Decreased DIO3OS expression predicts poor prognosis in hepatocellular carcinoma and is associated with immune infiltration

doi:10.21203/rs.3.rs-1884978/v1

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Decreased DIO3OS expression predicts poor prognosis in hepatocellular carcinoma and is associated with immune infiltration

https://doi.org/10.21203/rs.3.rs-1884978/v1

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published 21 Feb, 2023

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Hepatocellular carcinoma has become one of the most shared cancers in the whole world because of its high morbidity, poor survival rate and low recovery rate. Especially in China, hepatocellular carcinoma has been regarded as one of the most malignant cancers. With the exploration of tumor diseases and the development of bioinformatics over the years, lncRNAs have been found to play a direct or indirect regulatory role in tumorigenesis and tumor development, which brings a new direction for the treatment of tumors. Previous studies had investigated that DIO3 opposite strand upstream RNA(DIO3OS) played a promoting role in the occurrence and development of thyroid carcinoma and uterine corpus endometrial carcinoma. However, research focusing on the role of DIO3OS in hepatocellular carcinoma is still uncleared. We found that low DIO3OS level often predicts poor prognosis in patients with hepatocellular carcinoma. In addition, there was significant correlation between different immune cells and their immune markers and DIO3OS. Our results suggest that DIO3OS can be used as an effective biomarker in the diagnosis and prognosis of hepatocellular carcinoma, affecting the development of hepatocellular carcinoma by influencing tumor immune cell infiltration.

DIO3OS

hepatocellular carcinoma

immunomarker

tumor immunity

Malignant neoplasms have become the main cause of death in 21st centurys. Despite improvements in treatment and surveillance over the past few decades, cancer mobidity and mortality continue to increase every year. Liver hepatocellular carcinoma(LIHC) is the third most shared cause of cancer-related death in the world(El-Serag, 2011). Over the last few years, global clinical data has showed that hepatocellular carcinoma(HCC) patients are the most common in LIHC patients(El-Serag and Rudolph, 2007). Currently, the main diagnostic methods for HCC included serum marker detection, CT, MRI and pathological biopsy, but these methods lack sensitivity and accuracy. Due to the lack of effective biomarker screening and early prevention, most patients are already in advanced stage at the time of diagnosis. According to the above reasons, investigating more sensitive and specific biomarkers for HCC patients is a crucial task.

Previous reports suggested that lncRNA was defined as transcripts with more than 200 nucleotides and had no obvious protein coding function(Sanchez Calle, et al., 2018). Eukaryotic transcriptome transcripts produce complex transcriptional networks, including tens of thousands of lncRNAs, which have little protein coding function. Previously, scholars took the attitude that lncRNA was only the "noise" generated during transcription, and had no practical effect on diseases(Struhl, 2007). However, more and more examines have proven that lncRNA can play a role in a variety of ways, and affect nearby or distant genes expression, which was a crucial regulatory molecule in cells. Previous studies have pointed out that the expression level or mutation of lncRNA gene is closely related to tumor proliferation, migration and invasion. LncRNA can promote chromatin remodeling, regulate protein activity, regulate epigenetics, chromatin modification and so on(Wilusz, et al., 2009). Thus, lncRNA is an crucial therapeutic target for diseases.

DIO3OS located on chromosome c14q32.31(Heilig, et al., 2003). DIO3 gene encodes type 3 deiodinase (D3) (Hernandez, 2005). Human D3 gene particiapates in maintaining the appropriate level of thyroid hormone (TH) in fetuses and adults(Hernandez, 2005). Hernandez et al. examed the expression of DIO3OS in human tissues by Northern blot using human DIO3OS cDNA as a probe (Hernandez, et al., 2004). His result showed that DIO3OS was widely expressed in human tissues. And their observation results indicated that the predicted DIO3OS polypeptide sequence had no significant homology with any entry in the protein database(Reik and Walter, 2001), which proved that DIO3OS was a noncoding gene. Previous examines indicated that DIO3OS was highly expressed in thyroid cancer, and the DIO3OS/let-7d/NF-κB2 axis regulated the activity of thyroid cancer cells(Wang, et al., 2021). DIO3OS can promote the growth and invasion of pancreatic cancer cells by competing miR-122 to regulate the expression of ALDOA(Cui, et al., 2019). However, there is little exploration on DIO3OS, and its specific role in HCC is not clear. The purpose of this article was to investigate whether lncRNA DIO3OS plays a role in the early diagnosis and prognosis of HCC as a potential biomarker.

