Overexpression of TMEM79 combined with SMG5 is related to prognosis, tumor immune infiltration and drug sensitivity in hepatocellular carcinoma

doi:10.21203/rs.3.rs-3149747/v1

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Research Article

Overexpression of TMEM79 combined with SMG5 is related to prognosis, tumor immune infiltration and drug sensitivity in hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-3149747/v1

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Journal Publication

published 07 Nov, 2023

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Background

Hepatocellular carcinoma (HCC) is a primary liver malignancy that is now relatively common worldwide. TMEM79 (Transmembrane Protein 79) is a Protein Coding gene. It has been reported to play diagnostic and prognostic markers in a variety of cancers and was found to be closely associated with immune infiltration in kinds of tumors in a follow-up study. One study found that Multiple nonsense-mediated mRNA processes require the involvement of SMG5[1]. SMG5 is associated with immune cell infiltration in HCC[2]. However, the relationship between TMEM79 expression in HCC and prognosis, its role and mechanism of action, and its relationship with SMG5 have not been studied. This article focuses on not only the prognostic role of TMEM79 and its biological significance including immuno-infiltration, tumor mutations and drug sensitivity, but also the interaction with SMG5 in HCC.

Methods

Differential expression analysis and the multiCox proportional hazards regression analyses of TMEM79 and SMG5 were performed by multiple databases. And then IHC was used to validate the differential expressions, correlation of TMEM79 and SMG5, and prognosis of TMEM79 and SMG5 in HCC in our research. Subsequently, we used R software to analyze the clinical phenotype of both: analysis of clinicopathological features, enrichment analysis, analysis of immune infiltration, analysis of immune checkpoints, analysis of drug sensitivity, and immunotherapy. Then TMEM79-related molecules were classified into two types by using R software. And then prognostic analysis, enrichment analysis, and immuno-infiltration analysis were performed for the two types.

Results

Both the database studies and the results of our research group showed that TMEM79 and SMG5 were differentially expressed in HCC and normal tissues. The multiCox proportional hazards regression analyses of TMEM79 and SMG5 showed that they were independent prognostic factors in HCC. Validation of immunohistochemistry showed that differential expression of TMEM79 and SMG5, which influenced the prognosis of patients with HCC, could be an independent prognostic factor. Results of the TCGA database study showed that TMEM79 and SMG5 were correlated with immune infiltration, immune checkpoints, drug sensitivity, and immunotherapy. We typed TMEM79-related molecules in HCC according to R software. Two types of TMEM79 correlated with clinical features, survival of patients with HCC, and immune infiltration.

Conclusion

TMEM79 are highly expressed in HCC and play an important role in the prognosis of patients with HCC. TMEM79 and SMG5 are positively correlated and may both associated with immune infiltration, and closely linked to immune checkpoints, drug sensitivity, and immunotherapy in HCC.

TMEM79

SMG5

HCC

prognosis

immune infiltration

Environment surrounding the tumor is comprised of a diverse population of immune cells, endothelial cells, and fibroblasts. The structure of the TME (The tumor microenvironment) has been demonstrated to impact the efficacy of immune-checkpoint blockade (ICB), which employs the infiltration of immune cells within tumors to revitalize a potent anti-cancer immune reaction [3]. Today, scientists can design therapeutic agents at the cellular molecular level to target well-defined oncogenic sites and cause tumor cell-specific death. Therefore, we need to discover more immune checkpoints to reactivate the immune activation and prevent tumor progression and metastasis.

The incidence of HCC is increasing year by year, making it the sixth most prevalent form of cancer across the globe [4]. Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver and accounts for 90% of hepatic cancers[5]. Despite advances in medical, locoregional and surgical therapies, HCC remains one of the most common causes of cancer-related death globally[6]. The use of immune checkpoint inhibitors has been shown to produce meaningful improvements in survival time in patients with HCC [7].

TMEM79 is a protein-encoded gene that is expressed in membranes in squamous epithelial and prostate gland cells and contributes to epidermal integrity and skin barrier function. TMEM79 has been linked to both dermatitis and atopic dermatitis as potential health concerns. High expression of TMEM79 plays a role in promoting many cancer types, and it has been studied that TMEM79 is a diagnostic marker for prostate cancer [8]. TMEM79 has been reported to be associated with immune cell infiltration in prostate cancer [9]. However, the underlying mechanisms of HCC remain largely unknown despite extensive research into its pathology. We found in our study of the reciprocal protein of TMEM79 that it interacts with SMG5, and SMG5 was closely related to TMEM79 by cBioportal (cBioPortal for Cancer Genomics) .

SMG5 is a kind of RNA-binding proteins (RBPs). And they have been shown to be key regulators of oncogenesis and tumor progression[10]. SMG5 plays a role in the degradation of mRNA through the process of nonsense-mediated decay and encodes a protein. SMG5 has been linked to the development of pancreatic cancer and epiphyseal chondrodysplasia among various illnesses [11]. Which is associated with prognostic effects through mutational actions in HCC [2]. SMG5 may be associated with a poorer prognosis in gastric cancer [12]. According to the study, SMG5 is associated with the infiltration of immune cells such as macrophages, B cells, and T cells in HCC[2].

