CN116837096A

CN116837096A - Biomarker for regulating liver cancer immune microenvironment and judging prognosis and application thereof

Info

Publication number: CN116837096A
Application number: CN202310541009.2A
Authority: CN
Inventors: 左学良; 蔡娟; 董润雨; 陈志强
Original assignee: First Affiliated Hospital of Wannan Medical College
Current assignee: First Affiliated Hospital of Wannan Medical College
Priority date: 2023-05-15
Filing date: 2023-05-15
Publication date: 2023-10-03

Abstract

The invention relates to the field of biotechnology, and discloses a biomarker for regulating liver cancer immune microenvironment and judging prognosis and application thereof, and the biomarker comprises application of the biomarker in a product for regulating liver cancer immune microenvironment and judging prognosis, wherein the biomarker is HECTD2, a nucleic acid sequence of the biomarker is shown as SEQ ID NO.1, HECTD2 is knocked out to inhibit proliferation, migration and invasion of HCC cells, and HECTD2 is overexpressed to promote HCC development. The invention fully researches the related biological roles of other HECTE 3ubiquitin ligase subfamily genes in HCC; knocking out HECTD2 has been shown to inhibit proliferation, migration and invasion of HCC cells, whereas overexpression of HECTD2 promotes HCC progression, which is of greater significance for the development of therapeutic drug molecules to target the activity of members of the "other" subfamily HECTE3s in human diseases.

Description

Biomarker for regulating liver cancer immune microenvironment and judging prognosis and application thereof

Technical Field

The invention relates to the technical field of biology, in particular to a biomarker for regulating liver cancer immune microenvironment and judging prognosis and application thereof.

Background

Primary liver cancer is the sixth most common cancer and the third most common cancer cause of death in the world, with about 90.6 tens of thousands of new cases and 83 tens of thousands of deaths. Hepatocellular carcinoma (HCC) is the most common primary liver cancer, accounting for about 90% of the total number of cases. In recent years, immunotherapy has become the mainstream of liver cancer treatment, bringing new hopes for liver cancer patients. However, the early diagnosis of HCC is still not satisfactory, with a survival rate of only 18% over 5 years. Therefore, the search for new molecular targets and prognostic biomarkers is critical for immunotherapy of liver cancer patients.

E3ubiquitin ligase is a key component of the ubiquitination cascade, which tightly controls the efficiency and substrate specificity of the ubiquitination reaction. Generally, three categories are defined: RING-E3s, HECT-E3s, and RBR-E3s. Furthermore, human HECT E3s is divided into three subfamilies according to the structure of its N-terminal substrate binding domain: "NEDD4", "HERC" and "other" subfamilies. The "NEDD4" and "HERC" subfamilies have identical N-terminal domains, differing in the possession of the WW and C2 domains, respectively, as well as the RCC 1-like domain. The "other" HECT E3ubiquitin ligase subfamily genes have 13 members, each lacking a WW or RLD domain, with different N-terminal regions. Here we focused on the function of eight less studied genes in this family in cancer, which are E6AP, UBE3B, UBE3C, HECTD1, HECTD2, HECTD3, HECTD4 and HACE1.

E6AP, one of the starting members of the HECT E3s family, is a 100kDa cellular protein encoded by the UBE3A gene and exhibits aberrant activity in a variety of cancers. In prostate cancer, non-small cell lung cancer and lymphomas, it acts as an oncogene to drive the development of tumors by targeting multiple key tumor factors. Studies have shown that UBE3B is expressed at 7-8 fold different levels in cancer cell lines than in normal cell lines, particularly in the LN428 glioblastoma cell line. Furthermore, UBE3B knockout sensitizes glioma cells to clinically reachable doses of temozolomide treatment. UBE 3C-mediated degradation of the tumor suppressor membrane binding protein 7 promotes glioma progression. UBE3C can also promote lung tumor development by promoting degradation of the p53 accessory protein AHNAK, and enhance transcription of stem cell-related genes in non-small cell lung cancer. HECTD1 plays a key role in regulating cell adhesion and motility and is involved in cancer migration through ubiquitinated PIPKic 90. Recently, it has also been shown that the deletion of HECTD1 increases the expression of Snail, which in turn leads to down-regulation of E-calpain. In prostate cancer, HECTD2 can down-regulate its expression and promote the growth of prostate cancer cells by non-coding RNAmiR-221. In addition, HECTD2 has been found to have a role in regulating renal cell carcinoma, including its effects on proliferation, apoptosis, migration and growth [17]. HECTD3 prevents its recruitment to the death-inducing signaling complex for self-cleavage and subsequent activation in breast cancer by ubiquitinating caspase-8 [18]. Long-chain non-coding RNA HEIH can promote cell proliferation, migration and invasion by increasing expression of HECTD4 in cholangiocarcinoma [19]. HACE1 can mediate the development of autophagy in heart disease and can also inhibit cell invasion of colorectal cancer by ubiquitination.

The "other" HECT E3ubiquitin ligase subfamily genes are closely related to cancer. However, their function in HCC has not been fully studied. We aimed to explore the possible roles of "other" HECT E3ubiquitin ligase subfamily members in HCC using bioinformatics databases to assess their expression pattern, prognostic value and their status of immune infiltration. Our study may provide a new therapeutic target for the treatment of HCC.

Disclosure of Invention

The invention provides a biomarker for regulating liver cancer immune microenvironment and judging prognosis and application thereof, and aims to solve the technical problem that the functions of other HECT E3ubiquitin ligase subfamily genes in HCC are not specifically studied.

The invention is realized by adopting the following technical scheme: the application of the biomarker in the products for regulating liver cancer immune microenvironment and judging prognosis is that the biomarker is HECTD2, and the nucleic acid sequence of the biomarker is shown as SEQ ID NO. 1.