Data acquisition and processing

In this study, we included The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/)and Univercity of California Santa Cruz(UCSC) Xena (https://xenabrowser.net/datapages/) data to determine the expression levels and prognostic significance of DIO3OS in 32 types of cancers. Besides, we also extracted normal and tumor samples from the LIHC project RNA-seq data of TCGA database to compare the changes of gene expression of lncRNA DIO3OS between tumor and normal tissues. The characteristic markers of tumor-infiltrating lymphocytes in the study were downloaded from the CELLMARKER database (http://bio-bigdata.hrbmu.edu.cn/CellMarker/) to analyze the correlation between DIO3OS and immune cell signature genes. In the above studies, the median of DIO3OS was used for separating high and low expression groups online. Convert the level3 HTSeq-FPKM(high-throughput sequencing fragments per kilobase per million) format RNA-seq data into TPM(transcrips per million reads) format, and then convert the data undergoes log2 conversion. The purpose of our work is to explain the prognostic diagnosis and impact of lncRNA DIO3OS on HCC. Therefore, patients who died of other causes are not included in the scope of this study.

Survival Analysis

The prognostic significance of lncRNA DIO3OS expression in HCC was analyzed by Kaplan-Meier plotter. The mainly prognostic indicators in our study were overall survival rate (OS), progression free disease (DSS) and progression free interval (PFI). The data was gained from the TCGA database. R package “survival” package was used for statistical analysis, and “survivor” package was used for visualization.

Univariate And Multivariate Cox Regression Analysis

We implemented proportional risk regression model to explore the effects of clinicopathological factors and DIO3OS expression on the OS of HCC patients. The relevant data came from the TCGA database(Liu, et al., 2018). P value<0.1 was taken as the index included in multivariate analysis. The “survival” package was used for statistical analysis and “ggplot2” package was used for visualization. The clinical variables in our job included: T stage, N stage, tumor status, pathological stage, historical grade, vascular invasion, residual tumor, AFP level, albumin level, and DIO3OS expression level.

Enrichment Analysis

The principle of gene set enrichment analysis(GSEA) method is to compare the target gene with the predetermined gene set to analyze the whole genome expression profile microarray data. Through this method, we can understand the expression trend of genes of interest in specific functional gene sets and whether the expression trend is statistically significant(Subramanian, et al., 2005). We applied GSEA to explore the special biogenical function of DIO3OS in the progression of HCC. The species selected for our analysis was Homo sapiens, with reference to c2.cp.v7.2.symbols.gmt curated from the MsigDB collections (https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp#C2). In order to screen out the significantly enriched pathways, we ranked the calculated results by enrenrichment score(ES), and considered that the pathways with | NES | (Normalized Enrichment Score)>1, p.val≤0.05 and false discovery rate(FDR)≤0.25 were statistically significant. The “clusterprofiler” package was used for statistical analysis, “ggplot2” package was used for visualization.

Tumor immunoassay

In this study, RNA-seq data from the TCGA database LIHC project were downloaded and processed, and the "ESTIMATE" package was used to evaluate the content level scores of immune cells and stromal cells expressed by DIO3OS in each sample. The association between DIO3OS expression level and tumor immune infiltration of HCC was compared by score estimation and data visualization.

Referring to Bindea et al., we included 24 types of immune cells(Bindea, et al., 2013). In our study, ssGSEA algorithm in “GXVA” package and spearman correlation analysis method were used to study the association between DIO3OS and immune cells. In addition, we also downloaded the markers of all immune cells in liver tissue from the CELLMARKER database, and compared the degree of correlation between DIO3OS and different immune cell markers through the spearman algorithm.