In this paper, we focus on expressions of TMEM79 and SMG5 in HCC and how they affect the prognostic role in HCC. Next, we investigated the interaction between TMEM79 and SMG5, as well as their expression in HCC patients, their prognostic impact on HCC patients, and functional phenotype analysis.

The expression of TMEM79 in HCC in the TCGA database

To analyze the expression levels of TMEM79 in pan-cancer and determine its mRNA expression profile, TIMER2.0 (https://link.zhihu.com/?target=http%3A//timer.comp-genomics.org/) database was employed. Expression of TMEM79 was found to be increased in 15 types of cancer in pan-cancer tissues, comprising uroepithelial carcinoma of the bladder, breast cancer, bile duct cancer, colon cancer, esophageal cancer, renal clear cell carcinoma, renal papillary cell carcinoma, hepatocellular cancer, lung adenocarcinoma, lung squamous carcinoma, rectal adenocarcinoma, melanoma, gastric cancer, thyroid cancer, and endometrial cancer. On the other hand, the levels of TMEM79 were reduced in three types of cancer, consisting of head and neck squamous cell carcinoma, renal suspicious cell carcinoma, and prostate cancer (Fig. 1A). To investigate the mRNA expression level of TMEM79 in HCC. TCGA database (https://portal.gdc.cancer.gov/repository) was used significantly elevated levels of TMEM79 mRNA expression were observed in HCC compared to normal tissues. TMEM79 mRNA expression levels were elevated in HCC (Fig. 1B, C).

Impact of TMEM79 on survival and prognosis in HCC in databases

Four hundred and twenty-four patients with clinical data were included in the survival analysis and the patients with case HCC were divided into a high TMEM79 expression group and a low TMEM79 expression group according to the median critical value. The survival analysis results indicated that patients in the low expression group had a significantly longer overall survival (OS) compared to those in the high expression group (Fig. 2A). The area under the curve (AUCs) for the TMEM79-expressing group of OS were 0.675, 0.615, and 0.602 at 1 year, 3 years, and 5 years, respectively (Fig. 2B). Univariate and multivariate analysis of TMEM79 showed that TMEM79 was an independent prognostic factor (Fig. 2C, D).

Analysis of TMEM79-related derivatives in the TCGA database

TMEM79-related interacting proteins were analyzed using the string database, followed by further analysis of 10 genes (Fig. 3A). The expression levels of TMEM79-related derived factors in 374 liver cancer patients and 50 normal human liver tissues were examined on the TCGA database (Fig. 3B). The molecules that were differentially expressed in tumor and normal cells by R software analysis were TMEM79, SMG5, NAA35, SLC45A3, FLG, TMEM254. The findings indicated that in tumor tissue when compared to normal tissue, six factors, among them TMEM79, were expressed differently. Prognostic analysis of these six genes showed that NAA35, SMG5, and TMEM79 have a prognostic role in HCC, and the higher the expression, the worse the prognosis (Fig. 3C, D, E). One study found that SMG5 was reported to have a prognostic role in HCC [13]. Therefore, we next analyzed the expression and prognostic value of SMG5 in HCC.

Expression and prognostic value of SMG5 in HCC in TCGA database

mRNA expression of SMG5 in HCC (LIHC) was investigated using the TCGA database. A significantly higher expression level of SMG5 mRNA was observed in LIHC compared to normal tissues (Fig. 4A, B). Survival analysis showed that the OS was significantly longer in the low expression group than that in the high expression group (Fig. 4C). The area under the curve (AUCs) of the SMG5 expression group of OS at 1, 3, and 5 years were 0.727, 0.667, and 0.648 (Fig. 4D).

Expressions of TMEM79 and SMG5, their correlation and their relevance to clinicopathological features in HCC in our research..

We used immunohistochemical methods to detect the expression of TMEM79 and SMG5 in HCC and adjacent normal tissues to validate the previous results. The results showed that TMEM79 and SMG5 were significantly upregulated (Fig. 5, Table 1) in HCC. Correlation analysis showed that TMEM79 and SMG5 played a role in promoting liver cancer progression (p = 0.001, R = 0.200)(Table 2).

Table 1

Expression of TMEM79 and SMG5 in HCC
Characteristics	n	TMEM79		χ2	P value	SMG5		χ2	P value
Characteristics	n	Low or none (%)	High (%)	χ2	P value	Low or none (%)	High (%)	χ2	P value
HCC	282	113(40.1)	169(59.9)	150.025	< 0.001^a	100(35.5)	182(64.5)	89.983	< 0.001^a
Adjacent normal	282	252(89.4)	30(10.6)	150.025	< 0.001^a	212(75.2)	70(24.8)	89.983	< 0.001^a
^aP<0.05 was considered significant. HCC, hepatocellular carcinoma.

