LncRNA AC008622.2 is an unfavorable prognostic factor of Liver hepatocellular carcinoma: a bioinformatic analysis

DOI: https://doi.org/10.21203/rs.3.rs-1649593/v1

Abstract

Background

Globally, Liver hepatocellular carcinoma (LIHC) is among the most commonly reported cancer type. Despite numerous reports and efforts of this condition, its pathogenesis remains incompletely clear.

Methods

To search for candidate genes that play a role in LIHC carcinogenesis and progression, we analyzed LncRNA AC008622.2 mRNA expression using LIHC data from The Cancer Genome Atlas (TCGA). A chi-square (χ2) test was performed to assess relationships of AC008622.2 mRNA expressions and clinic-pathological characteristics. The diagnostic value of AC008622.2 for LIHC was generated by the receiver operating characteristic (ROC) curve assessment. Cox regression and Kaplan–Meier analyses were analyzed to determine the prognostic significance of AC008622.2 for LIHC outcomes. Interactions between AC008622.2 cells and infiltrated immune cells were analysed via ESTIMATE. Furthermore, a compound-target-lncRNA network was constructed from the TCMSP database.

Results

AC008622.2 mRNA expressions in LIHC tissues were elevated. AC008622.2 demonstrated specific value in LIHC diagnosis. High AC008622.2 levels correlated with histologicalal grade, clinical stage, and T classification of LIHC. Elevated AC008622.2 mRNA levels were associated with worse overall survival, and poor progression-free interval. Furthermore, attest to univariate analysis, AC008622.2 was established to be an independent risk factor for LIHC development. Additionally, there were significantly positive correlations between AC008622.2 expression and infiltrating immune cells, such as NK cells, neutrophils, DCs, cytotoxic cells, CD8 + T cells as well as B cells. Furthermore, a visualization network was constructed and suggested that AC008622.2 has 9 overlapping target genes with Danzhi Xiaoyao Powder.

Conclusions

AC008622.2 is a potential diagnostic as well as prognostic marker for LIHC.

Introduction

Liver hepatocellular carcinoma (LIHC),a prevalent liver tumor, is a leading cause of tumor-associated deaths(1–3). Therefore, searching for new biomarkers are important for early diagnosis of LIHC. Many scholars have conducted in-depth studies on the pathogenesis of liver carcinoma at home and abroad, but the exact cause of its pathogenesis is still unknown(4, 5). LncRNAs are special long-chain non-coding RNA molecules that are commonly expressed in eukaryotic cells and can regulate the expression of many genes by controlling or degrading RNA. LncRNAs play a crucial role in tissue development and embryogenesis, organ development, cell growth, cell differentiation and apoptosis, and the occurrence and progression of various diseases(6–8). Many clinical studies have proven the efficacy of Traditional Chinese Medicine (TCM) in improving the symptoms of advanced liver cancer patients. Danzhi Xiaoyao Powder, a classic TCM treatment, is a proprietary Chinese medicine with detoxification, anti-inflammation, and immune regulation properties(9–11). In this research, RNA sequencing (RNA-seq) data were acquired from the TCGA database and used to evaluate the variations in lncRNA expressions between LIHC and the adjacent liver tissues and to establish prospective genetic markers. Through RNA-seq survival analysis, a prognostic model of AC008622.2 (rarely studied in tumor-related research) was established. We compared AC008622.2 mRNA levels between LIHC and normal tissues. Then, associations between clinical features and AC008622.2 mRNA levels in LIHC patients were assessed. We then explored the potential relationships between AC008622.2 expression and immune infiltration using R. Furthermore, network pharmacology combined with bioinformatic methods was employed to analyze the relationship between AC008622.2 and Danzhi Xiaoyao Powder(12). Our findings imply that AC008622.2 is a potential diagnostic as well as prognostic marker for LIHC that can play a therapeutic role along with Danzhi Xiaoyao Powder.

Methods

Gene expressions and clinical features of the TCGA data

Data from the TCGA database are publicly available and open-source, therefore, ethical approval for their use was waived. TCGA_LIRC DeSeq2 data were retrieved from the TCGA database (https://portal.gdc.cancer.gov/)(13). AC008622.2 mRNA levels, clinic-pathological features as well as general information for LIHC samples were obtained.