Statistical analysis

R 3.6.3 software (http://www.Rproject.org) was emploied for data preprocessing and visualization in this study. DIO3OS levels were extracted as M±SDs in each study. The analysis results of the above studies were statistically significant with P<0.05.

LncRNA DIO3OS was significantly lower in hepatocellular carcinoma

We used wilcoxon rank sum test to investigate the expression differences of DIO3OS in a variety of tumors (Fig.1a). Then, we compared the DIO3OS expression in tumor and normal samples. As the result demonstrated that DIO3OS expression was notably declined in HCC patients (P<0.001) (Fig.1b). Futhermore, we investigated the DIO3OS expression in 50 pairs of tumor tissues and normal adjacent liver tissues by paired sample t-test (Fig.1c). The results showed that DIO3OS levels were significantly lower in various types of tumors than in normal tissue. Not only that, the differental analysis of the expression of this lncRNA between tumor samples and normal samples of HCC also proved that a significant decrease in the level of DIO3OS in HCC (P < 0.001).

Fig. 1. LncRNA DIO3OS expression status in vatious of cancers. a. The expression level of DIO3OS in various cancer tissues. b. Comparison of DIO3OS in normal tissues(n=50) and cancer tissues(n=374) of hepatocellular carcinoma. c. Differential expression of DIO3OS in paired hepatocellular carcinoma cancer tissues(n=50) and normal tissues(n=50).(^*P<0.005, ^***P<0.001)

Low DIO3OS level predicts poor prognosis in HCC patients

Kaplan-Meier curves can describe patients’ survival. As shown in Fig 2a-c, after analyzing the relationship between DIO3OS expression and survival probability and survival time in patients’ OS (HR=0.63, p=0.024), DFS (HR=0.56, p=0.015) and PFI (HR=0.70, p=0.016), it can be concluded that a high level of DIO3OS is a favorable indicator for patients’ higher survival rate.

The results of the Cox regression analysis are shown in Fig 2d and 2e, the size and extent of tumorinvasion (HR=2.598, P<0.001), tumor status (HR=0.432, P<0.001), pathological stage (HR=2.504, P<0.001) and DIO3OS expression (HR=0.777, P=0.017) were significantly correlated with poor OS in HCC patients. Among them, high level of DIO3OS is a protective factor for HCC patients. And multivariate analysis revealed that tumor status (HR=0.561, P=0.005) was an independent predictor for negtive prognosis (Figure. 2E). This findings suggest that low-level DIO3OS is closely related to the process and variation of HCC.

Fig. 2. Low expression of lncRNA DIO3OS is associated with poor OS, DSS and PFI in patients with hepatocellular carcinoma. a. Overall survival (n=373), b. disease specific survival (n=365), c. progress free interval (n=373). d. Multivariate Cox regression analysis is used to determine the effect of selected variables on OS. e.Univariate Cox regression analysis is used to determine the effect of selected variables on OS.

GSEA identifies biological functions related to DIO3OS

The Fig.3 and Tab.1 showed that DIO3OS was closely related to multiple immune-related signaling pathways. Including the following seven signaling pathways, IL8 signaling pathway, immunoreulatory interactions between a lymphoid and a nonlymphoid cell, TGFβ signaling pathway, interleukin 4 and interleukin 13 signaling, WNT signaling pathway, JAK/STAT signaling pathway, leukocyte transendothelial migration. In addition, neutrophil degranulation, PI3K/AKT signaling pathway, EMC receptor interaction, notch pathway also showed significant enrichment in the DIO3OS high expression group.

Fig. 3. Enrichment plots from gene set enrichment analysis (GSEA). a. neutrophil degranulation, immunoregulatory interactions between a lymphoid and a non lymphoid cell, interleukin 4 and interleukin 13 signaling. b. PI3K/AKT signaling pathway, IL 18signaling pathway, NOTCH pathway. c. ECM receptor interaction, TGFβ signaling pathway, WNT signaling pathway, JAK/STST signaling pathway, leukocyte transendothelial migration.