Table 2

Relationship between TMEM79 and SMG5 in HCC。
		SMG5		χ2	P value	R
		low or none	high	χ2	P value	R
TMEM79	low or none	53(53.5)	60(33.0)	11.283	0.001	0.200
TMEM79	high	46(46.5)	122(67.0)	11.283	0.001	0.200
^aP<0.05 was considered significant.

Also we then analyzed the clinical information of the TCGA database by R software and the results showed that the higher T grade, the higher expression of TMEM79 in HCC (Fig. 6E). In G1, G2, and G3 staging, TMEM79 was found to be highly expressed in the higher staging (Fig. 6A). Higher staging, higher expression of TMEM79 (Fig. 6D). No statistically significant associations was found between TMEM79 and clinical characteristics such as age, gender, and M and N stages (Fig. 6B, C, F). There were statistically significant differences between SMG5 and stage, grade, and T stage, with higher SMG5 expression at a higher grade (Fig. 7A). Patients with HCC with high SMG5 expression have a higher stage and T-stage (Fig. 7D, E). There were statistically significant differences between SMG5 and age, but no statistical differences with gender, M stage, and N stage (Fig. 7B, C, F).

And the results in IHC showed that TMEM79 was correlated with multiple factors, such as vascular invasion (P = 0.007), T (P = 0.004), M (P < 0.001), and recurrence (P = 0.004) (Table 3). SMG5 exhibited significant associations with tumor number (P = 0.01), tumor differentiation (P = 0.046), T(P = 0.021), M (P = 0.002), and TNM stage (P = 0.001) (Table 3).

Table 3

TMEM79 and SMG5 expression by clinicopathological characteristics of patients with HCC.
Characteristics	n	TMEM79 (%)		χ2	P	SMG5 (%)
Characteristics	n	Low or none	High	χ2	P	Low or none	High	χ2	P
Gender		113(40.1)	169(59.9)	0.016	0.507	99(35.2)	182(64.8)	0.149	0.402
Male	71	28 (39.4)	43 (60.6)			26 (37.1)	44 (62.9)
Female	211	85 (40.3)	126(59.7)			73(34.6)	138 (65.4)
Age (years)		113(40.1)	169(59.9)	0.024	0.493	99(35.2)	182(64.8)	2.959	0.058
< 60	211	84(39.8)	127(60.2)			68(32.4)	142(67.6)
≥ 60	71	29(40.8)	42(59.2)			31(43.7)	40(56.3)
Tumor diameter (cm)		113(40.1)	169(59.9)	1.289	0.156	99(35.2)	182(64.8)	0.743	0.232
< 5	176	66(37.5)	110(62.5)			65(37.1)	110(62.9)
≥ 5	106	47(44.3)	59(55.7)			34(32.1)	72(67.9)
AFP( µg /L)		113(40.1)	169(59.9)	0.025	0.492	99(35.2)	182(64.8)	0.387	0.316
< 400	216	86(39.8)	130(60.2)			74(34.3)	142(65.7)
≥ 400	66	27(40.9)	39(59.1)			25(38.5)	40(61.5)
Tumor number		113(40.1)	169(59.9)	2.654	0.071	99(35.2)	182(64.8)	5.928	0.010
Solitary	243	102(42.0)	141(58.0)			92(38.0)	150(62.0)
Multiple	39	11(28.2)	28(71.8)			7(17.9)	32(82.1)
TNM stage		113(40.1)	169(59.9)	22.021	< 0.001	99(35.2)	182(64.8)	15.986	0.001
I	193	91(47.2)	102(52.8)			77(39.9)	116(60.1)
II	53	20(37.7)	33(62.3)			20(38.5)	32(61.5)
III	15	1(6.7)	14(93.3)			1(6.7)	14(93.3)
IV	21	1(4.8)	20(95.2)			1(4.8)	20(95.2)
T		113(40.1)	169(59.9)	13.464	0.004	99(35.2)	182(64.8)	9.749	0.021
1	193	90(46.6)	103(53.4)			77(39.9)	116(60.1)
2	71	21(29.6)	50(70.4)			21(30.0)	49(70.0)
3	12	2(16.7)	10(83.3)			1(8.3)	11(91.7)
4	6	0(0.0)	6(100.0)			0(0.0)	6(100.0)
N		113(40.1)	169(59.9)	0.395	0.417	99(35.2)	182(64.8)	0.140	0.527
0	275	111(40.4)	164(59.6)			97(35.4)	177(64.6)
1	7	2(28.6)	5(71.4)			2(28.6)	5(71.4)
M		113(40.1)	169(59.9)	11.026	< 0.001	99(35.2)	182(64.8)	8.624	0.002
0	262	112(42.7)	150(57.3)			98(37.5)	163(62.5)
1	20	1(5.0)	19(95.0)			1(5.0)	19(95.0)
Differentiation		113(40.1)	169(59.9)	1.944	0.378	99(35.2)	182(64.8)	4.926	0.085
Well	33	11(33.3)	22(66.7)			17(51.5)	16(48.5)
Moderate	159	61(38.4)	98(61.6)			55(34.8)	103(65.2)
Poor	90	41(45.6)	49(54.4)			27(30.0)	63(70.0)
Hepatitis B virus infection		113(40.1)	169(59.9)	1.210	0.199	99(35.4)	181(64.6)	2.448	0.098
No	18	5(27.8)	13(72.2)			9(52.9)	8(47.1)
Yes	264	108(40.9)	156(59.1)			90(34.2)	173(65.8)
Vascular invasion		113(40.1)	169(59.9)	7.094	0.005	99(35.4)	181(64.6)	0.300	0.354
No	231	101(43.7)	130(56.3)			83(36.1)	147(63.9)
Yes	51	12(23.5)	39(76.5)			16(32.0)	34(68.0)
Liver cirrhosis		113(40.1)	169(59.9)	2.202	0.087	99(35.2)	182(64.8)	0.870	0.210
No	137	61(44.5)	76(55.5)			52(38.0)	85(62.0)
Yes	145	52(35.9)	93(64.1)			47(32.6)	97(67.4)
Recurrence		113(40.1)	169(59.9)	7.667	0.004	99(35.2)	182(64.8)	0.407	0.320
No	230	101(43.9)	129(56.1)			83(36.1)	147(63.9)
Yes	52	12(23.1)	40(76.1)			16(31.4)	35(68.6)
^aP<0.05.

