognostic Value of PCLAF in Cancers Survival analysis data

First, we divided the tumor cases into high-expression and low-expression groups according to the average expression of PCLAF. We mainly use TCGA and GEO databases to validate the correlation between PCLAF expression and prognosis in tumor patients. As shown in Figure 2A, highly expressed PCLAF was linked to poor prognosis of overall OS (Overall Survival) for cancers of ACC (P = 3.5E-06), KIRC (P = 0.047), KIRP (P =0.0019), LGG (P=9E-07), LIHC(P=0.002), LUAD(P=0.0012), MESO(P=2.2E-07), PAAD(P=0.011), within the TCGA project. DFS analysis data showed a correlation between high PCLAF expression and poor prognosis for the TCGA cases of ACC (P =0.0019), KIRP (P = 0.00041), LGG(P=1E-04), LIHC(P=0.0022), and LUAD(P=0.0038), MESO(P=0.01), PRAD(P=0.014), UVM(P=0.0018) (Figure 2B). The Kaplan-Meier Plotter database was used to evaluate PCLAF-related survival rates further. A high expression level of PCLAF was associated to poor OS (Overall Survival) (P =0.0016), DMFS (Distant metastasis-free survival) (P = 0.00022), and PPS (Relapse-free survival) (P = 1E-16) prognosis for breast cancer, OS (P =4.1E-05), DSS (Disease-specific survival) (P =0.00011), and RFS (P = 8.5e-05) prognosis for liver cancer and OS (P = 1.8E-16) and FP (First progression) (P=1.7E-06) PPS (Progress-free survival) (P=0.0057) prognosis for lung cancer (Supplemental Figure2B).To better understand the predictive value and possible mechanism of PCLAF expression in LUAD, we used the Kaplan-Meier database to explore the relationship between PCLAF mRNA expression and clinical features. Interestingly, PCLAF plays a harmful role in LUAD patients and has the following characteristics: high PCLAF expression was significantly correlated with poor OS and PFS in male and female lung cancer patients in adenocarcinoma. High PCLAF expression was associated with poor OS and PFS only in stage 1 lung cancer patients concerning different tumor stages. In American Joint Committee on Cancer (AJCC) N-0 lung cancer patients, PCLAF expression was significantly correlated with poorer OS and PFS. In addition, high PCLAF expression was significantly associated with poor OS and PFS in smoking lung cancer patients (Supplemental Figure 3). These results suggested that PCLAF mRNA expression had prognostic value in lung cancer.

3.3 Genetic alteration analysis data

Then we performed a fundamental cancer analysis of PCLAF in malignant tumors. In the TCGA pan-cancer group, the most common DNA change is amplification. As shown in Figure 3A, amplification was mainly distributed in Mesothelioma, Kidney Chromophobe, Sarcoma, Prostate Adenocarcinoma. Mutation of PCLAF was observed in Uterine Carcinosarcoma, Skin Cutaneous Melanoma, Head and Neck Squamous Cell Carcinoma, Kidney Renal Papillary Cell Carcinoma, Cervical Squamous Cell Carcinoma, and Brain Lower Grade Glioma patients. In addition, PCLAF deep deletion in malignancies was distributed across Stomach Adenocarcinoma, Esophageal Adenocarcinoma, and Glioblastoma Multiforme. Furthermore, Figure 3B showed the types, sites, and case number of the PCLAF genetic alteration. The main genetic changes identified in the PCLAF gene are missense mutations. The most frequent mutation was F68LY alteration, which was detected in 1 case of Bladder Urothelial Carcinoma, 1 case of Endometrial Carcinoma, and 1 case of Endometrial Carcinoma (Figure 3B). We can observe the F68LY site in the three-dimensional structure of the PCLAF protein (Figure 3C). We then confirmed the relevance of genetic disorders and PCLAF expression. We found that mutations are not related to RNA expression status (Figure 3D). In addition, we found that modifications and DNA copy variations were statistically independent of PCLAF expression (Figure 3E). Therefore, the high expression of PCLAF in cancer may not be the result of genetic variation. Then we evaluated the epigenetic disorders of PCLAF in cancer. We have found that the six highly PCLAF express tumors presented with decreased DNA methylation levels of PCLAF, including CHOL, CESC, GBM, PCPG, SARC, TGCT STAD, and UCEC (Supplemental Figure4A). Since there is no available KICH DNA methylation dataset, we cannot assess the overall DNA methylation level of KICH. On the contrary, we compared the DNA methylation level of KICH in different tumor stages and found no significant change in DNA methylation (Supplemental Figure4B). In addition, the DNA methylation level of PCLAF in BRCA and COAD remained unchanged (Supplemental Figure4C), suggesting that DNA methylation is not the only cause of abnormal expression of PCLAF.

