Prognostic value of p16, p53, and pcna in sarcoma and an evaluation of immune infiltration.

BACKGROUND: p16, p53, and proliferating cell nuclear antigen (pcna) genes play significant roles in many chromatin modifications and have been found to be highly expressed in a variety of tumor tissues. Therefore, they have been used as target genes for some tumor therapies. However, the differential expressions of the p16, p53, and pcna genes in human sarcomas and their effects on prognosis have not been widely reported.
METHODS: The Oncomine dataset was used to analyze the transcription levels of p16, p53, and pcna genes, and the gene expression profile interactive analysis (GEPIA) dataset was used to analyze the differential expressions of p16, p53, and pcna. The expression levels of p16, p53, and pcna were further analyzed by Western Blotting. GEPIA and Kaplan-Meier analyses were used to analyze the prognostic value of p16, p53, and pcna. Furthermore, p16, p53, and pcna gene mutations and their association with overall survival (OS) and disease-free survival (DFS) were analyzed using cBioPortal datasets. In addition, genes co-expressed with p16, p53, and pcna were analyzed using Oncomine. The DAVID dataset was used to analyze the functional enrichment of p16, p53, pcna, and their co-expressed genes by Gene Ontology (GO) and Metascape were used to construct a network map. Finally, the immune cell infiltration of p16, p53, and pcna in patients with sarcoma was reported by Tumor Immune Estimation Resource (TIMER).
RESULTS: p16, p53, and pcna were up-regulated in human sarcoma tissues and almost all sarcoma cell lines. Western Blotting showed that the expression of p16, p53, and pcna was elevated in osteosarcoma cell lines. The expression of pcna was correlated with OS, the expression of p16, p53, and pcna was correlated with relapse-free survival, and the genetic mutation of p16 was negatively correlated with OS and DFS. We also found that p16, p53, and pcna genes were positively/negatively correlated with immune cell infiltration in sarcoma.
CONCLUSIONS: The results of this study showed that p16, p53, and pcna can significantly affect the survival and immune status of sarcoma patients. Therefore, p16, p53, and pcna could be used as potential biomarkers of prognosis and immune infiltration in human sarcoma and provide a possible therapeutic target for sarcoma.

Introduction

Sarcoma is a rare group of heterogeneous tumors, mainly from the bone and soft tissue, and is highly invasive malignant tumors [1, 2]. Sarcomas can occur in almost all age groups; however, they are more common in adolescents and children and account for 10% of the malignant tumors in children and adolescents [3]. The exact etiopathogenesis of sarcomas is still unclear and needs further research. Osteosarcoma is one of the leading causes of cancer deaths in young people [4, 5]. Childhood and adolescents osteosarcoma has a high incidence [6, 7] and is reported at about 5% [8]. Osteosarcoma has a high rate of metastasis, and the effect of treatment and prognosis are poor [9]. Although surgery combined with chemotherapy slightly improves the survival rate of patients, there has been no substantial improvement in the survival rate in the past 40 years. In addition, the prognosis of patients with metastatic osteosarcoma is very poor, and the overall survival (OS) rate is 30% [10, 11].

Of the tumor suppressor genes, p16 and p53 are very important. Once inactivated, there is a proliferation of malignant cells. With a tumor suppressor gene protein product cdkn2a, p16 mutations seriously affect the progression and prognosis of many tumors. p53 is an important tumor suppressor gene [12, 13]. The p53 gene mutation rate is very high. Due to changes in its spatial conformation, the p53 gene loses the ability to regulate cell growth, cell apoptosis, and DNA repair, thus progressing from a tumor suppressor gene to a cancer gene. Proliferating cell nucleus antigen (pcna) plays an important role in DNA replication and repair. It forms a DNA containing homology trimer ring, anchoring along its side DNA polymerase and other editing enzymes that regulate the DNA and protein sequence motif of pcna—interacting protein tape (PIP—box), thereby influencing the development of tumor [14].