The HECTD2 knockout inhibited proliferation, migration and invasion of HCC cells, and HECTD2 overexpression promoted HCC progression.

A biomarker preparation for regulating liver cancer immune microenvironment and judging prognosis biological product, comprising the HECTD2.

The biologic includes: reagents, kits, chips.

A primer pair for detecting a biomarker, comprising a HECTD2 primer pair, wherein the forward primer is; 5'-AGTTCACCTGCACATCTTGTTT-3'; the reverse primer is as follows: 5'-GCCTTCATttCGGATGATGC-3'.

An application of primer pair in regulating liver cancer immune microenvironment and judging prognosis.

A method for regulating liver cancer immune microenvironment and judging prognosis detects the expression quantity of biomarker.

And detecting by using a primer pair.

Compared with the prior art, the invention has the beneficial effects that:

the invention comprehensively researches the related biological roles of other HECT E3ubiquitin ligase subfamily genes in HCC; the results of the study showed that there are three genes (HECTD 2, HECTD3 and HACE 1) that could be biomarkers for poor prognosis of HCC; their expression level and infiltrated CD4 in HCC ⁺ T cells, CD8 ⁺ The abundance of T cells, MDSCs and Tregs are positively correlated, and furthermore, their expression is clearly correlated with the expression of immune checkpoint molecules (CTLA-4, PDCD1 and TIM-3) and TMB in HCC;

while HECTD2 knockout inhibits proliferation, migration and invasion of HCC cells, HECTD2 overexpression promotes HCC progression, which is of greater interest in the development of therapeutic drug molecules to target the activity of "other" subfamily HECT E3s members in human disease.

Drawings

FIG. 1 is a graph of mRNA expression levels in ubiquity of the "other" subfamily of HECT E3ubiquitin ligases (E3 s) analyzed by TIMER database, wherein:

FIG. 1A is a graph showing the expression level of UBE3A in a carcinoma;

FIG. 1B shows a graph of HECTD1 expression levels in flood carcinoma;

FIG. 1C shows a map of the expression level of UBE3B in a pan-carcinoma;

FIG. 1D shows a graph of HECTD2 expression levels in flood carcinoma;

FIG. 1E depicts a map of the expression levels of UBE3C in a pan-carcinoma;

FIG. 1F shows a graph of HECTD3 expression levels in flood carcinoma;

FIG. 1G shows a map of HACE1 expression levels in a pan-carcinoma;

FIG. 2 is a graph of mRNA expression in HCC of the "other" subfamily of HECT E3ubiquitin ligase (E3 s) in the TCGA database, wherein:

FIG. 2A is a graph showing the mRNA expression profile of UBE3A between normal tissue and HCC tissue in the TCGA database;

FIG. 2B is a graph showing the mRNA expression profile of UBE3B between normal tissue and HCC tissue in the TCGA database;

FIG. 2C is a graph showing the mRNA expression profile of UBE3C between normal tissue and HCC tissue in the TCGA database;

FIG. 2D is a graph showing the mRNA expression profile of HACE1 between normal tissue and HCC tissue in the TCGA database;

FIG. 2E is a graph of mRNA expression of HECTD2 between normal tissue and HCC tissue in the TCGA database;

FIG. 2F is a graph of mRNA expression of HECTD3 between normal tissue and HCC tissue in the TCGA database;

FIG. 2G is a graph of mRNA expression of HECTD4 between normal tissue and HCC tissue in the TCGA database;

FIG. 3 is a graph of the expression level of the "other" subfamily of HECT E3s genes in the UALCAN database in normal samples and HCC samples, wherein:

FIG. 3A is a graph of UBE3A expression levels in a UALCAN database in normal samples and HCC samples;

FIG. 3B is a graph of UBE3B expression levels in a UALCAN database in normal samples and HCC samples;

FIG. 3C is a graph of UBE3C expression levels in a UALCAN database in normal samples and HCC samples;

FIG. 3D is a graph of the expression level of HACE1 in a UALCAN database in a normal sample and an HCC sample;

FIG. 3E is a graph of HECTD2 expression levels in a UALCAN database in normal samples and HCC samples;

FIG. 3F is a graph of HECTD3 expression levels in a UALCAN database in normal samples and HCC samples;

FIG. 3G is a graph of HECTD4 expression levels in a UALCAN database in normal and HCC samples;

FIG. 3H is a graph of HECTD4 expression levels in a UALCAN database in normal samples and HCC samples;

FIG. 4 is an analytical profile of prognostic value of the "other" subfamily of HECT E3s in hepatocellular carcinoma (HCC), wherein:

FIG. 4A is an analytical profile of total survival of "other" subfamily genes (UBE 3A, HACE1, HECTD2 and HECTD 3) of HECT E3s by Kaplan-Meier plot analysis;

FIG. 4B is an analytical profile of Progression Free Survival (PFS) of HECT E3s "other" subfamily genes (UBE 3A, HACE1, HECTD2 and HECTD 3) by Kaplan-Meier plot analysis;

FIG. 4C is an analysis map of the prognostic value of HECT E3s "other" subfamily genes (HECTD 2, HECTD3 and HACE 1) in a TCGA dataset using R software;

FIG. 5 is an analytical profile of prognostic value of "other" subfamilies of HECT E3s in HCC by Kaplan-Meier Plotter and R software, wherein:

FIG. 5A is an analysis chart of total survival (OS) of UBE3B, UBE C and HECTD1 by Kaplan-MeierPlotter analysis;

FIG. 5B is an analytical profile of Progression Free Survival (PFS) analysis of UBE3B, UBE3C and HECTD1 by Kaplan-MeierPlotter;