Tab.1 Gene sets enriched in high phenotype

Description	NES	p.val	FDR q values
REACTOME_NEUTROPHIL_DEGRANULATION	1.469	0.001	0.019
REACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A _LYMPHOID_AND_A_NON_LYMPHOID_CELL	1.715	0.001	0.018
REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING	1.674	0.001	0.018
WP_PI3KAKT_SIGNALING_PATHWAY	1.832	0.001	0.018
WP_IL18_SIGNALING_PATHWAY	1.503	0.001	0.018
PID_NOTCH_PATHWAY	1.611	0.001	0.018
KEGG_ECM_RECEPTOR_INTERACTION	1.979	0.001	0.018
KEGG_TGF_BETA_SIGNALING_PATHWAY	1.838	0.001	0.018
KEGG_WNT_SIGNALING_PATHWAY	1.744	0.001	0.018
KEGG_JAK_STAT_SIGNALING_PATHWAY	1.618	0.001	0.018
KEGG_LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION	1.472	0.003	0.035

NES: normalized enrichment score after correction. p value: p values of statistical tests. FDR q-value: the corrected q-value obtained by the p-value correction method. The threshold of significant enrichment: false discovery rate (FDR) <0.25 and p. value<0.05.

Association with DIO3OS expression and immune infiltration in hepatocellular carcinoma

From the “ESTIMATE” analysis, we can draw the following conclusions. As shown in Fig 4a, both immune scores and matrix scores were significantly higher in the high DIO3OS expression group than in the low DIO3OS expression group. The immune score represents the content of immune cells in the tumor, and the stromal score represents the content of stromal cells. This suggests that the number of immune cells in the tumor is relatively reduced when DIO3OS levels are reduced.

According to the document of Bindea et al.(Bindea, et al., 2013), we selected 24 kinds of immune cells to investigate the correlation between DIO3OS expression and immune cells. Figure 4b and table 2 illustrate that in HCC, DIO3OS is associated with NK cells, mast cells, two types of DC cells (iDC, pDC), five types of T cells (Tem, TFH, CD8 T cells, Th1 cells, Tgd), eosinophils, macrophages, B cells, NK CD56 bright cells were significantly positively correlated, and Treg cells were significantly negatively correlated. This result is consistent with that of the ESTIMATE analysis.

As Tab.3 and supplement figures showed that the DIO3OS expression had positive correlation with CD40LG (r=0.212, p=3.6e-05), LTK (r=0.425, p=76e-18), OSBPL5 (r=0.406, p=2.5e-16), CD19 (r=0.128, p=0.013), CD79A (r=0.194, p=1.64e-04), PTPRC (r=0.142, p=0.006), CCR4 (r=0.286, p=2.94e-04), B3GAT1 (r=0.366, p=2.75e-03), CXCR3 (r=0.113, p=0.029), IL7R (r=0.286, p=1.87e-08), ITGAE (r=0.254, p=1.6e-06), CD123 (r=0.228, p=8.78e-06), CD83 (r=0.189, p=2.39e-04), HLA-DR (r=0.136, p=0.009), HLA-DMB (r=0.132, p=0.011), HLA-DPB1 (r=0.174, p=7.21e-04), HLA-DQA1 (r=0.145, p=0.005), HLA-DQB1 (r=0.140, p=0.007), HLA-DRB1 (r=0.136, p=0.009), HLA-DRB5 (r=0.114, p=0.028). In addition, a significant negative correlation with FOXP3 (r=-0.127, p=0.014), CD40 (r=-0.191, p=2.07e-04), DPP4 (r=-0.17, p=9.89e-04) was observed.

Fig. 4. Correlation analysisi between the degree of immune cell infiltration and the expression of lncRNA DIO3OS in hepatocellular carcinoma tissues. a. Density of immune cells in tumor samples. b. Correlation analysis between various immune cells and lncRNA DIO3OS.(^*P<0.005, ^**P<0.01, ^***P<0.001)

Table.2 Correlation analysis between DIO3OS and related immune cells in HCC.