According to previous research, p16, p53, and pcna genes affect the development of various tumors in many ways. Ishida et al. reported, for example, that the lack of p16 gene was associated with a 91% prevalence of esophageal squamous cell carcinoma and showed that the overexpression of the p16 was caused by an imbalance in the RB1 pathway [15]. Wang and others reported the highest p53 gene mutation frequency in liver cancer patients in China [16]. Ho et al. show that pcna was highly expressed in colon adenocarcinoma, with the expression of pcna in the distant metastasis of tumor as well [17]. However, the mechanisms of p16, p53, and pcna genes in sarcoma have not been widely reported.

The aim of this work was to study the expression of p16, p53, and pcna gene in sarcomas and evaluate its prognostic significance and association with immune cell infiltration.

Results

Transcription of p16, p53, and pcna in patients with sarcoma

Based on Oncomine database data, we found high p16, p53, and pcna expressions in sarcoma (Fig. 1). In addition, the differential transcriptional expression levels of p16, p53, and pcna in sarcoma subtypes and corresponding normal tissues were analyzed using the Detwiller and Barretina sarcoma databases. The expression level of p16 in the Detwiller's sarcoma database was up-regulated, and the multiple changes in these genes in leiomyosarcoma, pleomorphic liposarcoma, round cell liposarcoma, dedifferentiated liposarcoma, and malignant fibrous histiocytoma were 21.899, 25.731, 11.632, 4.897, and 4.789, respectively. Analysis of the Barretina sarcoma database showed that compared with normal tissue, multiple changes of p16 expression levels in dedifferentiated liposarcoma, leiomyosarcoma, pleomorphic liposarcoma, myxoid fibrosarcoma, and myxoid/round cell liposarcoma were 6.870, 5.963, 6.395, 4.967, and 1.300, respectively.

Fig. 1

Expression levels of p16, p53, and pcna genes in different types of human cancer and normal samples

According to the Detwiller sarcoma database analysis, multiple changes in p53 expression in synovial sarcoma and fibrosarcoma compared with normal tissues were 2.660 and 1.885, respectively. In the Barretina sarcoma database, multiple changes in p53 expression in myxoid/round cell liposarcoma and dedifferentiated liposarcoma compared with normal tissues were 4.235 and 1.755, respectively.

Based on the Detwiller sarcoma database analysis, the multiple changes in pcna expression in pleomorphic liposarcoma, leiomyosarcoma, fibrosarcoma, synovial sarcoma, and malignant fibrous histiocytoma were 3.836, 3.155, 3.750, 2.385, and 3.444, respectively. In the Barretina sarcoma database, the multiple changes of pcna expression in pleomorphic liposarcoma, leiomyosarcoma, myxofibrosarcoma, dedifferentiated liposarcoma, and myxoid/round cell liposarcoma were 4.323, 5.521, 4.905, 2.874, and 2.492, respectively.

mRNA expression of p16, p53, and pcna in sarcoma

Using the GEPIA tool, we found that the p16 and pcna mRNA levels were significantly raised in sarcoma compared to normal tissue (P > 0.05), although the level of mRNA expression of p53 showed no significant increase (Fig. 2A–G).

Fig. 2

Expression levels of p16, p53, and pcna genes in sarcomas. A–C The expression levels of p16, p53, and pcna in generalized carcinoma, and D–G the expression levels of p16, p53, and pcna in sarcoma. Each point represents a separate sample. H Expression of p16, p53, and pcna in normal osteoblasts and two osteosarcoma cell lines by Western Blotting. *P < 0.05

To further evaluate the expression of p16, p53, and pcna in osteosarcoma, we selected two osteosarcoma cell lines to detect their protein expression. Normal osteoblasts constituted the control group. Western Blotting results showed that the expression of p16, p53, and pcna in various osteosarcoma cell lines was significantly higher than that in normal osteoblasts (Fig. 2H), consistent with the results of the Oncomine and GEPIA database analysis.

Prognostic value of p16, p53, and pcna gene in sarcoma patients

Kaplan–Meier plots and GEPIA evaluation demonstrated the prognostic effect of p16, p53, and pcna gene expression in sarcoma patients. The expression of pcna was related to a poor 5- and 10-year OS. With the increase in p53 expression, the 5-year recurrence-free survival (RFS) was up-regulated. The increase in pcna expression was associated with a lowering of the 5- and 10-year RFS in sarcoma, and in recurrent sarcomas, pcna was found to be differentially expressed (Fig. 3). p16 tended to affect the RFS in sarcoma, but this was not statistically significant. We found an inverse correlation between pcna expression and the OS in sarcoma patients. The expression of p16, p53, and pcna was not related to DFS in patients with sarcoma (Fig. 4).