Based on the TCGA database, columnar plots were established to predict the 1-year, 3-year, and 5-year OS for patients with HCC. The parameters used were the final expression level of TMEM79, age, T-stage, N-stage, and clinical stage. The ideal model fits better with calibration curves for OS at 1, 3, and 5 years (Fig. 8A, B).

The Kaplan-Meier analysis in IHC showed that high-expression TMEM79 and SMG5 revealed poorer prognosis of HCC patients (Fig. 9A, B). Patients with HCC have poorer prognosis when TMEM79 and SMG5 were both expressed at higher levels (Fig. 9C). Further univariate and multivariate analysis demonstrated that TMEM79 (HR = 3.825, P < 0.001) and SMG5 (HR = 1.924, P = 0.002) were independent prognostic factors in HCC (Table 4).

Table 4

Univariate and multivariable analysis of survival factors in patients with HCC.
	Univariate analysis				Multivariate analysis
	HR	P>\|z\|	95% CI		HR	P>\|z\|	95% CI
TMEM79 expression	4.552	< 0.001^a	3.047	6.801	3.825	< 0.001	2.515	5.817
High vs low or none
SMG5 expression	2.973	< 0.001	2.003	4.414	1.924	0.002	1.269	2.917
High vs low or none
Age (years)	1.001	0.995	0.694	1.444
≤ 60 vs > 60
Gender	1.183	0.373	0.818	1.712
Male vs Female
Tumor diamater (cm)	1.482	0.015	1.079	2.037	1.388	0.068	0.977	1.972
≤ 3 vs ༞3
AFP( µg /L)	1.091	0.644	0.754	1.579
≤ 400 vs ༞400
Tumor number	0.491	0.001	0.328	0.737	1.014	0.959	0.601	1.710
Solitary vs Multiple
TNM stage	1.878	< 0.001	1.612	2.187	1.470	0.127	0.897	2.409
I vs II vs III + IV
T	2.274	< 0.001	1.839	2.813	1.399	0.119	0.917	2.134
1 vs 2 vs 3 vs 4
N	2.005	0.126	0.822	4.895
0 vs 1
M	4.278	< 0.001	2.590	7.066	0.869	0.780	0.324	2.331
0 vs 1
Differentiation	1.211	0.146	0.936	1.566
Well vs Moderate vs Poor
Hepatitis B virus infection	0.762	0.384	0.412	1.406
No vs Yes
Vascular invasion	1.763	0.003	1.209	2.571	0.370	0.343	0.048	2.885
No vs Yes
Liver Cirrhosis	0.945	0.723	0.689	1.294
No vs Yes
Recurrence	1.812	0.002	1.247	2.633	2.018	0.493	0.271	15.015
No vs Yes
^aP<0.05; HCC, hepatocellular carcinoma; HR, hazard ratio; CI, confidence interval.

Enrichment analysis of TMEM79 in the TCGA database

Based on the RNA sequencing data from the TCGA database, co-expression analysis was performed and a total of 130 genes were found to be significantly associated with TMEM79. TMEM79-related genes were found to be mainly enriched in the nuclear division, mitosis, embryonic organ development, nuclear chromosome segregation, meiosis, microtubule microfilaments, and transport channel proteins. In addition, GSEA analysis showed that immune-related cellular functions were enriched in HCC patients with low TMEM79 expression (Fig. 10A-B). Circos showed co-expression network of TMEM79 and 11 genes (Fig. 10C). Heatmap showed down-regulated genes (blue) and up-regulated genes (red) identified in the high expression group (Fig. 10D).