Description	Correlation	P value
NK cells	0.310	<0.001
Mast cells	0.286	<0.001
iDC	0.266	<0.001
Tem	0.259	<0.001
Eosinophil	0.227	<0.001
TFH	0.218	<0.001
Macrophages	0.215	<0.001
B cells	0.205	<0.001
CD8 T cells	0.189	<0.001
NK CD56bright cells	0.180	<0.001
Treg	-0.144	0.005
T cells	0.141	0.006
Th1 cells	0.141	0.006
pDC	0.133	0.010
Tgd	0.131	0.011
DC	0.119	0.022
Cytotoxic cells	0.096	0.063
Neutrophils	0.085	0.102
NK CD56dim cells	0.053	0.311
T helper cells	0.039	0.456
Tcm	-0.037	0.474
Th17 cells	-0.032	0.542
Th2 cells	0.018	0.729
aDC	0.002	0.964

Statistically significant value : P < 0.05.

Tab.3 Correlation analysis between DIO3OS and related biomarker genes of immune cells.

Description	Gene Markers	correlation	P value
T Cells	CD4	0.160	0.002
	CD40LG/CD40L LTK	0.212 0.425	3.6e-05 7.6e-18
	OSBPL5	0.406	2.5e-16
B Cells	CD19	0.128	0.013
	CD79A	0.194	1.64e-04
	PTPRC	0.142	0.006
	CD40	-0.191	2.07e-04
Treg Cells	FOXP3	-0.127	0.014
	NINJ2	-0.219	1.84e-05
	CD25 CDKN2A	0.076 -0.282	0.14 3.03e-08
	CCR4 PRDX1	0.286 -0.274	2.94e-04 6.97e-08
Natural killer cells	B3GAT1	0.366	2.75e-03
	CXCR3	0.113	0.029
	IL7R	0.286	1.87e-08
	ITGAE	0.254	1.60e-06
	DPP4	-0.17	9.89e-04
	NCR3/NKP30	0.148	0.004
Dendritic cells	CD123	0.228	8.78e-06
	CD83	0.189	2.39e-04
	HLA-DR	0.136	0.009
	HLA-DMB	0.132	0.011
	HLA-DPB1	0.174	7.21e-04
	HLA-DQA1	0.145	0.005
	HLA-DQB1	0.140	0.007
	HLA-DRB1 HLA-DRB5	0.136 0.114	0.009 0.028

Statistical studies in the past few decades have proved that LIHC is the third highest cause of cancer-related death around the world(Global Burden of Disease Cancer, et al., 2019), in which about 90% of cases were diagnosed as HCC(Forner, et al., 2018). Compared with western countries, the cases of HCC in China account for about half of the total cases worldwide(Parkin, et al., 2001). Statistical analysis revealed that the average five-year survival rate of HCC patients was less than 6%(Chen, et al., 2016). It has only been in recent years that harnessing the lncRNA in the fight against cancer has become well appreciated. Previous outcomes had shown that lncRNA DIO3OS expression was closely related to a multitude of cancers, including LIHC(Song, et al., 2019), but the specific role of DIO3OS in HCC is still unclear.