Fig. 3

K-M survival curve. A Association between high expression levels of p16, p53, and pcna genes and 5-year overall survival in patients with sarcoma. B Association between elevated levels of p16, p53, and pcna gene expression and 5-year relapse-free survival (RFS) in patients with sarcoma. C Association between high expression levels of p16, p53, and pcna genes and 10-year overall survival in patients with sarcoma. D Association between elevated levels of p16, p53, and pcna gene expression and 10-year relapse-free survival in patients with sarcoma

Fig. 4

GEPIA survival curve. A Association between elevated p16, p53, and pcna gene expression and overall survival in patients with sarcoma. B Association between expression levels of p16, p53, and pcna genes and disease-free survival (DFS) in patients with sarcoma

Mutations in p16, p53, and pcna genes and their effects on OS and DFS in sarcoma patients

Next, we analyzed the mutations of p16, p53, and pcna genes and its influence on OS and DFS in sarcoma. High mutation rates of p16, p53, and pcna were observed in sarcomas; 154 of the 254 sarcomas sequenced showed gene mutations with a mutation rate of 61%. Of the three, p16 and p53 gene mutation rates were relatively high, at 17 and 50%, respectively. pcna gene mutation rate was 6% (Fig. 5A). In addition, the results of Kaplan–Meier survival analysis showed that genetic mutations in p16 represented a decrease in OS (Fig. 5B, P = 8.164E − 4) and DFS (Fig. 5C, P = 0.0130) in sarcoma patients. The high frequency of p53 gene mutations tends to be negatively correlated with DFS in sarcoma, but it is not significant (P = 0.0563). The pcna gene mutations were not shown to significantly affect the OS and DFS in sarcoma patients. These results suggest that p16 and p53 gene mutations may significantly influence the outcome of patients with sarcoma.

Fig. 5

Gene mutations in p16, p53, and pcna and their association with OS and DFS in patients with sarcoma (cBioPortal). High mutation rates of p16, p53, and pcna (61%) were observed in SARC patients, with mutation rates of 17, 50, and 6%, respectively (A). Relationship between genetic changes of p16, p53, and pcna and OS (B) and DFS (C) in SARC patients

Identification of genes co-expressed with p16, p53, and pcna genes in osteosarcoma

We used the Oncomine dataset to detect genes co-expressed with p16, p53, and pcna in osteosarcoma. The genes co-expressed with p16 were rnf17, gcnt4, sp140, dio1, acan, clc, tyrobp, grik3, a4gnt, and cacna1c. The genes co-expressed with p53 were chaf1a, gdf15, ap1g2, abcb9, tk1, tle2, mapkapk3, fli1, hip1r, and rfc2. The genes co-expressed with pcna were hist2h4a, ccl2, s100a2, cbs, pdgfrl, nr4a1, nck2, trim27, atp6v1b2, and tk2 (Fig. 6A).

Fig. 6

A Genes co-expressed with p16, p53 and pcna genes in osteosarcoma. B Correlation between p16, p53, and pcna genes in sarcomas

Then, we analyzed the co-expression between p16, p53, and the pcna gene. The results showed that p16 and pcna were positively correlated (R = 0.29, P < 0.05), p53, and p16 showed negative correlation (R = 0.17, P < 0.05). There was no significant correlation between p53 and pcna (Fig. 6B).