Active compounds in Danzhi Xiaoyao Powder

The chemical compounds of Danzhi Xiaoyao Powder were retrieved from the TCM Systems Pharmacology Database (TCMSP, http://tcmspw.com/tcmsp.php), which is an analysis platform for comprehensive studies on the TCM. Active compounds identified by a statistical analysis with OB ≥ 30% and DL ≥ 0.18 were screened from TCMSP for successive research according to the most commonly used criteria. Ultimately, 46 active compounds in Danzhi Xiaoyao Powder were selected.

Venn diagram and lncRNA-hub gene-active ingredient networks

The differentially expressed genes (DEGs) of AC008622.2 were identified by single gene difference analysis and intersected with the target genes of the active ingredients of Danzhi Xiaoyao Powder. Findings are presented in a Veen diagram. A network of the relationships among lncRNAs, target genes and active ingredients was consctructed using the Cytoscape software (Version. 3.6.1).

Statistical analysis

Analyses were done in R. ROC curves were established using the pROC package. Associations between clinical features and AC008622.2 mRNA levels were assessed using the Chi-square test. Then, Kaplan–Meier and multivariate cox analyses were done to assess the correlation of AC008622.2 expression with the prognosis of LIHC. ESTIMATE measures of immune cell infiltrations was done by single-sample gene set enrichment analysis (ssGSEA) according to the established immune signature. P < 0.05 denoted significance.

Results

Clinical features of LIHC patients

Clinical as well as gene expression data for 374 primary cancer and 50 normal samples were downloaded from the TCGA database. They included patient age, histologicalal grade, pathologic stage, vascular invasion, survival status, gender, and T classification (Table 1).

Table 1

Clinical features of LIHC patients

Characteristic

levels

Overall

n

  

374

Age, n (%)

≤ 60

177 (47.5%)
 

> 60

196 (52.5%)

Gender, n (%)

Female

121 (32.4%)
 

Male

253 (67.6%)

histological grade, n (%)

G1

55 (14.9%)
 

G2

178 (48.2%)
 

G3

124 (33.6%)
 

G4

12 (3.3%)

Pathologic stage, n (%)

Stage I

173 (49.4%)
 

Stage II

87 (24.9%)
 

Stage III

85 (24.3%)
 

Stage IV

5 (1.4%)

Vascular invasion, n (%)

No

208 (65.4%)
 

Yes

110 (34.6%)

OS event, n (%)

Death

244 (65.2%)
 

Survival

130 (34.8%)

T stage, n (%)

T1

183 (49.3%)
 

T2

95 (25.6%)
 

T3

80 (21.6%)
 

T4

13 (3.5%)


Elevated AC008622.2 levels in LIHC patients

AC008622.2 transcript levels were analyzed in data from the TCGA database. AC008622.2 mRNA expressions were markedly elevated in LIHC tissues, relative to normal tissues (paired samples, P < 0.001; unpaired samples, P < 0.001). Patients at more advanced histologicalal stages had high AC008622.2 mRNA levels, relative to patients at less advanced histologicalal stages (P < 0.05). High-grade groups (G3/G4) exhibited high AC008622.2 mRNA levels than the low grade groups (G1/G2) (P < 0.001) (Figs. 1 and 2).

Diagnostic significance of AC008622.2 mRNA levels in LIHC

The ROC curve analysis was used to investigate the diagnostic significance of AC008622.2 mRNA levels in LIHC. Area under curve (AUC) for AC008622.2 mRNA expression levels was 0.958. With regards to diagnostic value at varying stages, the findings revealed a comparable diagnostic significance, with AUC values of 0.781, 0.746, 0.723 and 0.952 for stages I, II, III and IV, respectively (Fig. 3).

Association between clinical features and AC008622.2 mRNA levels in LIHC

Based on median AC008622.2 mRNA levels, samples were assigned into high and low groups. Elevated AC008622.2 mRNA levels correlated with histologicalal stage (P < 0.001) (Table 2).