3.4 Immune infiltration analysis data

As an essential part of the tumor microenvironment, tumor-infiltrating immune cells are closely related to tumors' occurrence, development, and metastasis [22,23]. Fibroblasts in the tumor microenvironment matrix regulate the functions of various tumor-infiltrating immune cells [24,25]. Herein, algorithms such as TIMER, CIBERSORT, CIBERSORT-abs, QUANTISEQ, XCELL, MCPCOUNTER, EPIC are used to explore the potential relationship between different levels of immune cell infiltration in various tumor types of TCGA and PCLAF gene expression. In TGCT tumors, we observed that PCLAF expression is statistically positively correlated with the estimated value of cancer-associated fibroblasts infiltration. In contrast, it is negatively correlated in BRCA, COAD, HNSC, STAD, and THYM (Figure4A, B, Supplemental Figure5A). Tumor infiltrating lymphocytes (TILs) can be performed as an independent predictor of sentinel lymph node status and cancer survival. Therefore, we also assessed the correlation between PCLAF expression and 28 TILs in the TISIDB database. Among the diseases in the TISIDB database, the expression of PCLAF was positively correlated with the Activated CD4 T cell (Act CD4) and Type 2 T helper cell (Th2) (Supplementary Figure 5B, C). These data indicated that PCLAF might play a specific role in tumor immune infiltration. TME (tumor microenvironment) plays a vital role in the occurrence and development of tumors, which may accelerate the deterioration of tumors and affect the prognosis. We used the TISCH database to analyze the expression of PCLAF in TME-related cells. Among ALL, BLCA, MM, KIRC, HNSC, NHL, MCC, MM tumors, we found that PCLAF was expressed in immune cells, malignant cells, and stromal cells (Figures 5A). In addition, PCLAF expression was the highest in CD8 T Cells, Conventional CD4 T Cells, Exhausted CD8 T Cells, Monocytes or Macrophages, Proliferating T Cells Fibroblasts in BRCA, Glioma, NSCLC, UCEC (Figures 5B). These results demonstrated that PCLAF was closely related to TME in cancer.

3.5 Enrichment analysis of PCLAF-related partners

To further elucidate the underlying molecular mechanism of the PCLAF gene in tumors, we tried to screen out the targeted PCLAF binding protein and PCLAF expression-related genes by conducting a series of pathway enrichment analyses. In the STRING database, we obtained a total of 50 PCLAF-binding proteins, which are supported by evidence of Co-expression. Figure 6A shows the interaction network of these proteins. We use the GEPIA2 tool to combine all tumor expression data of TCGA to obtain the top 100 genes related to PCLAF expression. Then an intersection analysis was conducted among the PCLAF-binding and correlated genes by Venn (http://bioinformatics.psb.ugent.be/webtools/Venn/), and 36 common genes were obtained (Figure 6B). The functions of 36 genes were predicted by analyzing GO and KEGG in Metascape.

We found that these genes are mainly enriched in the "mitotic cell cycle process," "cell division," "Cell Cycle," "DNA replication," and so on (Figure 6C, D). As shown in Figure 6E, the PCLAF expression level was positively correlated with that of BUB1B (BUB1 mitotic checkpoint serine/threonine kinase B) (R = 0.6), CCNB1 (Cyclin B1) (R = 0.63), CDC45 (Cell division cycle 45) (R =0.63), DLGAP5 (DLG associated protein 5) (R = 0.6) and PCNA (Proliferating cell nuclear antigen) (R = 0.69) genes (all P < 0.01). The heatmap data also shows that PCLAF is positively correlated with the above five genes (Figure 6F). During mitosis, transcription factors can maintain the ability to bind to target cells and nucleosome arrays. Due to the importance of PCLAF in cancer, we explored the transcription factors that regulate PCLAF. We obtained 20 TFs regulating PCLAF in the CHEA database. These TFs were displayed as a bubble plot based on correlation in 14 tumors (Supplemental Figure6A, B). These transcription factors are mainly involved in the cell cycle, apoptosis regulation, DNA damage repair, and other pathways. In 75%, 69% and 53% of selected cancer types, E2F1, FOXM1 and E2F7 may regulate PCLAF and activate cell cycle pathways (Supplemental Figure6C D). The previous studies reported that FOXM1 could regulate PCLAF to predict poor prognosis in high-grade serous ovarian cancer patients[28].