Next, the enrichment of David analysis showed that p16, p53, and pcna and co-expressed genes mainly involved were: GO:0,046,104 (thymidine metabolic process), GO:0,007,265 (Ras protein signal transduction), GO:0,048,010 (vascular endothelial growth factor receptor signaling pathway), GO:0,009,157 (deoxyribonucleoside monophosphate biosynthetic process), and GO:0,031,497 (chromatin assembly) of biological processes. Cellular components (CCs), GO:0,005,663 (DNA replication factor C complex), GO:0,016,605 (promyelocytic leukemia [PML] body), GO:0,005,654 (nucleoplasm), GO:0,005,657 (replication fork), and GO:0,032,839 (dendrite cytoplasm) were enriched, and molecular functions (MFs): GO:0,042,802 (the same protein binding): GO:0,004,797 (thymidine kinase activity): GO:0,005,515 (protein binding): GO:0,019,206 (nucleoside kinase activity), GO: 0,019,899 (binding) were enriched (Fig. 7 and Table 1).

Fig. 7

GO analysis of p16, p53, and pcna genes and co-expressed genes

Table 1

GO analysis of p16, p53, pcna, and co-expressed genes

The enriched	GO-ID	Description	Count	P Value	Genes
BP	0,046,104	Thymidine metabolic process	2	0.00380782	tk2, tk1
BP	0,007,265	Ras protein signal transduction	3	0.007837465	cdkn2a, tp53, mapkapk3
BP	0,048,010	Vascular endothelial growth factor receptor signaling pathway	3	0.00827545	nck2, ccl2, mapkapk3
BP	0,009,157	Deoxyribonucleoside monophosphate biosynthetic process	2	0.009493227	tk2, tk1
BP	0,031,497	Chromatin assembly	2	0.015147194	tp53, chaf1a
BP	0,043,065	Positive regulation of apoptotic process	4	0.019103663	hip1r, cdkn2a, tp53, nr4a1
BP	0,007,165	Signal transduction	7	0.020711051	nck2, tle2, tyrobp, ccl2, gdf15, mapkapk3, nr4a1
BP	0,016,925	Protein sumoylation	3	0.020834415	cdkn2a, pcna, tp53
BP	0,090,399	Replicative senescence	2	0.022637202	cdkn2a, tp53
BP	0,043,097	Pyrimidine nucleoside salvage	2	0.022637202	tk2, tk1
BP	0,009,165	Nucleotide biosynthetic process	2	0.028218465	tk2, tk1
BP	0,006,260	DNA replication	3	0.035043323	pcna, rfc2, chaf1a
BP	0,042,276	Error-prone translesion synthesis	2	0.035612139	pcna, rfc2
BP	0,070,987	Error-free translesion synthesis	2	0.035612139	pcna, rfc2
BP	0,006,297	Nucleotide-excision repair, DNA gap filling	2	0.044777623	pcna, rfc2
BP	0,071,158	Positive regulation of cell cycle arrest	2	0.046600567	cdkn2a, tp53
BP	0,007,507	Heart development	3	0.047320182	acan, pcna, cacna1c
BP	0,071,897	DNA biosynthetic process	2	0.04842014	tk2, tk1
CC	0,005,663	DNA replication factor C complex	2	0.010164404	pcna, rfc2
CC	0,016,605	PML body	3	0.012026098	tp53, sp140, trim27
CC	0,005,654	Nucleoplasm	11	0.013931808	cdkn2a, tle2, flii, pcna, tp53, rfc2, sp140, trim27, mapkapk3, nr4a1, hist2h4a
CC	0,005,657	Replication fork	2	0.028540071	pcna, tp53
CC	0,032,839	Dendrite cytoplasm	2	0.030194119	hip1r, grik3
CC	0,043,234	Protein complex	4	0.032294565	cdkn2a, tp53, chaf1a, hist2h4a
MF	0,042,802	Identical protein binding	8	4.19E-04	s100a2, pcna, tp53, cbs, tyrobp, trim27, chaf1a, tk1
MF	0,004,797	Thymidine kinase activity	2	0.003787763	tk2, tk1
MF	0,005,515	Protein binding	25	0.006734124	s100a2, hip1r, tle2, rfc2, sp140, nr4a1, tk1, hist2h4a, acan, cacna1c, clc, cbs, gdf15, ap1g2, chaf1a, nck2, pcna, tp53, abcb9, cdkn2a, flii, tyrobp, atp6v1b2, trim27, mapkapk3
MF	0,019,206	Nucleoside kinase activity	2	0.018800305	tk2, tk1
MF	0,019,899	Enzyme binding	4	0.024733293	pcna, brox, gabrd, iqch
MF	0,003,677	DNA binding	8	0.034312324	cdkn2a, pcna, tp53,rfc2, sp140, trim27, neu2, hist2h4a

To intuitively show the relationship between the enrichment of each gene (p16, p53, and pcna and their co-expressed genes), we used Metascape to construct a network diagram, the cytoscape visualization network with each node representing a rich group (Fig. 8).