FIG. 5C is an analysis map of the prognostic value of HECT E3s in HCC by R software analysis of the "other" subfamilies (UBE 3A, UBE3B, UBE3C, HECTD1 and HECTD 4);

FIG. 6 is an analytical map of the association of HECT E3s "other" subfamily gene expression with clinical pathology characteristics of HCC patients in the UALCAN database, wherein:

FIG. 6A is an analytical profile of the relationship of HECTD2 expression to clinical pathology in HCC patients;

FIG. 6B is an analytical profile of the relationship of HECTD3 expression to the clinical pathology of HCC patients;

FIG. 6C is an analytical profile of the relationship of HACE1 expression to clinical pathology of HCC patients;

FIG. 7 is a functional enrichment analysis map of the "other" subfamily of HECT E3s and its co-expressed genes in HCC, wherein:

FIG. 7A is a graph of Gene Ontology (GO) analysis of HECT E3s "other" subfamily;

FIG. 7B is a Kyoto gene and genome encyclopedia (KEGG) pathway enrichment analysis map of HECTD 2;

FIG. 7C is a KEGG pathway enrichment analysis profile of HECTD 3;

FIG. 7D is a volcanic plot of HECTD2, HECTD3 and HACE1, which co-express genes in HCC;

FIG. 7E is a heat map of the first 50 genes with positive correlation to HECTD2, HECTD3 and HACE 1;

FIG. 7F is a heat map of the first 50 genes negatively correlated with HECTD2, HECTD3 and HACE 1;

fig. 8 is a functional analysis map of HACE1 and HECTD3 in HCC, wherein:

FIG. 8A is a Kyoto gene and genome encyclopedia (KEGG) pathway enrichment analysis map of HACE 1;

FIG. 8B is a graph of HECTD3 expression versus Tumor Mutational Burden (TMB) at the level of ubiquity;

FIG. 9 is a functional enrichment analysis map of "other" subfamily co-expressed genes of HECT E3s, wherein:

FIG. 9A is an intersection of the co-expressed genes of HECTD2, HECTD3 and HACE 1;

FIG. 9B is a GO analysis map of the 2617 genes intersected;

FIG. 9C is an analytical profile of KEGG pathway enrichment of the 2617 genes crossed;

FIG. 9D is a graph showing the apparent positive correlation between the expression of the "other" subfamily genes (HECTD 2, HECTD3 and HACE 1) for each HECT E3s gene;

FIG. 10 is an analytical profile of the relationship between HECT E3s "other" subfamily gene expression and the level of immune cell infiltration of HCC, wherein:

FIG. 10A is an analytical profile of upregulation of HECTD2 expression in relation to increased infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells;

FIG. 10B is an analytical profile of upregulation of HECTD3 expression in relation to increased infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells;

FIG. 10C is an analytical profile of upregulation of HACE1 expression in relation to increased infiltration of CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells;

fig. 10D is an analytical profile of the correlation between HECTD2 and bone marrow derived immunosuppressive cells (MDSC) and T cell regulatory (Tregs) in HCC;

FIG. 10E is an analytical profile of the correlation between HECTD3 and MDSC and Tregs in HCC;

FIG. 10F is an analytical map of the correlation between HACE1 and MDSC and Tregs in HCC;

FIG. 10G is an analytical profile of HECTD2 expression versus Tumor Mutational Burden (TMB) at the level of ubiquity;

FIG. 10H is an analytical map of the relationship between expression of HACE1 and TMB;

FIG. 11 is a graph showing the correlation between HECTD2, HECTD3 and HACE1 gene expression and the level of immune cell infiltration of HCC, wherein:

FIG. 11A is an analytical plot of HECTD2 high and low expression sets versus immune cell infiltration level;

FIG. 11B is an analytical profile of HACE1 high and low expression sets as a function of immune cell infiltration level;

FIG. 11C is an analytical plot of HECTD3 high and low expression sets versus immune cell infiltration level;

FIG. 12 is a heat map of the correlation between expression levels of "other" subfamily genes of HECT E3s and immune cell infiltration levels;

fig. 13 is an analytical profile of HECTD2 knockout inhibiting proliferation, migration and invasion of Huh7 cells, wherein:

FIG. 13A is a graph of HECTD2 expression in HCC cell lines (HCCLM 3, focus, and Huh 7) and normal human hepatocyte line QSG-7701 using qRT-PCR;

FIG. 13B is a graph showing the expression of HECTD2 in HCC cell lines (HCCLM 3, focus, and Huh 7) and normal human hepatocyte line QSG-7701 detected by Westernblotting;

FIG. 13C shows the knockdown of HECTD2 expression in Huh7 cells with three siRNAs. Confirming an analysis map of knockout efficiency by qRT-PCR;

FIG. 13D is an analytical profile of Westernblotting to verify the knockout efficiency of HECTD2 in Huh7 cells;

FIG. 13E is an analytical map of the number of clones of Huh7 cells after HECTD2 knockout, as demonstrated by plate cloning experiments;

FIG. 13F is an analytical map of proliferation of Huh7 cells after HECTD2 knockout detected by CCK-8 assay;

FIG. 13G is an analytical profile of the proliferation potency of Huh7 cells detected by EdU;

FIG. 13H is an analytical profile of the effect of knock-out HECTD2 on Huh7 cell migration detected by a Transwell experiment;

FIG. 13I is an analytical profile of the effect of knock-out HECTD2 on Huh7 cell invasion detected by a Transwell experiment;

FIG. 13J scratch assay, analytical profile assessing the effect on Huh7 cell migration ability following HECTD2 knockout;

fig. 14 is an analytical profile of overexpression of HECTD2 promoting proliferation, migration and invasion of Focus cells, wherein:

FIG. 14A is an analytical profile of HECTD2 overexpression efficiency in Focus cells confirmed by qRT-PCR;

FIG. 14B is an analytical plot of HECTD2 overexpression in Focus cells using Westernblotting;

FIG. 14C is an analytical map of the number of Focus cell clones after HECTD2 overexpression was verified by plate cloning experiments;

FIG. 14D is an analytical map of proliferation of Focus cells after HECTD2 overexpression, using the CCK-8 assay;

FIG. 14E is an analytical profile of the effect of EdU detection of HECTD2 overexpression on Focus cell proliferation;

FIG. 14F is an analytical profile of migration and invasion of Focus cells after detection of HECTD2 overexpression by transwell experiments;

FIG. 14G is an analytical profile of a scratch assay to detect the effect of HECTD2 overexpression on Focus cell migration.