Correlations of TMEM79 and SMG5 with immune cell infiltration in HCC in the TCGA database

Differential analysis of immune cell infiltration levels between high and low TMEM79 expression groups was performed based on 'CIBERSORT'. TMEM79 showed a noteworthy correlation with the immune cells that infiltrated the tumor. Macrophage and dendritic cell infiltration levels were elevated in the high TMEM79 expression group (Fig. 11A). Macrophage and dendritic cell infiltration levels were positively correlated with TMEM79 (Fig. 11B-D). According to TIMER ( https://cistrome.shinyapps.io/timer/ ), TMEM79 expression levels were positively correlated with CD4 + T cells, macrophages, bone marrow dendritic cells, and neutrophils. In the same way, the analysis of immune cell infiltration was performed for SMG5. High expression of SMG5 was related to B cells, M0 macrophages, and CD8 + T cells (Fig. 12A). Lollipop showed the correlation between immune infiltration and SMG5 (Fig. 12B). The results showed that SMG5 expression was positively correlated with the level of B-cell and M0 macrophage infiltration and negatively correlated with CD8-positive T-cell immune cell infiltration (Fig. 12C-E).

Associations of TMEM79 and SMG5 with immune checkpoints and drug responsiveness in HCC in TCGA database

Initially, we investigated the association between TMEM79 and immune checkpoint inhibitors by analyzing the expression levels of 23 immune checkpoint-related genes along with TMEM79. TMEM79 was significantly correlated with all 11 immune checkpoint-related genes (P < 0.05) [14]. Among them, TMEM79 was negatively correlated with IDO2 and positively correlated with LGALS9, NRP1, CD276, VTCN1, TNFSF4 (Fig. 13A, B). Anti-CTLA4 and anti-PD1 inhibitors also had higher treatment scores, with higher treatment scores associated with better treatment outcomes. Statistical significance was found only in CTLA4-positive PD1-positive sections, suggesting that combined anti-CTLA4 and anti-PD1 therapy is a better option for patients with low TMEM79 expression (Fig. 13C-F). Similarly, SMG5 is associated with 24 immune checkpoints, SMG5 is positively correlated with immune checkpoint action, and has the most pronounced effect with CD275 (Fig. 14A-B). There was no statistically significant difference between SMG5 and drug sensitivity (Fig. 14C-F).

TMEM79-related molecular in HCC in databases

String online analysis of TMEM79-associated reciprocal proteins was performed to further analyze these 10 genes. The expression levels of TMEM79-related derivatives in 374 liver cancer patients and 50 normal human liver tissues were examined on the TCGA datasets. Also, mutation analysis showed that 34 of 371 samples (9.16%) had mutations, with FLG having the highest mutation rate. based on pairwise copy number changes and all 6 genes had frequent copy number changes (Fig. 15A-C).

Figure 15: Analysis of genes associated with TMEM79. A Data on mutation frequency in patients with HCC. B CNV variant loci of associated genes on chromosomes. C Distribution of CNV gains, losses, and non-CNVs in associated genes.

Identification of different subtypes in HCC in databases

A correlation network plot illustrates a robust correlation among 5 genes (Fig. 16A). We next performed consensus clustering analysis to classify HCC samples into different patterns, and two distinct clusters were identified using unsupervised clustering (Fig. 16B). We named these clusters A-B and principal component analysis of cluster showed obvious segregation among two clusters (Fig. 16C). According to Kaplan-Meier survival calculations, subtype B had a better prognosis than subtype A (P < 0.001; Fig. 16D). By KEGG, these clusters were correlated with CELL CYCLE, SPLICEOSOME, UBIQUITIN MEDIATED PROTEOLYSIS and so on (Fig. 16E).

TME infiltration and functional enrichment of different subtypes in the TCGA database

We used gene-set variant analysis enrichment analysis to investigate the possible effects of both isoforms on biological behavior. Subtype A was found to be enriched in pathways related to immune activation when compared to subtype B. According to GSVA enrichment studies, isoform A is considerably enriched in metabolic activation pathways such as folate utilization of a carbon pool, lysine degradation, citrate cycle, RNA metabolism, arachidonic acid metabolism and N-glycan biosynthesis (Fig. 17A, B). In each HCC sample, we used the CIBERSORT approach to assess the association between the two isoforms and 23 other immune cell isoforms in order to learn more about how they work in the tumor microenvironment. According to our study, the number of immune cells infiltrated by both subtypes in CD4 T cells, type 2 T helper cells, regulatory T cells, γ δ T cells, immature dendritic cells and immature B cells were found to be more prevalent in subtype A compared to subtype B (Fig. 17C).