Fig. 8

A and B are colored by p values, where items containing more genes have more significant p values, and C and D are colored by cluster IDS, where nodes sharing the same cluster ID are usually close to each other

Expression of p16, p53, and pcna genes and degree of immune infiltration in sarcoma

In this study, the TIMER was used to explore the correlation between the expression of p16, p53, and pcna genes and immune cell infiltration within the sarcoma. TIMER analysis showed that p16 and infiltrating neutrophils showed a significant correlation (R = 0.102, P < 0.05). Similarly, the expression of p53 and B lymphocyte infiltration also showed a significant correlation (R = 0.103, P < 0.05), and so did CD8 + T cells (R = 0.117, P < 0.05), macrophages (R = 0.184, P < 0.05), neutrophils (R = 0.122, P < 0.05), and dendritic cells (R = 0.197, P < 0.05). The expression of pcna and B lymphocyte infiltration showed a significant correlation (R = 0.1, P < 0.05), so did CD4 + T cells (R = 0.17, P < 0.05), and macrophages (R = 0.122, P < 0.05) (Fig. 9).

Fig. 9

Relationship between differential expression of p16, p53, and pcna genes and immune cell infiltration

Discussion

p16, p53, and pcna genes have been shown to play an irreplaceable role in the occurrence and development of a variety of malignant tumors. However, gene differential expression of p16, p53, and pcna in sarcoma and its influence on prognosis have not been widely reported. At present, tumor immunotherapy has become a research hotspot, and reports have said that effective immunotherapy has been carried out for many different types of malignant tumors [18-22]. In addition, clinical studies have made some progress in detecting personalized new vaccine antigens in patients with tumors [23]. There is growing attention toward immune-related research in sarcomas and evaluation of immunotherapy combined with radiotherapy, chemotherapy, and some targeted treatment. We conducted this study to reveal the relationship between p16, p53, and pcna genes in human sarcomas with patient prognosis and immune infiltration.

The p16 gene is a very important anticancer gene. Once inactivated, malignant proliferation of cells occurs. At present, the lack of homozygous, nonsense, missense, and p16 frameshift mutations was found in many types of tumor tissue, showing that p16 gene deletions and mutations are widely involved in tumor formation. Zeng et al. [24] reported a method to monitor the direct effects of the genetic damage of p16 on low-channel primary human melanocytes using precision engineering and digital holography. They observed that the deletion of p16 promotes melanocyte movement through transcriptional activation of brn2, as well as increases the invading and metastasizing ability of the melanoma cells, thereby showing that p16 deletion leads to melanoma progression. Sarun et al. [25] reported that homozygous deletion of cdkn2a is one of the most common gene mutations in malignant pleural mesothelioma. Tirode et al. [26] reported that p16 gene mutation frequency of Ewing's sarcoma was 12%. Campbell et al. [27] reported the important role of cdkn2a (p16) gene in the etiology of pancreatic cancer and thought it to be one of the most common mutations in pancreatic cancer. They also found that cdkn2a promoter methylation plays a key role in the pathogenesis and prognosis of pancreatic cancer [36]. Previous research has shown that changes in the p16 gene can significantly affect the prognosis of patients with many types of tumors. Botana-Rial et al. [28] reported that patients with p16 ink4a/methylation have a shorter survival time than those without methylation. Pessôan et al. [29] reported that p16 has a high mutation rate in gliomas, and the higher the degree of malignancy of gliomas, the higher the frequency of p16 mutation (16.7%) and the worse the prognosis. Trietsch and others reported that p16 was often mutated in human papilloma virus-negative vulvar squamous cell carcinoma, and the 5-year survival rate of patients with obvious mutations was worse than patients without mutations [30]. El-mokadem et al. [31] found that when patients with renal clear cell carcinoma developed p16 gene mutation, the degree of malignancy was usually high, and p16 gene mutation increased the risk of tumor metastasis, leading to higher tumor recurrence and metastasis, which was associated with poor prognosis. These results are similar to the results of our study. The genetic variation rate of p16 is more common in sarcoma patients (17%), and genetic mutations of p16 are associated with poor OS and DFS. It is reported that overexpression of p16 in traditional esophageal squamous cell carcinoma is associated with a better clinical prognosis [32, 33]. Zhou et al. [34] found that p16 positive lung adenocarcinoma had a good prognosis. Kommoss et al. [35] found that p16-negative status was an indicator of poor prognosis in clear cell ovarian cancer and mucinous ovarian cancer subgroups. Barber et al. [36] found that in oropharyngeal squamous cell carcinoma, up-regulation of p16 expression implied better RFS. In the current study, the up-regulation of p16 expression was found to increase the RFS of sarcoma patients, but this was not statistically significant (P = 0.052). There was no significant correlation between the expression of p16 and OS in patients.