Detailed Description

The present invention will be further described with reference to the accompanying drawings and detailed description, wherein it is to be understood that, on the premise of no conflict, the following embodiments or technical features may be arbitrarily combined to form new embodiments.

The application of a biomarker in a product for regulating liver cancer immune microenvironment and judging prognosis is disclosed, wherein the biomarker is HECTD2 (namely NM_001284274.3Homo sapiens HECT domain E3ubiquitinprotein ligase 2), and the nucleic acid sequence of the biomarker is shown as SEQ ID NO. 1.

The biologic includes: reagents, kits, chips.

The primer pair is applied to regulating liver cancer immune microenvironment and judging prognosis.

Example 1:

expression level of "other" subfamily genes of HECT E3s in ubiquity

As shown in fig. 1-3, the expression levels of the "other" HECT E3ubiquitin ligase subfamily genes in various cancer types were analyzed using TCGA data; the results show that mRNA levels of "other" subfamily genes of HECT E3s (UBE 3A, UBE3B, UBE3C, HECTD1, HECTD2, HECTD3 and HACE 1) are deregulated in pan-cancer, as shown in fig. 1A-G; furthermore, their expression levels were all up-regulated in HCC compared to normal liver tissue;

the results of the UALCAN database also confirm the abnormal expression pattern of the "other" HECT E3ubiquitin ligase subfamily genes in HCC; the expression of the remaining 7 genes in HCC was elevated except for HECTD4 and had statistical significance as shown in fig. 2A-G;

in paired data analysis with R language, expression of HECTD1, UBE3A, UBE3B, UBE3C, HECTD2, HECTD3, HECTD4 and HACE1 were found to be significantly over-expressed in HCC, as shown in fig. 3A-H;

the above data indicate that the expression of the "other" HECT E3ubiquitin ligase subfamily genes in HCC is abnormal.

Example 2:

prognostic value of "other" subfamily genes of HECT E3s in HCC

As shown in fig. 4-5, the prognostic significance of the "other" HECT E3ubiquitin ligase subfamily genes was evaluated by KM database; as a result, it was found that low expression of UBE3A was associated with shorter Overall Survival (OS) and progression-free survival (PFS), high expression of HECTD2, HECTD3, and HACE1 was significantly associated with poor OS and PFS, as shown in fig. 4A and B, however, expression of UBE3B and UBE3C was not significantly associated with OS and PFS;

meanwhile, the low expression of HECTD1 was also associated with only a short OS in HCC patients, as shown in fig. 5A and B, and furthermore, the survival data analysis of HECTD2, HECTD3 and HACE1 in TCGA database showed similar results, as shown in fig. 4C, but the survival analysis of the remaining five genes (UBE 3A, UBE3B, UBE3C, HECTD1 and HECTD 4) in TCGA database showed no significant significance, as shown in fig. 5C; the above results indicate that increased expression of HECTD2, HECTD3 and HACE1 suggests a poor prognosis for HCC patients.

Example 3:

relationship of HECTD2, HECTD3 and HACE1 expression with HCC clinical pathology

As shown in fig. 6, HECTD2, HECTD3 and HACE1 were selected to analyze the relationship between their expression levels and clinical pathological parameters, including sex, age, tumor stage, tumor grade, presence or absence of lymph node metastasis in HCC, and TP53 gene mutation;

as shown in fig. 6A, the expression level of HECTD2 in HCC correlated with gender, age and cancer stage, the expression level of HECTD2 in stage 1, stage 2 and stage 3 cancer tissues was significantly higher compared to normal tissues, with respect to tumor grade, up-regulation of HECTD2 expression was observed in grade 1, grade 2, grade 3 and grade 4 tumors, and the expression of HECTD2 increased with increasing pathological grade; furthermore, the expression level of TP53 gene mutation is obviously higher than that of TP53 wild type; however, the relationship between high and low expression of HETCD2 and lymph node metastasis in HCC patients is not evident, as shown in fig. 6A; likewise, the expression levels of HECTD3 and HACE1 in HCC were significantly correlated with clinical pathology characteristics of HCC patients, including gender, age, tumor stage, and tumor grade, as shown in FIGS. 6B and C; the above results suggest that expression of these genes plays an important role in the progression of HCC, providing a instructive sense in the treatment of patients.

