OSBPL Family Genes serves as a potential biomarker for poor prognosis in kidney renal clear cell carcinoma

doi:10.21203/rs.3.rs-1543271/v1

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OSBPL Family Genes serves as a potential biomarker for poor prognosis in kidney renal clear cell carcinoma

https://doi.org/10.21203/rs.3.rs-1543271/v1

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Background: Renal cell carcinoma is one of the most malignant tumors originated from urinary tubular epithelia system and renal parenchyma, among which we can see kidney renal clear cell carcinoma (KIRC) most frequently in clinic. Furthermore, members of oxysterol-binding protein-like (OSBPL) gene family play an important coordinating role in intercellular communication and lipid flow. Recently, it has been reported that OSBPL is highly expressed in some cancers and promotes the occurrence, metastasis and invasion of cancer cells.

Methods: We used Oncolnc, cBioportal and other databases to analyze the data related to OSBPL family genes in patient with KIRC, such as the prognostic values of OSBPL gene family members for KIRC, and the possible mechanism of regulatory of OSBPL in the development and occurrence of KIRC.

Results: In patients with KIRC, we found that the expressions of OSBPL5 and OSBPL7 are highly upregulated. TIMER 2.0 analyses showed that OSBPL expression is connected with the infiltration of multiple immune cells.

Conclusion: Our study show that OSBPL gene family members especially OSBPL5 and OSBPL7 may have an important influence in KIRC and serve as a potential biomarker for poor prognosis and their expression is related to immune cell infiltration.

biomarkers

OSBPL5

OSBPL7

kidney renal clear cell carcinoma

As we all know, renal cell carcinoma (RCC) is one of the most common malignant tumors. In United State, 74000 patients were newly diagnosed and 1500 died in 2020. Kidney renal clear cell carcinoma (KIRC) can be seen most frequently in clinic, accounting for about 80% − 90% of patients with RCC. It is the cause of most RCC related deaths ^[1]–[3]. KIRC is resistant to radiotherapy and chemotherapy in most cases. Surgical resection is recommended for patients with localized renal clear cell renal carcinoma. Many patients have a good prognosis. However, there are about 20% − 40% of patients with KIRC will experience death and tumor progression after radical surgery ^[4]–[6]. Thus, it is crucial to find robust and novel biomarkers for clinical prognosis in patients with KIRC.

Oxysterol-binding protein homologues, one of the largest families of intracellular lipid binding / transfer proteins (LTP), also called ORP or OSBPL proteins, in which OSBPL protein family is composed of 9 members. Many ORPs are associated with organelle lipid transfer at membrane contact sites, which occurs in several cases in a ‘counter-current’ fashion ^[7],[8]. In addition, some studies demonstrate that OSBPL also exchanges these lipids at ER-lysosome (Lys) contacts and increase Lys cholesterol content, besides conducting as a exchanger of cholesterol/phosphatidylinositol 4-phosphate (PI4P) at membrane contact sites (MCS) of the endoplasmic reticulum (ER)-trans-Golgi network (TGN) ^[9],[10]. Moreover, they play an important role in the function of various cells, especially many human malignant tumor cells. For example, OSBPL3 is often overexpressed in colon tumors and osteosarcoma ^[9],[11]. High OSBPL3 and OSBPL10 often predict poor prognosis of pancreatic ductal adenocarcinoma ^[10]. OSBPL2, OSBPL7 and OSBPL8 are also up-regulated in cholangiocarcinoma ^[12]. In addition, some studies also showed that OSBPL7 and OSBPL10 emerged as novel lipid transfer proteins required for macroautophagy of selective cargo ^[13]. At present, the relationship between OSBPL family and KIRC has not been clarified, and a comprehensive analysis is needed

In recent years, more and more platforms and databases enable cancer researchers to obtain a variety of data, so that they can analyze a variety of cancers bioinformatics. In order to better understand the role of OSBPL family in KIRC, we used data obtained from TIMER 2.0, GEPIA 2.0, String and other databases to analyze the expression, clinicopathological characteristics and prognosis of OSBPL family in KIRC, and provided new insights into its role in KIRC immunity.

2.1. TIMER 2.0

TIMER 2.0 (http://timer.cistrome.org/, accessed on 20 January 2021) provides a more robust estimation of immune infiltration level for tumor profile provided by user or TCGA. We inputted OSBPL family genes in the “Gene_DE” module of TIMER 2.0 web and obtained a figure about the expression difference of OSBPL family genes between specific tumor subtypes or different tumors and adjacent normal tissues of the TCGA project. In addition, based on the expression of OSBPL gene family, we also used TIMER 2.0 database to determine the immune infiltration of tumors.