Hypoxia, chronic inflammation, and immunosuppression are the principal determinants that impact TME. The trio works together to create a complicated network of mechanisms essential for tumor growth and development [15]. Tumor-associated immune cells including macrophages, T cells, B cells, natural killer cells, and tumor-associated neutrophils are involved in tumor immune responses, affecting the TME and regulating tumor growth and metastasis [16]. The main body immune system responsible for supervising and killing tumor cells are killer NK cells, and the cells are effector lymphocytes with the ability to generate anti-tumor responses [17]. Major diseases occur when tumors differentiate in a disorderly manner and increase uncontrollably in the body, while the killer cell activity is diminished which cannot effectively remove tumor cells. However, tumor cells can produce immune escape against targeted drugs, such as the well-known PD-L1, which can bind to PD-1 of immune cells, thus causing a decrease in the activity of immune cells and allowing tumor cells to escape the killing effect of immune cells. Key targets like PD-L1 that can inhibit the effect of immune cells are called immune checkpoints. At present, there are several immune checkpoints, including PD-1, CTLA-4, LAG-3, and TIGIT, which have both distinct and shared inhibitory roles in regulating the activation. differentiation, and function of T cells [18]. Therefore, the search for more immune checkpoints is one of the directions to treat tumors.

TMEM79 is a member of the transmembrane protein (TMP) family that encodes for transmembrane protein 79. TMPs play a significant function in cells, serving as transporter proteins and receptors [19]. And TMP plays a crucial role in cells that act primarily as transporter proteins and receptors. TMEM79 (transmembrane protein 79), a protein-coding gene that helps maintain epidermal integrity and skin barrier function, plays a role in the formation of the lamellar granule (LG) secretion system and the stratum corneum (SC) epithelium [20]. TMEM79 may be involved in multiple processes including epithelial cell maturation, establishment of the skin barrier, and positive regulation of epidermal development. TMEM79 acts mainly upstream or within the keratinization which affects the development of the stratum corneum and the morphogenesis of the hair follicle. TMEM79 can interact with ubiquitin-specific protease 8 (USP8)[21], leading to human tumorigenesis. TMEM79 is a potential novel biomarker for BPH [8], and may act as a pivotal factor involved in immune response and tumor cell development in malignant melanoma tumorigenesis [22].

Through previous studies, we found that TMEM79 may play a potential role in promoting HCC, and SMG5 may play a common role in promoting the development of HCC. SMG5 may be associated with memory B cells, M0 macrophages, neutrophils, activated memory CD4 + T cells, follicular helper T cells and regulatory T cells in HCC[2]. SMG5 can be used to predict the prognosis of liver cancer in the current study [23] and may be associated with sex- and race-specific prognostic variability in gastric cancer [12]. SMG5 is involved in nonsense-mediated mRNA decay [24] and enhances the dephosphorylation of UPF1[24]. SMG5 is thought to provide a link to mRNA degradation mechanisms involving the extra nucleoside catabolic pathway and acts as an adapter of UPF1 to protein phosphatase 2A (PP2A), thereby triggering UPF1 dephosphorylation [24].

Targeted therapy of the TME has been considered a very promising anti-cancer strategy [25]. The clinical approval of drugs targeting the vascular system, immune checkpoint inhibitors, and T-cell therapy have benefited many patients [25]. However, because of the complexity and variability of the TME, a single target may not be sufficient to control tumor progression, and the combination of multiple approaches can exert better therapeutic effects[26]. As a result, we must identify additional therapeutic targets. Based on the previously known results, SMG5 plays an important role in HCC. Therefore, we further investigated through what pathway SMG5 affects HCC and whether it could be a potential immune checkpoint for HCC.

In this present study, we analyzed the expression of TMEM79 and SMG5 in HCC. The results showed that both TMEM79 and SMG5 were highly expressed in HCC. Prognostic analysis of TMEM79 and SMG5 suggested that they could act as independent prognostic factors in HCC and affect the prognosis of patients with HCC. Based on the results of TCGA database, we went to verify the expression and prognostic role of both in patients with HCC in our data sample. The results showed that the expressions of TMEM79 and SMG5 were higher in HCC than in adjacent tissues. Patients with TMEM79 and SMG5 high expression of HCC had poor OS. Patients with high TMEM79 and SMG5 expression had higher tumor stage and were more likely to metastasize to distant sites.

Next, we explored the pathways by which the expressions of TMEM79 and SMG5 may affect the prognosis of patients with HCC. TMEM79 were found to be mainly enriched in the nuclear division, mitosis, embryonic organ development, nuclear chromosome segregation, meiosis, microtubule microfilaments, and transport channel proteins. There was a significant correlation between the expression of TMEM79 and tumor-infiltrating immune cells, and a positive correlation between macrophages and dendritic cells. The expression of SMG5 was positively correlated with the level of B-cell and M0 macrophage and negatively correlated with CD8-positive T-cell immune cell infiltration. Expressions of TMEM79 and SMG5 in HCC patients were positively correlated with some immune checkpoints. Drug sensitivity analysis showed a negative correlation between the expression of TMEM79 and nevirapine, angiogenesis inhibitors and sunitinib. Based on protein interaction analysis, we identified several major factors that interacted, namely SLC45A3, NAA35, SMG5, SFTPC, FLG, TNMD, CLEC7A, FNDC4, TMEM254, and VSTM2A. For further study, we further analyzed the TMEM79-related derivatives. SMG5 has been reported to be associated with the prognosis of patients with HCC [2]. SMG5 may be associated with the OS of HCC patients. It could represent a potential drug target and help to optimize future clinical treatment [27].