3.6 PCLAF promotes the proliferation of liver cancer cells and inhibits apoptosis

In order to better explore the function of PCLAF in cancer, we selected the liver cancer cell line (HepG2) for verification. We knocked down PCLAF by short hairpin RNA (shRNA), and performed the following experiments after verifying the interference efficiency (Figure 7A). We transfected the HepG2 cell line with shPCLAF, and used CCK-8 to continuously monitor cell proliferation at 24, 48, 72, and 96 h after transfection. Interestingly, the cell viability decreased significantly after silencing PCLAF, indicating that the reduction of PCLAF expression can inhibit the proliferation of liver cancer cells (Figure 7B). At the same time, the cell cycle analysis results showed that reducing the expression of PCLAF in HepG2 cell line can increase the percentage of cells in the G1 phase of the cell cycle (Figure 7C). The results of the EDU experiment showed that after reducing the expression of PCLAF, the percentage of positive signals labeled with EDU was significantly reduced (Figure7D), indicating that the DNA replication activity of the cells was reduced and the cell proliferation ability was weakened. Apoptosis analysis showed that after the PCLAF gene was knocked down, the average percentage of apoptosis increased significantly (Figure 7E). Western blot was further used to detect cell proliferation and apoptosis-related proteins. Attenuating the expression of PCLAF protein can significantly down-regulate CyclinA2, Cyclin B1, CyclinD1, CyclinE2, CDK2, CDK6, Bcl-2 and enhance the expression of PARP, Cleaved PARP, Caspase3, Cleaved Caspase 3, Bax (Figure 7F, G). In conclusion, we proved that PCLAF can promote the proliferation of liver cancer cells and inhibit cell apoptosis in vitro.

Increasing publications have shown that PCLAF protein is involved in a series of cell biology events, such as cell cycle regulation, DNA replication, DNA repair, and cell survival. More and more studies have confirmed the functional interaction between PCLAF and tumors[8, 10, 11, 29, 30]. The pathogenesis of PCLAF in different tumors is still unclear, and further research is needed. After a comprehensive literature search, we did not find any publications on PCLAF pan-cancer analysis. Our research illustrates that computational biology can discover the molecular biological mechanisms by which PCLAF affects tumor progression. In this study, PCLAF played a prognostic role in the pan-cancer and tumor microenvironment, which provided clues to understand the prognosis and immune effects of PCLAF in different tumors.

This study used the TCGA data of ONCOMINE, GEPIA, and TIMER to explore the expression level of PCLAF in different tumors and visualized its prognosis in pan-cancer. In ONCOMINE, we found that the expression level of PCLAF was only low in breast cancer, and the other tumors showed high expression status. The TCGA data analysis in TIMER shows that PCLAF is in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, HNSC-HPV, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, PRAD, READ, HNSC, SKCM, STAD, THCA, and UCEC is higher than that of normal adjacent tissues. In addition, we found that the expression of PCLAF increased with the clinical stage from stage I to stage IV, including ACC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, and PAAD. We used the GEPIA2 tool to analyze the relationship between PCLAF gene expression and the overall and disease-free survival of different tumors in TCGA. We found that high expression of PCLAF is associated with poor prognosis of OS in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and PAAD.DFS analysis data showed that in ACC, KIRP, LGG, LIHC, LUAD, MESO, PRAD, UV tumors, patients with high PCLAF expression have a worse prognosis. In summary, these findings strongly indicate that PCLAF can be used as a biomarker for pan-cancer prognosis.