2.2. GEPIA 2

The Gene Expression Profiling Interactive Analysis 2 database (GEPIA 2, http://gepia2.cancer-pku.cn/, accessed on 20 January 2021) is a web-based method, TCGA derived data and genotype tissue expression, to study genes interaction and expression in normal tissues and cancers, and further provide customized functions. In this study, GEPIA 2 was used to search the expression difference of OSBPL family genes between different tumors or specific tumor and adjacent normal tissues.

2.3. UaLcan

Base on the clinical patient data and TCGA transcriptome, UaLcan(http://ualcan.path.uab.edu/index.html, accessed on 20 January 2022) allows users to obtain gene expression levels. In addition, UaLcan can also link the gene expression with the survival rate of patients, and further stratify patients based on other parameters to help users identify meaningful biomarkers. The UaLcan database was used to analyze the differential expression of OSBPL family genes in KIRC and normal tissues, and drew some conclusions in our study.

2.4. OncoLnc

Survival analysis curve and survival estimate are the most commonly used methods to display survival data in medicine ^[14]–[16]. OncoLnc (http://www.oncolnc.org/, accessed on 20 January 2022) can link the data in TCGA database with mRNA, miRNA or lncrna expression level for survival analysis. It uses 21 kinds of cancer data in TCGA, which is a new available resource of Cox coefficient ^[17]. OncoLnc was used to analyze the survival difference between high and low expression of OSBPL family genes in KIRC, and it was considered that hazard ratios (HRs) > 1, and p < 0.05 was significant.

2.5. cBioPortal

cBioPortal (http://www.cbioportal.org, accessed on 20 January 2022) database is a public database about tumor genomics and transcriptomics. This feature of cBioPortal platform enables users to conduct statistical analysis, graphical review and large-scale data processing of tumor changes from gene to protein level ^[18]–[20]. We used cBioPortal platform to analyze the gene changes of OSBPL members in KIRC.

2.6. GeneMANIA

GeneMANIA (http://www.genemania.org, accessed on January 2022) database is a very flexible database. It can be used to put forward assumptions about gene function and determine the gene priority of functional analysis, so as to further generate the list of analyzed genes ^[21]. According to a large number of genomic and proteomic data, GeneMANIA can find or predict proteins that have similar functions ^[22]. The GeneMANIA platform was used to search the relative genes that may have interaction with KIRC and understood the interaction between OSBPL family genes.

2.7. String

String (https://string-db.org/, accessed on 20 January 2022) database can be used to explore the interaction between various biological proteins and collect and integrate data ^[23]. In our study, we explored members of OSBPL gene family and their connected protein-protein interaction (PPI) network proteins.

2.8. Human Protein Atlas

The Human Protein Atlas (HPA, www.proteinatlas.org, accessed on 20 January 2022) platform contains 11250 antibody labeled tissue microarray images of human proteins. These microarrays contain 46 human normal tissues and sections of more than 20 human cancers ^[24]–[25]. We used HPA platform to further evaluate the expression of OSBPL family proteins.

2.9. Methsurv

Methsurv (https://biit.cs.ut.ee/methsurv/, accessed on 20 January 2022) database can be used to study the methylation sites of genes in TCGA database. We used the methsurv database to study the methylation sites of some osbpl family members and their prognostic value.

2.10. LinkedOmics

LinkedOmics (http://www.linkedomics.org/login.php, accessed on 20 January 2022) which involves multi-omics data from all 10 Clinical Proteomics Tumor Analysis Consortium (CPTAC) cancer cohorts and 32 TCGA Cancer types is publicly available portal ^[26]. We applied LinkedOmics platform to analyze the function of OSBPL5 and OSBPL7 genes, and obtained relevant visual charts.

2.11. Static Analysis

We used Spearman correlation test to determine the correlation between genes, and Wilcoxon rank sum test and ANOVA test were used to analyze the differences between groups, and. Kaplan-Meier diagram was established by OncoLnc database and log-rank test was performed. Lasso regression analysis was used to research the clinical prognosis of OSBPL gene family in KIRC. The standard of statically significant of the data is P < 0.05.