Table 2

Relationship between AC008622.2 mRNA levels and clinical features in liver cancer

Characteristic

Low expression of AC008622.2

High expression of AC008622.2

p

Age, n (%)

    

0.277

<=60

83 (22.3%)

94 (25.2%)

  

> 60

104 (27.9%)

92 (24.7%)

  

Gender, n (%)

    

0.377

Female

56 (15%)

65 (17.4%)

  

Male

131 (35%)

122 (32.6%)

  

histological grade, n (%)

    

< 0.001

G1

37 (10%)

18 (4.9%)

  

G2

95 (25.7%)

83 (22.5%)

  

G3

48 (13%)

76 (20.6%)

  

G4

3 (0.8%)

9 (2.4%)

  

Pathologic stage, n (%)

    

0.065

Stage I

98 (28%)

75 (21.4%)

  

Stage II

38 (10.9%)

49 (14%)

  

Stage III

38 (10.9%)

47 (13.4%)

  

Stage IV

1 (0.3%)

4 (1.1%)

  

Vascular invasion, n (%)

    

0.063

No

115 (36.2%)

93 (29.2%)

  

Yes

48 (15.1%)

62 (19.5%)

  

T stage, n (%)

    

0.131

T1

102 (27.5%)

81 (21.8%)

  

T2

40 (10.8%)

55 (14.8%)

  

T3

36 (9.7%)

44 (11.9%)

  

T4

6 (1.6%)

7 (1.9%)

  

OS event, n (%)

    

0.002

Alive

137 (36.6%)

107 (28.6%)

  

Dead

50 (13.4%)

80 (21.4%)

  

 

High AC008622.2 mRNA levels is an independent risk factor for OS outcomes in LIHC patients

Kaplan–Meier analysis showed that elevated AC008622.2 mRNA levels were associated with poor OS (P < 0.001), shorter progress free interval (P < 0.001) and poor disease specific survival (P < 0.05) (Figs. 4 and 5). Subgroup analysis revealed that AC008622.2 mRNA levels markedly affected OS outcomes in LIHC cases of G1/G3 (P = 0.001), M0 (P < 0.001), T1/T2 (P = 0.001), T1/T3 (P < 0.001), clinical stage I/III (P < 0.001), and II/III (P < 0.001) (Fig. 5).

Univariate analysis showed that elevated AC008622.2 mRNA levels, T classification, and advanced stage correlated with OS outcomes. Multivariate analysis showed that AC008622.2 mRNA levels are independent risk factors for OS outcomes in LIHC (Table 3).

Table 3

Association between mRNA expressions of AC008622.2 and overall survival

Characteristics

Total(N)

Univariate analysis

Multivariate analysis

Hazard ratio (95% CI)

P value

Hazard ratio (95% CI)

P value

Gender

373

        

Male

252

Reference

      

Female

121

1.261 (0.885–1.796)

0.200

    

Age

373

        

<=60

177

Reference

      

> 60

196

1.205 (0.850–1.708)

0.295

    

T stage

370

        

T1

183

Reference

      

T2

94

1.431 (0.902–2.268)

0.128

0.000 (0.000-Inf)

0.994

T3

80

2.674 (1.761–4.060)

< 0.001

0.865 (0.118–6.326)

0.887

T4

13

5.386 (2.690-10.784)

< 0.001

1.740 (0.199–15.200)

0.616

Pathologic stage

349

        

Stage I

173

Reference

      

Stage II

86

1.417 (0.868–2.312)

0.164

4267218.741 (0.000-Inf)

0.994

Stage III

85

2.734 (1.792–4.172)

< 0.001

2.964 (0.406–21.644)

0.284

Stage IV

5

5.597 (1.726–18.148)

0.004

3.815 (0.340-42.846)

0.278

histological grade

368

        

G1

55

Reference

      

G2

178

1.162 (0.686–1.969)

0.576

    

G3

123

1.185 (0.683–2.057)

0.545

    

G4

12

1.681 (0.621–4.549)

0.307

    

Vascular invasion

317

        

No

208

Reference

      

Yes

109

1.344 (0.887–2.035)

0.163

    

Fibrosis ishak score

214

        

0

75

Reference

      

1/2

31

0.935 (0.437–2.002)

0.864

    

3/4

28

0.698 (0.288–1.695)

0.428

    

5/6

80

0.737 (0.410–1.325)

0.308

    

Adjacent hepatic tissue inflammation

236

        

None

118

Reference

      

Mild

101

1.204 (0.723–2.007)

0.476

    

Severe

17

1.144 (0.447–2.930)

0.779

    

AC008622 2

373

4.559 (2.065–10.068)