Regulatory T(Treg) cells are a subset of CD4 T cells. Treg cells have immunosuppressive ability, which can avoid the occurrence of autoimmune diseases, but there is also the possibility that tumors can escape the immune defense of the body(Nishikawa and Sakaguchi, 2010). A number of studies have demonstrated that Treg cells easily infiltrate the tumor microenvironment(TME), which often predicts a poor prognosis for patients(Ohue and Nishikawa, 2019). FOXP3, a member of the forked transcription factor family, is a determinant of differentiation and development of Treg cell and plays a key role in regulating immune homeostasis(Marson, et al., 2007). In the TME, Treg cells can secrete TGF-β, IL-10, IL-35 and other inhibitory cytokines to play an immunosuppressive role(Zhou, et al., 2015). In addition, the IL-2α chain receptor(CD25) highly expressed on the surface of Treg cells can competitively bind with IL-2, giving Treg cells a competitive advantage in utilizing the limited IL-2 in TME, thus inhibiting the proliferation of CD4⁺T cells and CD8⁺T cells and inhibiting anti-tumor immunity(Sakaguchi, et al., 1995). In addition, the surface of Treg cells constitutionally express cytotoxic T lymphocyte-4 (CTLA-4) and lymphocyte-activating gene-3(LAG-3), and these inhibitory receptors can interact with DCs surface ligand CD80/CD86 to induce indoleamine 2,3-dioxygenase(IDO) enzyme to further inhibit the ability of T cells(Echarti, et al., 2019). Previous studies also demonstrated that Treg cells can express granzyme in TME. Granzyme and perforin-like molecules inhibit antitumor immunity by dissolving NK cells and CTL cells(Cao, et al., 2007). Our analysis results showed that DIO3OS was significantly negatively correlated with Treg cells in HCC, as well as the expression of FOXP3, a specific marker of Treg cells.

Natural killer (NK) cells are important congenital cytotoxic lymphocytes, which have the ability to recognize and kill tumor cells quickly and efficiently(Fregni, et al., 2012). One of the major factors limiting NK cell activity is TME. Schleypen et al. found that, in renal cell carcinoma, a reduction in the number of tumor-infiltrating NK cells and down-regulation of CD16 receptor expression indicated attenuated NK cell cytotoxic activity(Schleypen, et al., 2006). The TME contains many cells with immunosuppressive functions, such as Treg cells, tumor-associated macrophages(TAM), etc(Arneth, 2019). These cells produce and secrete factors that directly or indirectly inhibit the activation of NK cells, including IL-6, IL-10, TGFβ and IDO(Konjević, et al., 2019). TGF-β downregulates the expression of activating receptors and effector functions in NK cells, and also reduces IL-2-mediated mitochondrial metabolism, including oxidative phosphorylation (OXPHOS) and respiratory function in human NK cells(Wang, et al., 2020).In addition, tumor derived metabolites such as adenosine and lactate inhibit the antitumor activity of NK cells(Scott and Cleveland, 2016). Adenosine stimulates NK cells through the adenosine A_2A receptor (A_2AR) and inhibits its function(Young, et al., 2018). However, high concentrations of lactate and low pH decreased the cytotoxic activity of NK cells. In advanced liver disease and hepatocellular carcinoma, accumulation of lactate in the liver microenvironment inactivates hepatotoxic lymphocytes such as NK cells and CD8 T cells(Piñeiro Fernández, et al., 2019; Sun, et al., 2015). NK cell activity is inhibited by TME, and tumor cells disrupt the balance between NK cell inhibitory activation and inhibitory signaling, resulting in NK cell dysfunction(Habif, et al., 2019).

In the tumor immunity of the body, in order to initiate an effective adaptive immune response, tumor associated antigen(TAA) needs to be presented to T cells by antigen-presenting cells(APC)(Peterson and Barry, 2020). Dendritic cells(DC) are considered as the most important APCs. DCs are part of the hematopoietic cell lineage, and three subsets of DCs can be roughly identified from blood and lymphoid organs, including plasmacytoid DCs (pDCs), conventional type 1 DCs (cDC1s), and conventional type 2 DCs (cDC2s)(Amon, et al., 2019). It has been proved in many studies that the interaction between NK cells and DCs, especially cDC1s, is of great significance in regulating the immune response of cancer(Bödder, et al., 2020). CCL5, XCL1, and XCL2 produced by NK cells can promote the entry of cDC1s into the TME(Böttcher, et al., 2018). IL-12 produced by activated DCs can stimulate NK cell activation and anti-tumor T cell immunity(Sarhan, et al., 2015). The immunosuppressive factors in the TME can not only inhibit the aggregation of DC cells, but also inhibit their stimulatory activity. Studies have shown that tumor derived prostaglandin 2(PGE2) inhibits the production of IL12 by cDC1s, while downregulating the expression of costimulatory molecules and inhibiting the induction of NK cells by cDC1s, resulting in impaired NK cell function(Böttcher, et al., 2018). In addition, TGF-β and vascular endothelial growth factor A(VEGF-A) secreted by tumors can affect the differentiation and maturation of DCs, while TGF-β also regulates the activity of T cells, B cells, and macrophages, and supports the growth of Treg cells(Farhood, et al., 2020; Suuring and Moreau, 2021). To sum up, DCs interact with a variety of immune cells in tumor immunity and are regarded as key targets in the body's immune response. As far as the current status of immunotherapy is concerned, DC cells have gradually become a research hotspot that has attracted much attention.