3.1. Higher OSBPL expression levels in cancerous than in noncancerous tissues

We obtained the OSBPL family genes expression in normal tissues and other cancers such as ACC, HCC, colorectal cancer, LUAD and OV from TIMER 2.0 and GEPIA 2 database. We can find that OSBPL is highly expressed in a variety of tumor tissues, especially OSBPL3 and OSBPL7. In addition, we also found that almost all gene members of OSBPL family are highly expressed in STAD, indicating that there may be some relationship between OSBPL and STAD ^[27]. From TIMER 2.0 database, we can know that KIRC has higher expression of OSBPL5 and OSBPL7 than that in normal tissues (Fig. 1A), while GEPIA 2 database shows that OSBPL5, OSBPL7 and OSBPL11 are highly expressed in KIRC (Fig. 1B).

3.2. Expression differences of the OSBPL family between KIRC and normal tissues

Then, the expression differences of OSBPL family gene members in KIRC and normal tissues were compared by GEPIA 2.0 platform using TCGA and GTEx databases and UaLcan platform (Fig. 2). In 522 KIRC patients on GEPIA 2.0 platform, we can see that the relationship between OSBPL family members and KIRC is not very significant (Fig. 2A). However, in 533 patients on the UaLcan platform, we can see the OSBPL5 and OSBPL7 expression in KIRC was significantly higher than the expression in normal tissues, while OSBPL2, OSBPL3, OSBPL6, OSBPL8 and OSBPL9 were significantly higher than that in KIRC. OSBPL10 and OSBPL11 had no statistical difference between KIRC and normal groups (Fig. 2B). Next, CCLE was used to search the expression of OSBPL family genes in different cell lines in renal cell carcinoma. The results are following (Fig. 3).

3.3. Prognostic potential of the OSBPL family in KIRC

We first analyzed the overall survival (OS) of KIRC by using univariate/multivariate Cox regression (Table 1). Univariate correlation analysis showed age > 60 years (p < 0.001, HR = 1.765), histological grade of G3 & G4 (p < 0.001, HR = 2.702), pathologic stage of Ⅲ & Ⅳ (p < 0.001, HR = 3.946), T stage of T3&T4 (p < 0.001, HR = 3.228), N stage of N1 (p < 0.001, HR = 3.453) and M stage of M1 (p < 0.001, HR = 4.389) are significantly correlated with OS. Multivariate analysis showed age > 60 (p = 0.017, HR = 1.685), histologic grade of G3 & G4 (p = 0.037, HR = 1.696,) and M stage of M1 (p < 0.001, HR = 2.712) are significantly correlated with OS. In order to more accurately understand the clinical prognosis of OSBPL gene family, especially OSBPL5 and OSBPL7 in KIRC, we refined the members of OSBPL gene family by calculating the regression coefficient using Lasso regression algorithm (Fig. 4B, C). Next, we studied whether the prognosis of KIRC was related to the OSBPL family genes expression level through OncoLnc, and evaluated the effect of its transcription level on the survival rate of KIRC (Fig. 4A). Studies indicate that patients with lower expression levels of OSBPL3 (p = 2.72e-05), OSBPL5 (p = 0.0209), OSBPL6 (p = 0.00496) and OSBPL7 (p = 6.94e-05) are significantly connected with longer overall survival (OS). In contrast, patients with higher levels of OSBPL8 (p = 0.00179) and OSBPL11 (p = 0.017) transcription were significantly associated with longer OS. OSBPL2, OSBPL9 and OSBPL10 had no significant difference in KIRC OS. These results indicated that some members of OSBPL family could act as biomarkers for the poor prognosis and progression of KIRC in patients.

Table 1

The relationship between overall survival and multivariate characteristics of kidney renal clear cell renal carcinoma (KIRC) patients
Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Age	539
<=60	269	Reference
>60	270	1.765 (1.298–2.398)	< 0.001***	1.685 (1.098–2.586)	0.017*
Gender	539
Male	353	Reference
Female	186	1.075 (0.788–1.465)	0.648
Histologic grade	531
G1&G2	249	Reference
G3&G4	282	2.702 (1.918–3.807)	< 0.001***	1.696 (1.033–2.784)	0.037*
Pathologic stage	536
Stage I&Stage II	331	Reference
Stage III&Stage IV	205	3.946 (2.872–5.423)	< 0.001***	1.244 (0.489–3.160)	0.647
T stage	539
T1&T2	349	Reference
T3&T4	190	3.228 (2.382–4.374)	< 0.001***	1.590 (0.701–3.607)	0.267
N stage	257
N0	241	Reference
N1	16	3.453 (1.832–6.508)	< 0.001***	1.789 (0.896–3.573)	0.099
M stage	506
M0	428	Reference
M1	78	4.389 (3.212–5.999)	< 0.001***	2.712 (1.601–4.593)	< 0.001***
CI: confidence interval
P < 0.05, P < 0.01, **P < 0.001