Example 4:

functional enrichment analysis of HECT E3 s' other subfamily genes and their co-expressed genes in HCC

As shown in FIGS. 7-9, further GO and KEGG analyses were performed on these genes; GO analysis showed that these three genes (HECTD 2, HECTD3 and HACE 1) are mainly involved in proteasome-mediated protein ubiquitination and are closely related to the activity of various ubiquitin protein transferases, as shown in FIG. 7A; HECTD2 is involved in the regulation of Notch signaling pathways, also associated with differentiation and cell cycle of type 1T helper (Th 1) cells, type 2T helper (Th 2) cells, as shown in FIG. 7B; HECTD3 is enriched in MAPK signal path, ras signal path and the like as shown in FIG. 7C; HACE1 is involved in inositol phosphate metabolism and is resistant to platinum drugs and Hippo signaling pathway, as shown in fig. 8A;

since these genes belong to the same subfamily, more information about the function of this family is obtained by their identical co-expressed genes; co-expressed genes of the three genes were obtained from Linkedomics database, and volcanic and thermal maps of these co-expressed genes in HCC were plotted, with the first 50 genes showing positive or negative correlation, as shown in FIG. 7F; thereafter, all the co-expressed genes of the three genes were crossed to obtain 2617 genes, as shown in fig. 9A;

thereafter, GO and KEGG analysis showed that these co-expressed genes have obvious correlation with multiple important pathways; specifically, the KEGG pathway is mainly enriched in coronavirus diseases, ubiquitin-mediated proteasome degradation, PD-L1 checkpoint pathway, as shown in fig. 9B; GO functional analysis includes Biological Processes (BP), cellular Composition (CC), and Molecular Function (MF); for BP, these genes are mainly enriched in reactions to oxidative stress, RNA splicing, nuclear cytoplasmic trafficking, and rRNA processing; for CC, these co-expressed genes are mainly concentrated on ribosomes, including cytoplasmic ribosomes and ribosomal subunits, etc.; for MF, the biological functions of these genes are manifested by ATP-dependent activity on DNA, binding of RNA polymerase, as shown in fig. 9C;

thereafter, the TIMER database was used to analyze the correlation between these genes, where positive correlation was found between the expression of each member, with HECTD2 and HACE1 being the strongest in correlation, as shown in FIG. 9D; based on the above results, it was revealed that these genes can cooperate with co-expressed genes to participate in the development and progression of HCC and immune response of HCC patients by regulating ubiquitin-mediated proteasome degradation, PD-L1 checkpoint pathway, and the like.

Example 5:

as shown in FIGS. 8, 10-12, the relationship between expression of "other" subfamily genes of HECT E3s and the level of immune cell infiltration of HCC

First, the relation between the expression of the above genes (HECTD 2, HECTD3 and HACE 1) and the level of Tumor Infiltrating Lymphocytes (TILs) was further studied using TIMER database; the results of the study showed that upregulation of HECTD2 and HACE1 expression was associated with increased infiltration of B cells, cd8+ T cells, cd4+ T cells, macrophages, neutrophils and dendritic cells, while upregulation of HECTD3 expression was associated with infiltration of all immune cells except B cells;

specifically, HECTD2 is clearly associated with the level of infiltration of cd4+ T cells and macrophages, while HECTD3 and HACE1 are clearly associated with the level of infiltration of macrophages and neutrophils, as shown in fig. 10A-C; at this time, it was found that the expression levels of these genes were also clearly positively correlated with the infiltration levels of bone marrow derived immunosuppressive cells (MDSCs) and regulatory T cells (Tregs), as shown in FIGS. 10D-F; furthermore, the expression levels of HECTD2 and HACE1 also correlated with Tumor Mutational Burden (TMB) of HCC, as shown in fig. 10G and H, but the expression of HECTD3 did not correlate significantly with TMB of HCC, as shown in fig. 8B;

in order to further understand the expression relationship between the three genes and 22 immune cell types in the TCGA-LIHC queue, the expression of related tumor immune infiltration cells is quantified through enrichment analysis of a single sample gene set; CD4 in HECTD2 and HACE1 high expression group ⁺ Infiltration levels of T cells, memory B cells and Th2 cells were higher than in the low expression set, where CD8 was expressed ⁺ T cell expression was higher as shown in FIGS. 11A and B; the infiltration levels of dendritic cells, NK cells and T helper cells 17 were all higher in the HECTD3 high expression group, as shown in fig. 11C; the results of the TIMER database were further validated using the cibelort algorithm and presented in the form of a heat map, as shown in fig. 12;

the above results show that these three members of the "other" subfamily genes of HECT E3s are closely related to TILs and may play a key role in regulating HCC immune microenvironment; these three genes are involved as a certain immune component in regulating HCC progression and metastasis.

Example 6:

correlation analysis of expression of HECT E3s "other" subfamilies with expression of immune checkpoint molecules in HCC

First, the correlation between the expression of these genes and the expression of immune checkpoint molecules in different immune infiltrating cells was analyzed; expression of these three genes in HCC was found to be positively correlated with expression of marker genes in many immunoinfiltrating cells, such as B cells, tumor-associated macrophages (TAMs), tregs, dendritic cells, th1 cells and Th2 cells;

specifically, expression of HECTD2 has a clear correlation with marker gene expression of B cells, TAM cells and Tregs, as shown in table 1;

table 1 is an analysis of correlation of HECTD2 expression levels with immune checkpoint molecule expression in HCC:

HECTD3 expression was clearly correlated with marker gene expression in TAM, M1 macrophages, M2 macrophages, tregs and Th1 cells, as shown in Table 2;

table 2 is an analysis of correlation of HECTD3 expression levels with immune checkpoint molecule expression in HCC:

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the relationship of HACE1 to these marker genes was reflected in almost all immunoinfiltrating cells, except NK cells and depleted T cells, showing a close correlation between HACE1 and marker gene expression, as shown in table 3;

table 3 shows the close correlation between HACE1 and marker gene expression:

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the infiltration abundance of TAM cells is obviously related to the expression of CCL2 and IL-10, which indicates that the TAM cells possibly participate in the tumor immunosuppression process; in general, T cell depletion is an important factor affecting immune checkpoint blockade; it was also found that the expression of HECTD2, HECTD3 and HACE1 was significantly correlated with the expression of Tregs and T cell marker genes, FOXP3, CCR8, STAT5B, TGFB1 and TIM-3;

the above results further demonstrate that the "other" subfamily genes of HECT E3s genes are clearly associated with the level of immune infiltration of HCC, suggesting that the expression of HECTD2, HECTD3 and HACE1 plays a key role in immune escape of HCC.