< 0.001

4.274 (1.885–9.689)

< 0.001

 

Relationships Between AC008622.2 levels and Immune Markers

Tumor infiltration is associated with LIHC prognosis[10]. Therefore, we tested whether the transcription levels of AC008622.2 in LIHC were correlated with immune infiltration. ESTIMATE can reveal infiltration level of immune cells by conducting single-sample GSEA (ssGSEA) according to the established immune signature, and low p-values correlate with high total infiltrations. These biomarkers were used for immune cell characterization, including B cells, T helper cells, macrophages, CD8 T cells, cytotoxic cells, NK cells, neutrophils and DCs in LIHC. The results showed that AC008622.2 was very weakly negatively correlated with counts of B cells, cytotoxic cells, NK cells, neutrophils, CD8 T cells, and DCs. AC008622.2 expressions were weakly associated with immune infiltrations of B cells and macrophages (Fig. 6).

Target Recognition Results

We obtained 46 potential target genes from the TCM database by analyzing 96 active ingredients in Danzhi Xiaoyao Powder, and all the target names were corrected to the gene names of the targets. A total of 1525 differentially expressed genes (DEGs) related to AC008622.2 were analysed using the DESeq2 program. These potential target genes and 1525 differentially expressed genes intersected, and 9 potential LIHC-related genes were identified by the intersection of a Venn diagram. Cytoscape software was used to develop a visualization network, which helped to understand the relationship between lncRNAs, potential targets and Danzhi Xiaoyao Powder (Fig. 7).

Discussion

AC008622.2 has rarely been included in tumor related studies, especially in LIHC. Zhang et al. evaluated the prognostic value and found that AC008622.2 was an independent prognostic predictor for LIHC(14). Our findings confirmed that AC008622.2 mRNA levels were markedly high in LIHC tissues, relative to normal tissues. Elevated AC008622.2 mRNA levels were correlated with histological grade. In addition, high AC008622.2 mRNA levels correlated with poor overall survival outcomes. Finally, AC008622.2 is an independent prognostic factor for LIHC.

The AC008622.2 mRNA levels markedly correlated with poor overall survival, poor progress free interval and poor disease specific survival. Histologicalal grade was highly associated with AC008622.2 mRNA levels.

We evaluated the correlations between AC008622.2 levels and immune infiltrations(15). AC008622.2 levels and B cells as well as macrophages were positively correlated in LIHC. This finding reveals that AC008622.2 might be a potential prognostic immune-related gene that can reduce the aggregation of many immune cells, e.g., B cells and macrophages.

Network-based pharmacology is beneficial in analyzing the “drug-component-target disease” interaction network, since it can systematically identify the association between drugs and diseases and reveal the advantages of multi-molecule drug synergy(16). Danzhi Xiaoyao Powder is a Chinese medicine for liver soothing, spleen strengthening, qi regulation and reducing stagnation(17, 18). To date, no study has examined the relationship between AC008622.2 and Danzhi Xiaoyao Powder. Network pharmacology is beneficial for assessing “drug-component-target disease” interaction network, as it can establish the relationship between diseases and drugs as well as show the benefits of multimolecular drug synergy. In our study, 9 potential LIHC related genes (AR, ADRA1B, CAMKV, HTR3A, MMP2, AKR1B10, PI3, ABCG2, and ADRB2) of Danzhi Xiaoyao Powder were identified by examing the intersection with the AC008622.2 visualization network that was constructed to show the relations between lncRNAs, target genes and active ingredients.

Overall, the results demonstrate that AC008622.2 expression may serve as a diagnostic and prognostic marker in LIHC and it might interact with Danzhi Xiaoyao Powder to provide novel methods for treatment of liver cancer. However, we conducted only bioinformatics analyses, our findings should be confirmed in clinical samples.

Conclusions

AC008622.2 mRNA levels are elevated in LIHC tissues, and they correlate with poor overall survival outcomes for LIHC patients. Moreover, AC008622.2 mRNA levels were established to be an independent prognostic factor for LIHC, making it a potential marker in the future.

Declarations

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shengkai Wang, Jingjing Liu and Mengjie Gu. The first draft of the manuscript was written by Shengkai Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval

This is an observational study. The Hangzhou TCM Hospital Research Ethics Committee has confirmed that no ethical approval is required.

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