3.4. OSBPL expression correlated with poor clinicopathologic features

In addition, by the use of the Human Protein Atlas (HPA), we searched potential functions of OSBPL gene family members in clinical KIRC specimens; Data from the platform showed that in KIRC specimens, OSBPL2, OSBPL3 and OSBPL9 were expressed at a medium level, while OSBPL5 was expressed strongly. Representative images are presented (Fig. 5A). Univariate analysis indicated a correlation between OSBPL5 and OSBPL7 expression and prognostic characteristics and poor clinicopathologic by using logistic regression. The high expression of OSBPL5 in KIRC has significant associations with Pathologic stage (Stage II&Stage III&Stage IV vs. Stage I, p = 0.047) and T stage (T2&T3&T4 vs. T1, p = 0.028) (Table 2).The high expression of OSBPL7 in KIRC is significantly associated with Histologic grade (G2&G3&G4 vs. G1, p = 0.018) (Table 3).These result show that KIRC patients which has higher expression levels of OSBPL5 and OSBPL7 are easier to having tumors which are more malignant in grade, stage and status compared to those with lower expression levels of OSBPL5 and OSBPL7. Then, according to the regression coefficient and expression level of the selected gene, we calculated the risk score of each patient with KIRC. We defined the median as the threshold and divided patients with KIRC into low-risk group and high-risk group. The data demonstrate that a higher risk score may be correlated positively with poor prognosis and the expression of some genes such as OSBPL7 (Fig. 5B). Next, we researched the OSBPL5 and OSBPL7 ROC curves performance in terms of 1-, 3-, and 5-year prognoses (Fig. 5C). It can be seen that the areas under the time-dependent ROC curves (AUCs) of OSBPL5 are 0.572, 0.521 and 0.501 respectively, while the AUC of OSBPL7 is 0.615, 0.573 and 0.612 respectively.

Table 2

Association between OSBPL5 expression and clinicalpathologic characteritics
Characteristics	Total(N)	Odds Ratio(OR)	P value
Age (> 60 vs. <=60)	539	0.760 (0.541–1.065)	0.111
Histologic grade (G2&G3&G4 vs. G1)	531	0.000 (NA-46045888712311264952451072.000)	0.987
Pathologic stage (Stage II&Stage III&Stage IV vs. Stage I)	536	0.709 (0.504–0.995)	0.047*
T stage (T2&T3&T4 vs. T1)	539	0.684 (0.487–0.960)	0.028*
N stage (N1 vs. N0)	257	1.191 (0.425–3.337)	0.735
M stage (M1 vs. M0)	506	0.845 (0.518–1.370)	0.496
*P < 0.05

Table 3

Association between OSBPL7 expression and clinicalpathologic characteritics
Characteristics	Total(N)	Odds Ratio(OR)	P value
Age (> 60 vs. <=60)	539	0.881 (0.628–1.236)	0.464
Histologic grade (G2&G3&G4 vs. G1)	531	0.163 (0.025–0.607)	0.018*
Pathologic stage (Stage II&Stage III&Stage IV vs. Stage I)	536	0.887 (0.632–1.245)	0.490
T stage (T2&T3&T4 vs. T1)	539	0.895 (0.638–1.254)	0.519
N stage (N1 vs. N0)	257	2.784 (0.940-10.179)	0.083
M stage (M1 vs. M0)	506	0.877 (0.538–1.422)	0.596
*P < 0.05

3.5. Analysis of the Alterations, Co-expression. Interaction and of OSBPL gene family members in KIRC

Next, to learn more about the OSBPL, we explore, visualize and analyze genomic data, we used cBioPortal to determine the type and frequency of changes in OSBPL family gene members in TCGA KIRC samples. Based on the cohort (Fig. 6A), 81 of 448 KIRC patients had changes in OSBPL members, including mutation, deletion and amplification. The outcome shows that the rate of genetic alteration of OSBPL gene family members in patients with KIRC ranged from the lowest rate of 0.2% to the highest rate of 12% for OSBPL5 and OSBPL9. The others were 3% for OSBPL2, 2.7% for OSBPL3, 1.6% for OSBPL6 and OSBPL7, 0.4% for OSBPL8 and 0.9% for OSBPL11 (Fig. 6B). The protein-protein and gene-gene interaction network, which was acquired by GeneMANIA and STRING also indicated that about 17 potential genes and 9 potential target genes interact with the OSBPL family (Fig. 6C and 6E). The GeneMANIA showed that PLEKH gene family, PHETA gene family, DOK, ANLN, RTKN, PHLDB etc., has a close relationship with OSBPL gene family. From the STRING, we obtained a PPI network of OSBPL members to explore the regulated pathways, and the data show that there are nine members of OSBPL family members and potential interacting proteins. Furthermore, we used Spearson’s correlation analysis according to their mRNA expression levels, and the correlation coefficients between OSBPL members (Fig. 6D) was calculated. Based on the correlation plot, the results indicated that except for OSBPL8 with OSBPL9, there is a large and significant positive correlation among other OSBPL gene family members.