Potential clinical application of GOLM1-NAA35 chimeric RNA (seG - NchiRNA) in esophageal squamous cell carcinoma (ESCC) [28]. The remaining molecules were not found to be correlated with tumor progression in known studies at this time. Our study identified two distinct molecular isoforms in HCC. Patients with subtype A had more severe clinical features and shorter OS compared to subtype B. Individuals with high expression of NAA35, SMG5, and TMEM79 had a poor prognosis. The effect of gene expression patterns on overall survival in HCC was also investigated. In addition, we compared the characteristics of the two TME subtypes and the changes in immune-related biochemical pathways. Immune activation in the HCC subtype was also largely due to the activation of B cells, CD4 T cells, CD8 T cells, regulatory T cells, mast cells, neutrophils, type 2 T helper cells, and CD56 attenuated natural killer cells. The tumor microenvironment consists of tumor cells and their surrounding cells, such as lymphocytes, tumor-infiltrating immune cells, and tumor vascular system [29]. The above two subtypes are closely related to the abundance of immune cell infiltration in HCC and provide new ideas for tumor immunotherapy and immune infiltration.

TMEM79 is highly expressed in HCC and positively correlated with SMG5, which together affect the development of HCC and the survival prognosis of patients. The occurrence of this process may be related to the effects of both on the tumor microenvironment including the level of immune cell infiltration and immunization checkpoints and drug sensitivity. Notably, TMEM79 may serve as a potential tumor immune marker for immunotherapy in HCC.

Clinical samples in our experiment

We used 320 pairs of HCC tissue clinical specimens tissues from department of Pathology, Affiliated Hospital of Nantong University from January 2013 to December 2019 for immunohistochemical analysis. All patients were diagnosed with primary HCC and did not receive any treatment before surgery. This study was approved by the Ethics Committee of Affiliated Hospital of Nantong University.

Patient Information in databases

The TCGA database provided RNA sequencing information as well as clinical data for the corresponding patients. Clinical characteristics of HCC patients included age, family history, ethnicity, new tumor events, radiation therapy records, history of neoadjuvant therapy, clinical stage, tumor (T), lymph nodes (N), metastasis (M), and gender. Survival data of liver cancer patients was downloaded from the GEO database (GSE 10186 ) (https://www.ncbi.nlm.nih.gov/geo/).

Gene expression differences Analysis

Using R4.2.1 software, we examined the levels of TMEM79 and SMG5 expression in 374 primary HCC tissues and 50 adjacent normal liver tissues taken from patients with varying tumor grades and stages in the TCGA database. We explored the pan-cancer expression levels of TMEM79, and SMG5 between normal and tumor samples using TIMER.

Correlation Analysis

We used the String database (https://string-db.org) to analyze interacting proteins of TMEM79 online. We studied the correlation of TMEM79 with other molecules through the online site cBioPortal.

K-M Survival Curve and Prognostic Analysis

In the TCGA database, 424 HCC patients were categorized into groups based on their expression levels of TMEM79 and SMG5, with a distinction made between those with high and low levels. The optimal cut-off value was determined based on all possible cut-off values between the lower and upper quartiles. Patients diagnosed with HCC were separated into two groups, one with high expression and the other with low expression, according to the median cut-off values. Survival analysis was performed using R4.2.1 to construct K-M survival curves for both groups of patients to assess whether TMEM79 and SMG5 were prognostic factors for OS. The predictive power of TMEM79 for overall survival was evaluated using ROC curves that varied over time, and the statistical software packages "survival", "survivor", and "time ROC" were utilized for this analysis.

Univariate and multivariate analysis of TMEM79 and SMG5 using R software. To establish a prognostic model for predicting OS in HCC, column line plots based on TMEM79 expression level, age, T stage, M stage, N stage, and clinical stage were constructed. Then, calibration curves for 1-year, 3-year, and 5-year survival rates were plotted to verify the consistency of OS data. Furthermore, through both univariate and multivariate analysis, it was confirmed that TMEM79 serves as an autonomous clinical characteristic for predicting OS. The "survival", "regplot" and "rms" packages of R software were used in this procedure.

Functional Phenotype Analysis

Using R4.2.1 software, prognostic molecules associated with TMEM79 were studied. Circos plots were used to demonstrate the strong associations between TMEM79 and certain genetic markers. In addition, differential gene expression analysis was performed for the high and low expression groups to identify DEGs, with thresholds determined as |log FC|>1 and FDR < 0.05. The DEGs were analyzed using KEGG enrichment analysis to uncover the molecular pathways and cellular processes associated with their enrichment. The R packages “limma”, “ggplot2”, “ggpubr”, “ggExtra”, “circlize”, “corrplot” and “pheatmap” were applied in this procedure.