As a transcription factor, p53 suppresses cancer mainly through selective transcriptional regulation of multiple target genes, including regulation of apoptosis, cell cycle arrest, senescence, DNA repair, and metabolism [37-39]. The existing research is the same as the results of this work; the mutation rate of the p53 gene is so high that it occurs in more than 50% of human malignant tumors [40-42]. In many human tumors, p53 plays an important role in tumorigenesis and progression through gene mutation and other mechanisms, including the amplification and/or overexpression of p53-negative regulators, such as mdm2 and mdm4 [43]. It has been reported that mutation or downregulation of p53 (encoded by tp53) accelerates the occurrence and malignant progression of esophageal squamous cell carcinoma [44]. Hou et al. [45] found that the Claudin 7 closed protein gene (CLDN7) located downstream of p53 on the short arm of chromosome 17 is regulated by WTp53 by binding to the promoter region of colorectal cancer. Once p53 mutation or deletion occurs, the tumor inhibitory function of CLDN7 is lost, indicating that the tumor inhibitory effect of CLDN7 in colorectal cancer is closely related to the status of p53. Rodrigues et al. [46] have shown that 80% of patients with mantle cell lymphoma with tp53 mutations die within the first 5 years of diagnosis. Marcus and Ladds proposed the use of dihydrowhey acid dehydrogenase and p53 activation as tumor treatment targets, thus effectively killing tumor cells [47]. Takamatsu et al. proposed that inhibiting p53 aggregation through various methods, that is, reducing the evolutionary ability of p53 to nuclear stress, may enhance the effectiveness of cancer treatment and reduce cancer recurrence [48]. George et al. [49] reported that the mutation of the tumor suppressor gene tp53 is associated with poor survival of patients with acute myeloid leukemia and proposed a new molecular targeted therapy with the main goal of degrading or inactivating mutant p53 or restoring WTp53 to restore normal tp53 function. Saleh et al. [50] reported that p53 gene mutations are very frequent in high-grade serous ovarian cancer (HGSC), while the frequency of p53 mutations in normal organs is relatively low, making p53 an attractive target for specific therapy of HGSC. These results were similar to ours; the high-frequency genetic changes in p53 were associated with poor DFS in sarcoma patients, but this was not statistically significant (P = 0.0563).