3.6. Methylation of OSBPL family members in KIRC patients

Methylation of DNA exists in various cancers, which is a very common epigenetic mechanism. We studied the methylation levels of OSBPL3, OSBPL5, OSBPL6 and OSBPL7 and the prognostic value of each CpG through the Methsurv database (Fig. 7). The results showed that among the CpG sites of OSBPL3 gene, cg05107814, cg06173395, cg08379029, cg18023771 and cg25270424 had the highest degree of DNA methylation; Among the CpG sites of OSBPL5 gene, cg07992377, cg09821493, cg12491594, cg17214754 and cg25908390 had the highest degree of DNA methylation; Among the CpG sites of OSBPL6 gene, cg01616745, cg03915558, cg13547053, cg16136187 and cg26949612 had the highest degree of DNA methylation; Among the CpG sites of OSBPL7 gene, cg02206375, cg03816592, cg14295458, cg15103567 and cg25626992 had the highest degree of DNA methylation. Next, we analyzed the prognostic value of these CpG sites (Table 4). The results showed that in OSBPL3 and OSBPL5 genes, hypermethylation often represents a good prognosis; In OSBPL6 gene, hypermethylation represents a poor prognosis, while in OSBPL7 gene, hypermethylation of cg02206375 represents a good prognosis and hypermethylation of cg03816592 represents a poor prognosis. But overall, hypermethylation of OSBPL family often represents a poor prognosis.

Table 4

Effect of hypermethylation of some OSBPL family members on the prognosis of KIRC
CpG	Hazard ratio (95% CI)	p-value	Gene name
cg05107814 cg06173395 cg08379029 cg18023771 cg25270424	1.229 (0.801–1.886) 0.354 (0.232–0.541) 0.7 (0.476–1.029) 1.33 (0.837–2.114) 0.536 (0.322–0.891)	0.345 1.608e-06*** 0.069 0.227 0.016*	OSBPL3 OSBPL3 OSBPL3 OSBPL3 OSBPL3
cg07992377	0.446 (0.258–0.771)	0.003**	OSBPL5
cg09821493 cg12491594 cg17214754	1.168 (0.74–1.845) 1.936 (1.148–3.266) 0.695 (0.452–1.067)	0.504 0.013* 0.096	OSBPL5 OSBPL5 OSBPL5
cg25908390	0.515 (0.343–0.772)	0.001*	OSBPL5
cg01616745	1.956 (1.132–3.381)	0.016*	OSBPL6
cg03915558 cg13547053 cg16136187	1.567 (0.962–2.555) 0.799 (0.506–1.26) 1.764 (1.061–2.934)	0.071 0.333 0.028*	OSBPL6 OSBPL6 OSBPL6
cg26949612	0.835 (0.569–1.226)	0.357	OSBPL6
cg02206375	0.668 (0.454–0.982)	0.04*	OSBPL7
cg03816592 cg14295458	1.704 (1.036–2.802) 1.343 (0.832–2.166)	0.035* 0.227	OSBPL7 OSBPL7
cg15103567 cg25626992	0.632 (0.384–1.039) 0.727 (0.492–1.074)	0.07 0.109	OSBPL7 OSBPL7
CI: confidence interval
P < 0.05, P < 0.01, **P < 0.001