Example 7:

as shown in FIG. 13, HECTD2 was highly expressed in HCC cells

The data analysis results show that upregulation of HECTD2, HECTD3 and HACE1 expression is associated with poor prognosis of HCC; to further verify the results of the bioinformatic analysis, a further study of HECTD2 was selected;

at this time, HECTD2 expression in HCC cell lines (QSG-7701, HCCLM3, focus and Huh 7) was detected by qRT-PCR and Westernblotting, respectively, as shown in FIGS. 13A and B; as a result, it was found that mRNA and protein expression levels of HECTD2 were significantly higher in all three HCC cell lines as compared with the normal liver cell line QSG-7701.

Example 8:

as shown in fig. 13-14, HECTD2 significantly promoted proliferation, migration and invasion of HCC cells

To study the potential function of HECTD2 in HCC cells, based on the results of the validation in cell lines, knockdown and overexpression of HECTD2 was performed in Huh7 and Focus cells, respectively, qRT-PCR detection and Westernblotting were performed to validate the transfection efficiency of HECTD2 in Huh7 cells;

the results show that the knockdown efficiencies of si-hectd2#1 and si-hectd2#3 are more pronounced, as shown in fig. 13C and D; then, si-HECTD2#1 and si-HECTD2#3 were selected for subsequent functional studies;

the effect of HECTD2 on HCC cell proliferation was detected by plate cloning, CCK-8 and EdU experiments; wherein, the plate cloning experiments verify that the proliferation of Huh7 cells in the knockout HECTD2 group is significantly inhibited, as shown in fig. 13E; the growth curve of the CCK-8 experiment also shows that proliferation of HCC cells transfected with si-HECTD2 is significantly inhibited compared to those transfected with si-NC, as shown in FIG. 13F; the result of EdU showed that knocking down HECTD2 significantly reduced the proliferative capacity of HCC cells compared to the control group, as shown in fig. 13G;

furthermore, the role of HECTD2 in cell migration and invasion was evaluated by transwell and scratch experiments, as shown in fig. 13H and I, when HECTD2 was knocked down in Huh7 cells, less migrated and invaded cells were detected; in addition, the mobility of Huh7 cells transfected with si-HECTD2 was significantly inhibited after 24 and 48 hours of scoring compared to transfected si-NC, as shown in fig. 13J;

the above results indicate that knocking down HECTD2 can inhibit HCC cell proliferation, migration and invasion in vitro;

afterwards, the HECTD2 over-expression plasmid was transfected into Focus cells, and the transfection efficiency was verified by qRT-PCR and Westernblotting, as shown in FIGS. 14A and B; the results of the plate clone, CCK-8 and EdU experiments showed that overexpression of HECTD2 promoted proliferation of HCC cells, as shown in FIGS. 14C-E; transwell and scratch experiments showed that the migration and invasion capacity of HECTD2 over-expressed cells were significantly improved over control, as shown in FIGS. 14F and G.

Example 9:

data acquisition and processing

RNAseq data for HCC samples is downloaded from UCSC XENA web server (https:// xenabowser. Net/datapages /). Using the "ggplot2" and "reshape2" packages of R4.1.3 software, wilcoxon Rank Sum Test was used to evaluate the expression levels of the "other" HECT E3ubiquitin ligase subfamily genes between the different groups. (http:///www.r-project. Org /).

Example 10:

survival analysis

The biological relevance of the "other" HECT E3ubiquitin ligase subfamily genes to clinical prognosis was assessed by the Kaplan-Meier (KM) method and R software. KM Plotter (http:// kmplot. Com/analysis /) is a published database for assessing the relationship between gene expression and survival trend for a variety of cancers [22]. Survival data downloaded from TCGA was statistically analyzed using the "survivinr" R package and visualized using the "surviviner" R package. The P value is calculated using a logarithmic rank test.

Example 11:

UALCAN analysis

UALCAN (http:// UALCAN. Path. Uab. Edu) is an easy-to-use online interactive web portal for in-depth analysis of TCGA gene expression data [23]. In this study, members of the "other" HECT E3ubiquitin ligase subfamily were analyzed for association with clinical pathological parameters, such as sex, age, cancer stage, tumor grade, presence or absence of lymph node metastasis, TP53 gene mutation, etc., in HCC patients using UALCAN.

Example 12:

immunoinfiltration analysis

TIMER2.0 (http:// TIMER. Comp-genemics. Org) is an on-line platform for comprehensive analysis of tumor immunoinfiltrate cell penetration levels in various cancer types [24]. Differences in expression levels of the "other" HECT E3ubiquitin ligase subfamily genes in different cancer types, as well as the level of immunoinfiltrate cells of the genes of interest in liver cancer were analyzed using TIMER.

Example 13:

QSG-7701, HCCLM3, focus and Huh7 cells were all purchased from Shanghai cell biology institute at the national academy of sciences. Cells were cultured in an incubator at 37℃with 5% CO2 using DMEM (HyClone, logan, utah, USA) medium containing 10% fetal bovine serum, 100U/mL penicillin and 100. Mu.g/mL streptomycin.

Three different small interfering RNA (siRNA) sequences against HECTD2 were designed and synthesized by GenePharma (Shanghai, china). The siRNA was transfected with RiboBio transfection reagent in hepatoma cells according to the manufacturer's protocol, and subsequent experiments were performed 24h after transfection. The sequence of siRNA targeting HECTD2 is as follows: si-HECTD2#1: forward direction: 5'-GGAUGCAUCAUCCGAATT-3'; reversing: 5'-UUCGGAUGAUGAUGCAUCCTT-3'; si-HECTD2#2 forward direction 5'-CCUGCAAAGCCUGAAGAAUTT-3'; reversing: 5'-AUUCUUCAGGCUUGCAGGTT-3'; si-HECTD2#3 forward direction 5'-CCAUUGGUUAGCAGCUUUTT-3'; reversing: 5'-AAAGCUGCUAAACCAAUGGTT-3'.