As a hub protein, pcna is considered to be a key regulator of DNA and cell cycle regulation. The expression of pcna is found to be up-regulated in many tumor types, and its overexpression is thought to be related to cancer virulence [51]. Tumor cells have strong proliferative activity and can be used as an index to evaluate the state of cell proliferation. Therefore, pcna has been studied in many tumors, involving the relationship between pcna and tumor occurrence, development, prognosis, recurrence and metastasis, tumor markers, and so on. It is known that high expressions of pcna can promote the proliferation of lung cancer cells and the ability to invade adjacent tissues; therefore, it can be used as a new molecular targeted marker for the diagnosis and treatment of lung cancer [52-54]. Jin et al. [55] have shown that excessive up-regulation of pcna expression is one of the factors that can directly affect the prognosis of rectal cancer, which significantly reduces the survival rate of patients. Wang et al. [56] found that the transforming growth factor (TGF)-β1 signaling pathway may affect cell growth, cell cycle distribution, and apoptosis by regulating the expression of pcna and other molecules in hepatocellular carcinoma. Hu et al. [57] reported that pcna expression in gastric cancer tissues was significantly up-regulated compared with normal tissues, and the positive expression of pcna is a risk factor for the prognosis of patients with gastric cancer. Qin et al. [58] reported that in glioblastoma (GBM), a type of microRNA (miR-1258), inhibits pcna transcription by directly targeting e2f1, which provides a new potential target for GBM therapy and other e2f1-driven cancers. According to the above reports, the up-regulated expression of pcna is related to the poor prognosis of patients with tumors, which is similar to the results we analyzed using Kaplan–Meier graphs, indicating that the increased expression of pcna is related to poor 5- and 10-year OS and RFS in sarcoma patients. Thus, the up-regulation of pcna expression means that patients with sarcoma are more likely to relapse.

A growing body of evidence indicates that the tumor microenvironment plays an important role in the regulation of the proliferation and metastasis [59, 60]. A variety of immune cells in the tumor microenvironment can promote or inhibit the activity of tumor cells. The tumor microenvironment is thought to be an important determinant of the clinical prognosis and response to immunotherapy [61, 62]. Adib et al. [63] found that patients with cdkn2a -altered urothelial cancer had decreased pd-l1 expression in tumor-infiltrating immune cells, as well as decreased T cell receptors, antigen processing, and activation of interferon-gamma pathways. In urothelial carcinoma, the high expression of pd-l1 in tumor-infiltrating immune cells is associated with an enhanced efficacy of immune checkpoint inhibitors and a good prognosis. After systematically exploring the differentially expressed genes related to immunity in hepatocellular carcinoma, Luo et al. [64] concluded that the high expression of cdkn2a is associated with a poor prognosis of hepatocellular carcinoma and a decrease in immune infiltration. The up-regulation of cdkn2a expression in hepatocellular carcinoma may be related to the involvement of cdkn2a in the MAPK signaling pathway and the diversity of hepatocellular carcinoma. The degree of tumor immune infiltration will lead to the destruction of the immune microenvironment and immune escape. Tan et al. have proved that the new Golgi programming caused by p53 mutation is an important driver of cancer over secretion. The mechanism is as follows: p53 deletion increases the expression of the Golgi scaffold protein, progesterone, and fat receptor 11 (PAQR11). The secretion of PAQR11-dependent protease PLAU can stimulate the autocrine of PLAU receptor/signal transduction and transcriptional 3-dependent pathway activator, which up-regulates the expression of PAQR11, thus completing a feedforward cycle that amplifies the secretion of premetastatic proteins. Blocking the secretion of PAQR11 can improve the immunosuppressive process in the tumor microenvironment [65]. Kurie et al. [66] and Tan et al. found that tp53 deletion alleviates G55 (stacking protein 55 kDa) and myosin IIA activated G55-dependent secretion from miR-34a-dependent silencing. The G55-dependent secretory protein enhances the proliferation and invasion of tp53-deficient LUAD cells and the angiogenesis and CD8 + T cell depletion in the tumor microenvironment. Wang et al. [67] found that inhibiting the Y211 phosphorylation of pcna causes the failure of the replication fork, which drives the biosynthesis of cellular solute ssDNA (single-stranded DNA) into cellular solute. It is suggested that the progress of pcna Y211 phosphorylation in nuclear DNA replication is related to the DNA sensing cascade initiated by cGAS (cyclic GMP-AMP synthase) in the cytoplasm, thus regulating immune surveillance to inhibit tumor metastasis. Understanding the tumor microenvironment may contribute to a better understanding of tumor immune cell interactions to predict response to immunotherapy [68]. In this work, we found that p16, p53, and pcna can be used as surrogate immune markers for sarcoma.

This work had the following limitations: First of all, the data were analyzed from an online database, and there was a lack of adequate in vivo and in vitro testing. Second, the sample size was small. Therefore, a follow-up study with larger sample size is required.