3.7. Association between tumor TME immune cell infiltration and OSBPL expression

The association between immune cell (B cells, DCs, CD4 + T cells, macrophages CD8 + T cells, and neutrophils) infiltration and OSBPL expression was investigated by using TIMER 2.0 (Fig. 8A). The figure showed that the level of OSBPL2 has a strongly positive correlation with infiltrating levels of neutrophils, macrophages and CD4 + T cells and has a negative correlation with infiltrating levels of CD8 + T cells (p < 0.05). The levels of OSBPL3, OSBPL5 and OSBPL6 have a positive correlation with infiltrating levels of neutrophils, macrophages and CD4 + T cells but negatively correlated with B cells, and OSBPL5 has a positive correlation with CD8 + T cells and OSBPL3 with DCs (p < 0.05). OSBPL7, OSBPL8 and OSBPL9 were positively associated with the infiltration of CD4 + T cells and neutrophils, but OSBPL8 was negatively associated with B cells and positively with DCs, and OSBPL8 and OSBPL9 were highly positively associated with macrophages (p < 0.05). In addition, we used EPIC's quantitative algorithm to research the relationship between OSBPL expression and immune cell list. It can be seen that OSBPL family members have the strongest positive correlation with CD4 + T cells, while they have a strong negative correlation with NK cells and uncharacterized cells. Among these genes, we found a strong interaction between OSBPL5 and immune infiltration, suggesting that OSBPL5 plays an important role in immune function and TME (Fig. 8B). In addition, PD-L1 is an important immune checkpoint responsible for tumor immune escape, which can down regulate effector T cells in the process of immune response, resulting in immunosuppression. Considering the role of OSBPL family in KIRC carcinogenesis, we evaluated the relationship between OSBPL family members and PD-L1 in TCGA database (Fig. 8C). It can be seen that except OSBPL5, other OSBPL family members showed significant positive correlation with the expression of PD-L1, which also shows that the high expression of OSBPL family members can cause KIRC immune evasion. Moreover, we further explored the effect of methylation of some OSBPL family members on the expression of PD-L1 (Fig. 8D). The results showed that the higher the methylation degree of OSBPL3, OSBPL5 and OSBPL6, the higher the expression level of PD-L1, indicating that the hypermethylation of these genes often represents immunosuppression, thus promoting the progress of KIRC.

3.8. Functions of the OSBPL5 and OSBPL7 in KIRC

We first analyzed the metabolic role of OSBPL5 and OSBPL7 in cancer, the metabolism of lipids and lipoproteins (Fig. 10A, B) and the GSEA enrichment of some members in OSBPL gene family (Fig. 9E). After that, for investigating the functions of OSBPL5 and OSBPL7 in KIRC, we used LinkedOmics platform for function analysis. Among them, in the functional analysis of OSBPL5, angiogenesis and integrity signaling pathway have aroused our interest (Fig. 9A). In the functional analysis of OSBPL7, angiogenesis and VEGF signaling pathway have aroused our interest (Fig. 9C). In the angiogenesis of OSBPL5, we can see that Dok-R, Grb2, Grb7, Grb14, Shp-2 and PI3K-FAK/Akt pathway play a major regulatory role (Fig. 9B). In the VEGF signaling pathway of OSBPL7, we can see that PI3K-PIP2-Rac / Akt / PKB pathway, p38MAPK, FAK, SCK, VPAP and PLC- γ play a major regulatory role. In addition, we can also see that RAS and HIF-1 also play a role (Fig. 9D).

Renal cell carcinoma (RCC) is the one of the most common malignant tumor, and KIRC is the most common pathological subtype which account for about 75% of RCC. There are abundant blood vessels around KIRC, which enables KIRC to grow rapidly and transfer through blood vessels. Previous reports have demonstrated that more than 15% of patients with RCC have distant metastasis at the time of diagnosis ^[28]. The effect of surgical treatment of localized KIRC is good, but the prognosis of patients with metastasis or recurrence is serious. Therefore, we can learn from it that one of the ways to improve the treatment and diagnosis of advanced metastatic KIRC is to screen biomarkers of KIRC progress, such as OSBPL. Previous studies have indicated that KIRC is connected with a variety of genes, such as SAA1, TBC1D3 and TICRR etc ^[29]–[31].

The present study is the first to research the connection between OSBPL gene family and KIRC, including their expression, methylation and prognostic values. Our study found that in addition to OSBPL8, the high expression of most OSBPL family members represents the poor prognosis of KIRC patients, and the expression of individual OSBPL family members (such as osbpl5 and osbpl7) in KIRC patients is higher than that in normal people. Through the use of HPA, we found that their increased expression level will make patients more prone to tumors with higher malignancy, stage and status. Although gene mutations have very close relation with tumor occurrence and usually represent poor prognosis, we can see that the mutation of most OSBPL family members does not account for a high proportion in KIRC through the analysis of the family, and there is no data to show that their changes will lead to poor prognosis. So, in order to deeply understand the reasons for these phenomena, we made further research on the methylation, immune and function of OSBPL family members. The research showed that hypermethylation of OSBPL family members at some sites suggested high expression, which indicated the high expression of PD-L1 gene. The result was the decrease of CD8 + T cells and CD4 + T cells and the increase of Treg cells, resulting in immunosuppression. In addition, the study also shows that the high expression of some OSBPL family members often causes tumor angiogenesis through a variety of pathways, suggesting the deterioration of patients' condition.