Example 14:

qRT-PCR experiments

cDNA was synthesized using Fastking gDNA Dispelling RT SuperMix (TIANGEN, beijing, china). qRT-PCR was performed using SuperReal PreMix Plus (TIANGEN) as indicated. The relative expression level of RNA was calculated by the 2-DeltaCt quantization method. qRT-PCR amplification using the following primers: HECTD2: forward direction: 5'-AGTTCACCTGCACATCTTGTTT-3'; reversing: 5'-GCCTTCATttCGGATGATGC-3'.

Example 15:

western blotting experiment

Total protein was extracted from lysed cells and the BCA assay kit (Beyotime) was used to determine protein concentration. Proteins were separated in 10% SDS polyacrylamide gel electrophoresis and transferred to PVDF membrane. By QuickBlock ^TM Blocking buffer (Beyotime) blocked the membrane and incubated overnight at 4 ℃ with a proportionally diluted primary antibody. HECTD2 was purchased from Abcam (Cambridge, UK) and GAPDH was purchased from Servicebio (Wuhan, china). Horseradish peroxidase (HRP) -labeled goat anti-rabbit or anti-mouse IgG antibody (Cell Signaling Technology, danvers, MA, USA) was added on day 2, the membrane was treated at room temperature for 2 hours, developed with SuperKine ECL detection reagent (Abbkine, wuhan, china) and Bio-Rad (Hercules, CA, USA) and exposed.

Example 16:

cell proliferation assay

Cell counting kit-8 (CCK-8) and 5-ethynyl-2' -deoxyuridine staining (EdU) assays were used to test the proliferative capacity of hepatoma cells. For CCK-8 assays, a total of 2X 103 transfected cells were seeded uniformly in 96-well plates and incubated for 24, 48, 72 and 96 hours. CCK-8 reagent (BestBio, shanghai, china) was added to each well and incubated for 2 hours. Absorbance at a wavelength of 450nm was detected on an enzyme immunoassay analyzer (Bio-Rad). EdU detection was performed using the EdU detection kit (RiboBio). The EdU binding rate was calculated as the ratio of the number of EdU-bound cells to the number of Hoechst 33342-stained cells.

Example 17:

plate cloning test

Huh7 and Focus cells were grown in 6 well plates at 37 ℃ for 2 weeks, then fixed in methanol, and stained with 0.1% crystal violet solution for 15 minutes before colony counting.

Example 18:

cell migration and invasion assay

The migration ability of hepatoma cells was evaluated by scratch test and transwell test. Transfected hepatoma cells were evenly distributed on plates and cultured to 100% confluence in 6 wells. The cell monolayer was then dissected with a sterile 100 ml suction head, creating a clear wound. Cells were washed with PBS to remove floating cells and incubated with fresh serum-containing medium for 48 hours. The scratch area was photographed under a microscope and measured with ImageJ software, and the cell mobility was determined with the following formula: cell mobility (%) = (1-scratch area/original scratch area) ×100%. For the migration assay, transfected cells (2X 104) were suspended in 200 ml of serum-free medium and inoculated into the upper chamber, while the lower chamber was charged with cell culture medium containing 10% FBS. A layer of Matrigel (YB 356234, BD Biosciences, usa) was pre-covered on a transwell upper chamber, placed in a 24-well plate and dried overnight, then the same number of cells was added to the upper chamber. After 48 hours of incubation, the cells were fixed with 4% formaldehyde and stained with 0.1% crystal violet stain for 15 minutes. The migrated or invaded cells were counted using an optical microscope.

Example 19:

statistical analysis

Statistical analysis was performed using GraphPad Prism 8.0 (GraphPad Software, la Jolla, CA, USA). Data are shown as mean ± mean of SEM. Two-sided student t-test was used to analyze group differences. The differences in the "other" HECT E3ubiquitin ligase subfamily genes between tumor and non-tumor specimens were assessed using the Wilcoxon test. The Chi-square test was used to analyze the relationship of HECTD2, HECTD3 and HACE1 expression to clinical pathology. Survival results were compared using Kaplan-Meier curves and log-rank test. The Pearson's test was used to analyze the correlation between levels. P <0.05 is considered statistically significant.

The "other" subfamilies in the HECT E3s family, lack the WW or RLD domains, and have different N-terminal domains compared to the "NEDD4" subfamilies and the "HERC" subfamilies, this diversity possibly conferring selectivity and specificity for E3-substrate and E2-E3 interactions in the ubiquitination process; previous studies have shown that viruses or pathogens can hijack the activity of the "other" subfamilies of HECT E3s to evade the host's immune response, while mutations of the "other" E3ubiquitin ligase subfamilies are also involved in the development of cancer;

based on the data and experimental results of TCGA, family members have biological functions in HCC and determine genes related to HCC prognosis, providing an important idea for clinically relevant targeted therapies.

In the current study, based on the data of the TIMER database, 7 members of the "other" subfamily of HECT E3s were found to be differentially expressed in a variety of cancers, including HCC; then we quantified the expression levels of these 7 genes in HCC using TCGA data, and found that the expression of these 7 genes in HCC was significantly different from the corresponding normal tissue expression levels;

subsequently, by KM survival analysis, it was found that high expression levels of HECTD2, HECTD3 and HACE1 were significantly correlated with poor OS and PFS in HCC and with clinical pathological features such as age, histological grading, tumor grading of HCC patients; these results indicate that these three genes play an important role in the progression and prognosis of HCC;

thereafter, the relationship between the expression of several genes and tumor immune cell infiltration was examined in relation to prognosis and tumor immune microenvironment, where one of the genes was selected for a series of experiments to determine its differential expression in HCC cells, and further found to have carcinogenic effects in HCC cells and enhance HCC progression. To our knowledge, this was the first study of the biological function of "other" HECT E3ubiquitin ligase subfamily members in HCC.