It is generally believed that cancer is caused by genetic mutations, which biologically enhance the resistance of cancer cells to surrounding normal cells[31-33]. At present, advances in systems biology methods provide us with a large amount of data to identify molecular alterations and explore the heterogeneity of cancer cells[34-36]. We explored the mutation pattern and amplification frequency of PCLAF in different tumors by using the CbioPortal tool. We found that the most common DNA change in the TCGA pan-cancer group is amplification. Then we analyzed the correlation between genetic diseases and PCLAF expression and found that mutations have nothing to do with RNA expression status. In addition, we found that mutations and DNA copy variations are also independent of PCLAF expression. Therefore, genetic variation may not be the factor that causes the high expression of PCLAF in tumors. Then we assessed the epigenetic disorders of PCLAF in cancer and found that DNA methylation may be the cause of abnormal expression of PCLAF in tumors, but it is not the only cause.

Another significant finding of this study is that the expression of PCLAF is associated with diverse levels of immune infiltration in cancer. In TGCT tumors, we observed that the expression of PCLAF was statistically positively correlated with the estimated value of cancer-related fibroblast infiltration. At the same time, it was statistically negatively correlated in BRCA, COAD, HNSC, STAD, and THYM. In the diseases in the TISIDB database, the expression of PCLAF is positively correlated with activated CD4 T cells (Act CD4) and type 2 T helper cells (Th2), suggesting that PCLAF may play a specific role in tumor immune infiltration. Mounting evidence had demonstrated that TME (tumor microenvironment) plays a predominant role in the occurrence and development of tumors, which may accelerate the deterioration of tumors[37, 38]. Among the TISIDB database, in BRCA, glioma, NSCLC, and UCEC, PCLAF is highly expressed in CD8 T cells, regular CD4 T cells, CD8-poor T cells, monocytes or macrophages, and proliferating T cell fibroblasts, which suggested that PCLAF is closely related to tumor TME.

In addition, we integrated information on PCLAF binding components and PCLAF expression-related genes in all tumors. We performed a series of enrichment analyses, which revealed that most of the enriched pathways were related to the "mitotic cell cycle process," "cell division," "Cell Cycle," "DNA replication." Decreasing the expression of PCLAF can inhibit the proliferation of undifferentiated thyroid cancer and cervical cancer cell lines, DNA synthesis and cell viability of pancreatic cancer cell lines, leading to an increase in the number of G0/G1 cells in adrenocortical cancer cell lines and cervical cancer cell lines [6, 39, 40]. These indicate that PCLAF may cause cancer cell proliferation by promoting cell cycle progression. We obtained 20 TFs regulating PCLAF, mainly involved in the cell cycle, apoptosis regulation, DNA damage repair, and other pathways. Above results indicated that PCLAF could participate in carcinogenesis under the regulation of these TFs. Most importantly, our analysis of liver cancer cell lines indicated that PCLAF can enhance the proliferation of liver cancer cells and inhibit cell apoptosis in vitro, but the mechanism by which PCLAF promotes tumor cell proliferation will require further exploration.

In summary, our first pan-cancer analysis of PCLAF shows that PCLAF expression is statistically correlated with clinical prognosis, DNA methylation, and immune cell infiltration, which helps to understand the role of PCLAF in tumorigenesis from the perspective of clinical tumor samples.

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)

MM (multiple myeloma)

MCC (Merkel cell carcinoma)

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 Tumor)

THCA (Thyroid carcinoma)

THYM (Thymoma)

UCEC (Uterine Corpus Endometrial Carcinoma)

UCS (Uterine Carcinosarcoma)

UVM (Uveal Melanoma)

TCGA (The cancer genome atlas)

GEO (Gene expression omnibus)

TIMER2 (Tumor immune estimation resource)

CNA (Copy number alteration)

HCC (Hepatocellular carcinoma)

OS (Overall survival)

RFS (Relapse-free survival)

DFS (Disease-free survival)

DMFS (Distant metastasis-free survival)

FP (First progression)

KEGG (Kyoto encyclopedia of genes and genomes)

GO (Gene ontology)

TILs (Tumor infiltrating lymphocytes)

TME (Tumor microenvironment)

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analyzed during this research period are included in this article and additional files.

Competing interests

The authors declare no potential conflicts of interest.

Funding

This work is supported by Shanghai Jiao Tong University School of Medicine Doctoral Innovation Fund (No. BXJ201826) to Dr. Kai Chen and Natural Science Foundation of China (No. 81874234) to Dr. Zhixiang Wu.