In our study, we found an interesting phenomenon: the UaLcan database indicated that the expression of OSBPL3 and OSBPL6 genes in KIRC patients was lower than the expression in normal controls, but the OncoLnc database suggested that the high expression of OSBPL3 and OSBPL6 had connection with the poor prognosis of KIRC patients. By consulting the literature related to OSBPL3 and OSBPL6 gene, we did not find the exact explanation, but through the in-depth study of OSBPL3, we found many phenomena related to OSBPL3, which may explain the phenomenon of high expression but poor prognosis. In the literature on the relationship between these two genes and other cancers, we can see the role of OSBPL3 and OSBPL6: The top three tumors which have the strongest association between immune cell infiltration and OSBPL3 expression were BRCA, COAD and KIRC, respectively ^[32]. In gastric cancer, OSBPL3 may regulate cell cycle progression in gastric cancer cells through R-Ras / Akt signaling pathway. Knockout of OSBPL3 in GC cells can reduce the level of R-Ras in GC cells, indicating that its R-Ras / Akt signaling pathway is down regulated ^[33]. The OSBPL6 gene shows a binding site with miR-33a/b, a microRNA that shows a role in the regulation of FA β-oxidized genes have a targeted effect, which lead to a significantly lower level of β-hydroxybutyrate ^[34]. In addition, the structural analysis results of OSBPL3 and OSBPL6, especially OSBPL3 gene, may also explain this interesting phenomenon to some extent: In Sumana's study, we learned that all OSBPL gene family members contain a core OSBPL-related domain (ORD), and many family members also contain pleckstrin homology (PH) domain, endoplasmic reticulum (ER) - targeted FFAT motifs, transmembrane ankyrin repeat sequence and / or GOLD domain ^[36]. Furthermore, other studies have also shown that OSBPL family uses at least four non-mutually exclusive mechanisms for lipid metabolism, vesicular transport, cell signal transduction in cells and no vesicular lipid transport. ERK also plays an important role in the process of cell signal transduction. The regulation ERK by OSBPL helps to regulate the changes of gene expression due to the change of cell cholesterol level ^[35]. In the research related to OSBPL3, we found that OSBPL3 has made a variety of changes to its own domain. For example, the S251, S273 and S426 sites in the pleckstrin homologous domain of OSBPL3 show a high phosphorylation level in multiple tumors. In addition, the frequent mutations of OSBPL3 and the changes of x676_splicing / v676g in its domain are also related to the survival of tumor cells ^[37]. This characteristic of OSBPL3 may explain why there is a low expression in KIRC but high expression of OSBPL3 represents poor prognosis.

Furthermore, by using OncoLnc and HPA analysis, we gradually learned the association between the expression of OSBPL5 and OSBPL7 and the poor prognosis and immunohistochemistry of KIRC. In our study, we found that OSBPL5 and OSBPL7 are closely related to KIRC, which may indicate that OSBPL5 and OSBPL7 can be used as potential biomarkers of KIRC. However, the exact regulatory mechanisms of OSBPL5 and OSBPL7 in KIRC remain unclear. Therefore, we studied the mechanism of OSBPL5 and OSBPL7 in other cancers, and hope to find the possible mechanism of KIRC. We found that OSBPL5 is the downstream target of miR-526b-3p in non-small cell lung cancer, which can be absorbed by LMCD1-AS1, and LMCD1-AS1 can accelerate the invasion, migration and proliferation of NSCLC cells by regulating OSBPL5 ^[39]. In addition, some studies have found that compounds which target OSBPL7 can increase ABCA1 dependent cholesterol outflow and protect renal function in two kidney disease models (chronic kidney disease and diabetic kidney disease) ^[40]. Moreover, we also studied and analyzed the structure of OSBPL5, and found that OSBPL5 is a tail anchored endoplasmic reticulum membrane protein, which plays a role as a lipid transporter in the inner membrane. Due to its ORD domain, ER-anchored OSBPL5 can be used as a phosphatidylserine transporter. Some mutated residues in the ORD domain of OEBPL5 can bind to PS and PIP, transfer PIP to the endoplasmic reticulum and PS from the endoplasmic reticulum to the plasma membrane ^[38]. This may explain why OSBPL5 is highly expressed in KIRC or even more tumors and represents a signal of poor prognosis.