Carcinoma is a genetic and immune-mediated disease. Tumor immunotherapy is the most attractive anticancer method in the field of medicine. However, due to the balance between immune cells, the differences in immune cell infiltration between different tumor tissues, and the complexity of individual immune systems, immunotherapy dose not achieved the desired effect, and many patients develop many immune system problems during treatment. Therefore, there are still many obstacles to targeting immune cells within tumors.

Our analysis showed that DIO3OS in HCC often predicted negative prognosis of patients, and this lncRNA has certain accuracy and sensitivity as a diagnostic indicator. A large amount of data proves that lncRNAs cannot translate proteins, but have diverse biological functions. Functional analysis found that the expression of DIO3OS in HCC was significantly correlated with a variety of signaling pathways involved in immune regulation, and tumor purity analysis also confirmed that low levels of DIO3OS in the tumor environment were positively correlated with the degree of immune cell infiltration in tumors. After that, we focused on the immune analysis of HCC, and found that DIO3OS was significantly positively correlated with T cells, B cells, NK cells, macrophages, DCs and other classical immune effector cells, while significantly negatively correlated with immunosuppressive Treg cells. Then we found various markers of liver immune cells and conducted correlation analysis with DIO3OS. The results were consistent with the conclusions of the immune cell correlation analysis. Studies have shown that DIO3OS can disrupt the Hedgehog(Hh) pathway by binding to miR-328 mediated Hh interacting protein (Hhip), thereby inhibiting the occurrence of HCC(Wang, et al., 2020). This further proves that DIO3OS is an effective target for the treatment of HCC, but few studies on DIO3OS in tumor immunity can be found. Therefore, our study can provide a new idea for the treatment of HCC, by upregulating the expression of DIO3OS to activate the anti-tumor activity of NK cells or DC cells, or inhibit the activity of Treg cells to prevent their immunosuppressive effects. Our research is based on big data analysis of multiple database. Despite our comprehensive collection of relevant data, the study is still flawed, demonstrating a lack of experimental and clinical validation. Therefore, the preliminary theoretical basis we currently propose requires more follow-up studies to testify.

In summary, decreased of DIO3OS expression in HCC indicates poor prognsis in patients with increased Treg cells infiltration and decreased other immune cell infiltration. Besides, low DIO3OS expression is associated with multiple inflammatory pathway, and is positively or negatively correlated with multiple immunomarker genes of immune cells. These evidences suggest that DIO3OS may play an important role in tumor immune cell infiltration and may serve as a prognostic biomarker or therapeutic target for HCC patients.

Acknowledgments

This work was supported by the Shandong Natural Science Fondation(ZR2020MH415)

Data availability statement

All data generated or analyzed during this study are from the public database. And, all experimental protocols were performed in accordance with relevant guidelines.

Author contributions

Conception and design: Y Wang; Administrative support: D Zhang; Provision of study materials or patients: X Hao, P Sun; Collection and assembly of data: D Cao, X Hao, P Sun; Data analysis and interpretation: Y Wang, J Liu; Manuscript writing: All authors; Final approval of manuscript: All authors.

Competing interests

The authors declare no competing interests.

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Supplement figure. Correlation analysis of multiple immune cell markers and lncRNA DIO3OS.

No competing interests reported.

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Decreased DIO3OS expression predicts poor prognosis in hepatocellular carcinoma and is associated with immune infiltration

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