Immuno-infiltration analysis

Using "CIBERSORT", to assess the correlations between TMEM79 and SMG5 and immune cell infiltration in HCC, the expression profile of 22 immune cell subtypes was used to calculate the percentage of tumor-infiltrating immune cells. The differences in the levels of immune cell infiltration between high and low expression of TMEM79 and SMG5 were analyzed, and the results were demonstrated using box plots. Furthermore, scatter plots were utilized to illustrate the correlation between TMEM79 and SMG5 expression levels and immune cell infiltration, as determined by TIMER. Lollipop plots were also utilized to illustrate the association between immune cells and the expression levels of TMEM79 and SMG5. Analysis of the drug sensitivity of TMEM79 and SMG5 to CTLA4 and PD1. R software packages "reshape2", "ggpubr", "vioplot" and “ggExtra” were used in this procedure.

Typing analysis of TMEM79-related molecules in HCC

Consensus Cluster Plus R program was used to classify individuals into discrete molecular clusters based on TMEM79 and their reciprocal gene expression. This has been accomplished by using unsupervised clustering. In a Kaplan-Meier study, the clinical usefulness of the above gene in HCC was investigated using Kaplan-Meier method. We used survival and survivorship packages in R software to examine the survival curves and display the results. Later, principal component analysis was conducted using ggplot2 software. The biological processes of both subtypes were maintained using the gene set variation analysis tool. Malignant tumor tissue using expression and CIBERSORT were also used to represent the percentage of immune and stromal cells in patients with HCC. The extent to which each immune cell carried an enrichment score in each sample was also assessed by gene set enrichment analysis of individual samples.

Immunohistochemistry

A tissue microarray (TMA) containing 320 cases was constructed using a 0.3 mm core. Each section was baked at 70°C for approximately 1 hour and dewaxed, followed by antigen repair with EDTA buffer (pH 9.0) for 20 minutes. After blocking endogenous peroxidase by adding 3%H2O2 for 15 min at room temperature, sections were incubated with TMEM79 rabbit polyclonal antibody (1: 300) at 4 ° C overnight. After incubation with secondary antibody at room temperature for 30 min, the sections were stained with DAB assay. And then after blocking endogenous peroxidase by adding 3%H2O2 for 15 min at room temperature, sections were incubated with SMG5 rabbit polyclonal antibody (1: 150) at 4 ° C overnight. Positive TMEM79 was brown and expressed in the membrane and cytoplasm of tumor cells, SMG5 was expressed in the nucleus. For TMEM79 and SMG5 staining, the intensity was scored as no staining (0), weak staining (1+), moderate staining (2+) or strong staining (3+). The stained areas were classified into four categories based on the percentage 1) 0–25%, 2) 26–50%, 3) 51–75%, and 4) 76–100%. The staining intensity score and the percentage score were multiplied to obtain the final staining index. The analysis was performed independently by two professional pathologists, and high expression was considered when the combined score exceeded 6.

Statistical Analysis

The t-test was employed to establish significant discrepancies in the expression of TMEM79 and SMG5 between HCC tissues and the adjacent normal liver tissues. The survival rates were determined by utilizing Kaplan-Meier survival curve analysis and a log-rank test. Correlation analysis was performed by Pearson's method, and Pearson's correlation coefficient was performed by a two-tailed t-test. A p-value below 0.05 was considered statistically significant.

Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

Not applicable.

Funding

Not applicable.

Contributions

Yu Wang performed database analysis, experimental validation and drafted the manuscript. JQ made additional revisions to the manuscript. Yan Wang and ZS provided guidance for the study. All authors have read and approved the final manuscript.

Ethics declarations

This study was approved by the Ethics committee of Affiliated Hospital of Nantong University. The study is being conducted in accordance with standards of Good Clinical Practice and the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

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No competing interests reported.

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Overexpression of TMEM79 combined with SMG5 is related to prognosis, tumor immune infiltration and drug sensitivity in hepatocellular carcinoma

Status:

Journal Publication

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Results

The expression of TMEM79 in HCC in the TCGA database

Impact of TMEM79 on survival and prognosis in HCC in databases

Analysis of TMEM79-related derivatives in the TCGA database

Expression and prognostic value of SMG5 in HCC in TCGA database

Enrichment analysis of TMEM79 in the TCGA database

Correlations of TMEM79 and SMG5 with immune cell infiltration in HCC in the TCGA database

TMEM79-related molecular in HCC in databases

Identification of different subtypes in HCC in databases

TME infiltration and functional enrichment of different subtypes in the TCGA database

Discussion

Conclusion

Methods

Clinical samples in our experiment

Patient Information in databases

Gene expression differences Analysis

Correlation Analysis

K-M Survival Curve and Prognostic Analysis

Functional Phenotype Analysis

Immuno-infiltration analysis

Typing analysis of TMEM79-related molecules in HCC

Immunohistochemistry

Statistical Analysis

Declarations

References

Additional Declarations

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Journal Publication

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