In addition, the regulation of tumor immune microenvironment is a significant and attractive therapeutic target in the study of tumor progression. Previous immunotherapy studies have indicated that the tumor microenvironment of cancer can be widely divided into tumor infiltrating lymphocytes (TILs) (hot) or non-TILs (cold). It is fortunate for us that KIRC is a hot tumor ^[41]. In KIRC TME, immune infiltrating cells include B cells, mast cells, matrix cells and neutrophils. These cells can affect the balance of KIRC-related immune escape and antitumor immunity ^[42]. In our study, by using the TIMER 2.0 platform, it is not difficult to find that most OSBPL families have no significant difference or negative correlation with B cells, but have positive correlation with most other immune cells. CD4 + T cells can inhibit the growth of tumor cells by targeting the surface antigen of tumor cells, while the activated number of CD8 + T cells is often positively correlated with the good prognosis of various cancers. However, in the process of tumor progression, it will lose its immunogenicity, and the T cell response will also be inhibited by the tumor microenvironment. This inhibition involves regulatory T cells, which secrete immunosuppressive cytokines and myeloid and stromal cells. They regulate immune checkpoints by activating co-inhibitory receptors on T cells (such as PD-1, Tim-3 and CTLA-4), resulting in depletion of T cell phenotype and loss of function ^[43]. In addition, some members of OSBPL family can also control the phagocytosis of macrophages, and its overexpression in macrophages often indicates a poor prognosis ^[38],[44]. These results suggest that the high expression of some OSBPL gene family members may cause the infiltration of more immunocompetent cells in the tumor microenvironment, which seems to indicate that hot tumors have more immune checkpoints. They may play a potential role in the immune microenvironment and may benefit from the interference of immunotherapy.

Kidney renal clear cell carcinoma (KIRC), lipid binding / transfer proteins (LTP), lysosome (Lys), membrane contact sites (MCS), OSBPL-related domain (ORD), overall survival (OS), Oxysterol-binding protein-like (OSBPL), pleckstrin homology (PH), phosphatidylinositol 4-phosphate (PI4P), renal cell carcinoma (RCC), endoplasmic reticulum (ER)-trans-Golgi network (TGN), tumor infiltrating lymphocytes (TILs)

Acknowledgements

We wish to thank all the staff members of the Key Laboratory of the Science and Technology Department of Jilin Province.

Data Availability

The datasets generated and/or analyzed during the current study are available in the TIMER 2.0 [http://timer.cistrome.org/], GEPIA 2 [http://gepia2.cancer-pku.cn/], UaLcan [http://ualcan.path.uab.edu/index.html], OncoLnc [http://www.oncolnc.org/], cBioPortal [http://www.cbioportal.org], GeneMANIA [http://www.genemania.org], String [https://string-db.org/], HPA [www.proteinatlas.org], Methsurv [https://biit.cs.ut.ee/methsurv/], LinkedOmics [http://www.linkedomics.org/login.php], TISIDB [http://cis.hku.hk/TISIDB/index.php] CCLE [https://portals.broadinstitute.org/ccle] and TCGA [https://www.cancer.gov] repositories.

Funding Sources

This work was supported by National Natural Science Foundation of China (No. 31460303, No.81660609, No.81860741), Jilin Province Science and Technology Department Project (No.20180101007JC) and Jilin Provincial Education Department Project (JJKH20191116KJ).

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OSBPL Family Genes serves as a potential biomarker for poor prognosis in kidney renal clear cell carcinoma

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. TIMER 2.0

2.2. GEPIA 2

2.3. UaLcan

2.4. OncoLnc

2.5. cBioPortal

2.6. GeneMANIA

2.7. String

2.8. Human Protein Atlas

2.9. Methsurv

2.10. LinkedOmics

3. Result

3.1. Higher OSBPL expression levels in cancerous than in noncancerous tissues

3.2. Expression differences of the OSBPL family between KIRC and normal tissues

3.3. Prognostic potential of the OSBPL family in KIRC

3.4. OSBPL expression correlated with poor clinicopathologic features

3.5. Analysis of the Alterations, Co-expression. Interaction and of OSBPL gene family members in KIRC

3.6. Methylation of OSBPL family members in KIRC patients

3.7. Association between tumor TME immune cell infiltration and OSBPL expression

3.8. Functions of the OSBPL5 and OSBPL7 in KIRC

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

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