Tumor Microenvironment (TME) is a complex biological ecosystem for the survival and development of cancer cells, and refers to the surrounding environment of cancer cells, including cellular and non-cellular components [32]. Cellular components can enhance tumor resistance by recruiting and secreting various protective cytokines, whereas non-cellular components can mediate resistance by establishing physical barriers and affecting the growth and metabolites of tumor cells [33]. Several studies have shown that immune cell infiltration levels in TME are associated with tumor progression and prognosis [34,35]. Studies have shown that NK cells can secrete a variety of inhibited and activated signaling molecules to kill a variety of neighboring cancer cells [36]. TME can alter the nature of macrophages to allow TAMs to gain the ability to promote tumor metastasis and recurrence. It also inhibits the immune response of tumors, leading to relatively poor prognosis of tumors with high tumor-associated macrophage infiltration and resistance to treatment [37,38]. Our studies found that the expression levels of HECTD2, HECTD3 and HACE1 were significantly positively correlated with the infiltration levels of cd4+ T cells, macrophages, neutrophils and dendritic cells, but negatively correlated with tumor purity. A large number of immunosuppressive cells, such as MDSCs and Tregs, are also present in TMEs. They significantly inhibit infiltration and function of cytotoxic lymphocytes (CTLs), leading to continued tumor growth. We found that the expression levels of these three genes also had a clear positive correlation with the infiltration levels of MDSC and Tregs. And studies have shown that infiltration of immunosuppressive cells in tumors is also an important driver of tumor resistance to Immune Checkpoint Blocking (ICB) therapies [39,40]. This means that abnormally elevated levels of HECTD2, HECTD3 and HACE1 expression in HCC may promote tumor growth by inhibiting the function of anti-tumor T cells, and may also lead to resistance to ICB treatment. For immunotherapy, the variability in the immune response generated by different patients can be attributed to a number of factors. In addition to TME, TMB is also an important factor, high TMB being clearly associated with more T cell recognition and better clinical prognosis [41,42]. The expression levels of the three genes of interest, in addition to HECTD3, also correlated with TMB, further demonstrated that these three genes may be involved in immune escape or resistance to immunotherapy of tumors;

at this time, there is a clear correlation between the expression of HECTD2, HECTD3 and HACE1 and a significant portion of immune checkpoints in HCC; common immune checkpoints for tumor treatment include CD28, CTLA-4, PD-1 and PD-L1; TAM as an important part involved in tumor immunosuppression, its expressed related factors CCL2 and IL10 are also significantly associated with HECTD2, HACE1 and HECTD3, suggesting that they are involved in the tumor immunosuppression process; in addition, TIM-3, which is a key gene regulating the depletion of T cells, has strong positive correlation with the expression of these three genes; thus, it was shown that high expression levels of these three genes play a key role in TIM-3 mediated T cell failure;

one of the genes is selected for verification in the experimental part later; the differential expression of HECTD2 in the HCC cell line is verified, and the role of HECTD2 in proliferation, migration and invasion of liver cancer cells is identified through a functional experiment; it was further verified that high expression of HECTD2 promotes proliferation, migration and invasion of HCC cancer cells and is clearly associated with poor prognosis in HCC patients.

In summary, the invention fully investigated the relevant biological role of the "other" HECT E3ubiquitin ligase subfamily genes in HCC; the results of the study showed that there are three genes (HECTD 2, HECTD3 and HACE 1) that may be biomarkers of poor prognosis for HCC, and that their expression levels are positively correlated with the abundance of infiltrated cd4+ T cells, cd8+ T cells, MDSCs and Tregs in HCC. Furthermore, their expression is clearly associated with the expression of immune checkpoint molecules (CTLA-4, PDCD1 and TIM-3) and TMB in HCC. HECTD2 knockout inhibits proliferation, migration and invasion of HCC cells, while HECTD2 overexpression promotes HCC progression, which is of greater significance for the development of therapeutic drug molecules to target the activity of "other" subfamily HECT E3s members in human disease.

The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but any insubstantial changes and substitutions made by those skilled in the art on the basis of the present invention are intended to be within the scope of the present invention as claimed.

Claims

1. The application of the biomarker in the products for regulating liver cancer immune microenvironment and judging prognosis is characterized in that the biomarker is HECTD2, and the nucleic acid sequence of the biomarker is shown as SEQ ID NO. 1.

2. The biomarker of claim 1, wherein the knockout HECTD2 inhibits proliferation, migration and invasion of HCC cells, and overexpression of HECTD2 promotes HCC progression.

3. A biomarker for preparing a biological product for regulating liver cancer immune microenvironment and judging prognosis, which is characterized by comprising the HECTD2.

4. The biologic of claim 3, wherein said biologic comprises: reagents, kits, chips.

5. A primer pair for detecting a biomarker according to any of claims 1 to 4, comprising a HECTD2 primer pair, wherein the forward primer is; 5'-AGTTCACCTGCACATCTTGTTT-3'; the reverse primer is as follows: 5'-GCCTTCATttCGGATGATGC-3'.

6. The use of the primer pair according to claim 5 for regulating liver cancer immune microenvironment and judging prognosis.

7. A method for modulating the immune microenvironment of liver cancer and for determining prognosis, characterized in that the expression level of the biomarker according to any of claims 1-3 is detected.

8. The method of claim 7, wherein the detection is performed using a primer pair of claim 5.