Authors' contributions

XW Liu conceived and designed the study and drafted the manuscript. YX Cai, C Cheng explain the data and prepare the data. XW Liu, YY Gu, YK Wu collect and analyze data. K Chen and ZX Wu revised the manuscript and finally approved the version to be released. All authors read and approved the final manuscript.

Acknowledgments

Not applicable.

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FigureS1.tif
Supplemental Figure 1 | PCLAF has different expression levels in other cells, tissues, and plasma. We analyzed the expression of the PCLAF gene in different tissues using the consensus datasets of HPA, GTEx, and FANTOM5 (A) or in other blood cells using the consensus dataset of HPA, Monaco, and Schmiedel (B). Based on the consensus datasets of HPA, GTEx, and FANTOM5, we also analyzed the PCLAF expression in different cell lines (C).
FigureS2.tif
Supplemental Figure 2 | Expression level of PCLAF gene in different pathological stages (A) Based on the TCGA data, the expression levels of the PCLAF gene were analyzed by the main pathological stages (stage I, stage II, stage III, and stage IV) of ACC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, and PAAD. Log2 (TPM + 1) was applied for log-scale. (B) We use the Kaplan-Meier database to analyze the expression levels of the PCLAF gene in breast, liver, and lung cancers to perform a series of survival analyses, including OS, DMFS, RFS, PFS, PPS, FP, and DSS.
FigureS3.tif
Supplemental Figure 3 | A forest plot shows the correlation of PCLAF mRNA expression with OS (n = 1925) and PFS (n = 982) in LUAD with different clinicopathological features.
FigureS4.tif
Supplemental Figure 4 | DNA methylation aberration of PCLAF in tumors (A) Eight highly PCLAF express tumors presented with decreased DNA methylation level of PCLAF, including CHOL, CESC, GBM, PCPG, SARC, TGCT, STAD, UCEC. *p < 0.05. This data was obtained using Ualcan (B) The DNA methylation level of different stages of KICH. (C)DNA methylation level of PCLAF in BRCA and COAD remained unchanged.
FigureS5.tif
Supplemental Figure 5 | Correlations of PCLAF expression with immune infiltration level. (A)The relationship of PCLAF and infiltration level of cancer-associated fibroblasts across THYM and TGCT. (B) Relations between the expression of PCLAF and 28 types of TILs across human cancers. (C) PCLAF was correlated with the abundance of Activated CD4 T cell (Act CD4) and Type 2 T helper cell (Th2).
FigureS6.tif
Supplemental Figure 6 | TF regulatory networks. (A)TFs network; (B) Bubble plot of TFs alterations in 14 cancers; (C) Enrichment score of TFs in 14 cancer types. (D) Heat map presents the percentage of cancers in which a gene has the effect (FDR <=0.05) on the pathway among selected cancers types; the number in each cell indicates the percentage.

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published 14 Apr, 2022

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Pan-cancer Analyses Reveal Regulation and Clinical Outcome Association of the PCLAF in Human Tumors

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Journal Publication

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Gene expression analysis

2.2 Survival prognosis analysis

2.3 Genetic Alterations

2.4 Immune infiltration analysis

2.5 Regulatory Networks of Transcription Factors

2.6 Tumor-Immune System Interaction Database (TISIDB) and Tumor Immune Single Cell Hub Database (TISCH)

2.7 PCLAF-related gene enrichment analysis

2.8 Cell culture and lentivirus-mediated silencing of PCLAF

2.9 Cell viability analysis

2.10 EDU incorporation test

2.11 Cell cycle detection

2.12 Annexin V-FITC/propidium iodide (PI) flow cytometry

2.13 Western blotting

2.14 Quantitative real-time PCR

2.15 Statistical Analysis

3. Results

3.1 mRNA Expression Level of PCLAF in Pan-Cancer

3.2 Multifaceted Prognostic Value of PCLAF in Cancers Survival analysis data

3.3 Genetic alteration analysis data

3.4 Immune infiltration analysis data

3.5 Enrichment analysis of PCLAF-related partners

3.6 PCLAF promotes the proliferation of liver cancer cells and inhibits apoptosis

Discussion

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgments

References

Supplementary Files

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Journal Publication

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