Conclusion

In short, our research suggests that p16, p53, and pcna are overexpressed in human sarcomas. The expression of pcna was correlated with OS, the expression of p16, p53, and pcna was correlated with RFS, and genetic mutations in p16 were negatively correlated with OS and DFS. The p16, p53, and pcna genes were positively/negatively correlated with immune cell infiltration in sarcoma. These results suggest that the p16, p53 and pcna genes significantly affect the prognosis of human sarcomas and are of vital value in immunotherapy. Our work may provide a reference for the choice of new prognostic biomarkers and tumor immunity, and the goal of treatment of sarcomas, providing directions for further research.

Materials and methods

Oncomine analysis

Oncomine database (https://www.oncomine.org/resource/login.html) is usually used to analyze the DNA or RNA sequence of cancer, and genome-wide expression data from malignant tumors [69]. In this work, Oncomine transcription was used to explore the differential expression of p16, p53, and pcna in various cancer tissues.

Gene expression patterns of interaction analysis (GEPIA) assessment of datasets

GEPIA datasets (http://gepia.cancer-pku.cn/) are often used to analyze data generated by the cancer genome atlas project [70]. In this work, we use the GEPIA to explore the differential expression of p16, p53, and pcna genes.

Reagents and cell lines

Human osteosarcoma cell lines MG63 and 143B were purchased from the Shanghai Institute of Cell Biology, Chinese Academy of Sciences. Human primary osteoblasts (Cat.No.GN-H109) were obtained from the Gaining Biological Company (Shanghai, China). The cells were cultured in DMEM medium supplemented with 10% FBS (fetal bovine serum), streptomycin, and penicillin at 37 °C and 5% CO2. Anti-p16 antibody (item no. AF5484), anti-p53 antibody (item no. AF0865), anti-PCNA antibody (item no. AF0239), and anti-β-actin antibody (item no. AF7018) were purchased from Affinity Biosciences (Jiangsu, China).

Protein extraction and Western Blotting test

Total proteins were isolated from cells using the RIPA buffer (Solarbio). The Bradford assay (Bio-Rad Laboratories) was used to quantify protein. The protein was separated on 10% sodium dodecyl sulfate–polyacrylamide gel (SDS-PAGE) and then transferred to a polyvinylidene fluoride (PVDF) membrane (Millipore, USA). The resulting PVDF film was immersed in 5% skim milk and sealed for about 1 h. Next, the membrane was incubated overnight with anti-p16 (1VO1000, Affinity, China), anti-p53 (1VO1000, Affinity, China), and anti-PCNA (1REO1000, Affinity, China) antibodies at 4 °C and incubated with the corresponding secondary antibody (rabbit) at room temperature for 2 h. Finally, autoradiography was performed with the ECL kit (Thermo Fisher Scientific).

Survival analysis

We use the Kaplan–Meier plotter database (https://kmplot.com/analysis/index.php? P = service&cancer = pancancer_rna seq) to investigate the effects of p16, p53, and pcna expression on the survival in sarcoma patients [71, 72]. This was compared with the survival curve obtained by the GEPIA dataset analysis.

CBioPortal data analysis

In this work, cBioPortal (www.cbioportal.org) was used to analyze the gene mutations of p16, p53, and pcna and study their effect on OS and disease-free survival (DFS) in sarcoma patients [73].

Enrichment analysis of co-expressed genes

David database (http://david.ncifcrf.gov) provides a comprehensive set of functional annotation tool for researchers to understand the biological significance behind a string of genes [74]. It was used in this study to perform ontology (go) analysis of enrichment of p16, p53, and pcna, and the genes. Metascape (https://metascape.org) has been used to provide a comprehensive analysis of the gene annotation and resources, integrating a variety of functions, such as enrichment, interactive group analysis, gene function annotation and members of the search, and providing outputs as unique visual graphics [75]. In this work, we used Metascape to build a network graph of related genes.

Tumor immune to assess resource (TIMER) analysis

Timer web server (https://cistrome.shinyapps.io/timer/) is a comprehensive resource system that analyzes immune cell infiltration across different types of cancer [76, 77]. In this work, the TIMER was used to study the relationship between the expression of p16, p53, and pcna and immune cells penetrating the sarcoma.