Identification of a Pyroptosis-Related LncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-1593580/v1

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Identification of a Pyroptosis-Related LncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

https://doi.org/10.21203/rs.3.rs-1593580/v1

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Background: Liver cancer is considered one of the prevalent malignancies, of which most are hepatocellular carcinoma (HCC). Pyroptosis is a form of inflammation that can mediate cellular programmed death. Long non-coding RNA (lncRNA) is one of the non-coding RNAs which performs powerful biological functions, and they exert an irreplaceable role in the regulation of many biological processes. However, pyroptosis-related lncRNAs expression and their correlation to the HCC patient prognosis remain to be investigated.

Methods: Using The Cancer Genome Atlas (TCGA) database, we built a pyroptosis-related multi-lncRNA signature to predict the HCC patient prognosis. The least absolute shrinkage and selection operator (LASSO) Cox regression method was used to construct a pyroptosis-associated lncRNA signature to classify all HCC patients into high- and low-risk groups.

Results: The low-risk group patients have a better prognosis. We identified the risk score as an independently predictor in HCC patients. Furthermore, chemotherapeutic drug sensitivity (gemcitabine, doxorubicin, mitomycin C and sorafenib), and immune cell expression (NK cells resting, Mast cells resting, M0 macrophages, Monocytes and CD8 T cells) showed significantly different infiltration degree between the two groups. We identified SC-5608, anisomycin and vancomycin as possible potential drug targets for the high-risk group patients. We performed enrichment analysis for the differential genes in the two groups of patients. Intriguingly, the result showed that targeting the wnt/β-catenin signal pathway could help to develop treatment strategies for patients with HCC.

Conclusions: In conclusively, we found a signature of pyroptosis-related lncRNA for predicting independent HCC patient prognosis. The study provides new insights for reliably and accurately predicting prognosis and developing therapeutic strategies for HCC patients.

hepatocellular carcinoma

pyroptosis

lncRNA

TCGA

wnt/β-catenin

prognosis

HCC is the sixth of the most prevalent cancers as well as the fourth leading contributor to cancer fatalities [1]. Seriousness of liver malfunction, the extent of tumor growth and general health of the patient and have been identified as key predictors of HCC prognosis. Treatments including transarterial chemoembolization, ablation, liver transplantation and surgical resection improve the prognosis of HCC patients [2, 3], but the prognosis for HCC patients is poor since the high rate of recurrence after surgery. Currently, many methods, such as the Italian Liver Cancer Program (CLIP), BCLC and TNM staging system have been applied to assess the HCC patient prognosis [4]. Nevertheless, the evaluation criteria depend on clinical features such as patient gender, age, clinical stage, and pathological grading, and lack crucial and complicated molecular pathogenesis significantly associated with clinical outcomes [5]. Therefore, in order to improve the accuracy of patient prognosis prediction, we need to explore new valid molecular markers hence improving the HCC patient prognosis.

Pyroptosis is a caspase-dependent type of cellular programmed death [6]. Caspase-1/4/5/11 are key factors that cause pyroptosis and are triggered by particular inflammatory factors. For example, inflammasomes can directly activate caspase-1 and lead to the cleavage of proIL-18, proIL-1β and GSDMD, then IL-1β and IL-18 are secreted to the external environment [6, 7]. Pyroptosis plays an imperative role in inhibiting hepatocellular carcinoma cells. It was shown that in a mouse hepatocellular carcinoma model, sorafenib induced upregulation of Caspase-1 in MΦ cells, which increased the interaction between MΦ and NK cells thereby promoting the inhibition of hepatocellular carcinoma cells [8]. In addition, NLRP3 inflammatory vesicle activation induced by HBx (Hepatitis B virus X) [9] and 17β-estradiol [10] can promote cell death, correlating with the activation of IL-1β, IL-18 and caspase-1.

Typically, the length of lncRNA exceeds 200 nucleotides [11]. 68% of human cellular transcription products are categorized as lncRNA, and they play an unsubstitutable role in many biological processes [12]. A wide range of HCC-associated lncRNAs have been proven to be aberrantly expressed and can be involved in the oncogenic phenotype (sustained proliferation, evasion of apoptosis, accelerated angiogenesis and acquisition of invasive capacity) by binding to DNA, RNA, proteins and encoded small peptides [13]. Moreover, lncRNA also plays an important function in pyroptosis-induced HCC cell death [14].

Overall, we build a lncRNA signature associated with pyroptosis and validate the accuracy of our model. This not only points the way to understand the signaling pathways associated with pyroptosis-related lncRNA in HCC, but also helps to accurately predict the HCC patient prognosis and thus identify therapeutic targets and biomarkers.

Download data and identify pyroptosis-related lncRNAs

HCC patient data was downloaded from TCGA. The dataset contains a total of 50 non-tumor samples as well as 374 tumor samples. The ensembl human genome browser GRCh38.p12 was applied to differentiate lncRNAs from protein-coding genes. According to a previous review, 33 pyroptosis-related genes were extracted [15-18]. Applying Pearson correlation analysis to identify lncRNAs correlating with pyroptosis by Pearson correlation coefficient absolute value > 0.4 (|R| > 0.4) and p-value < 0.001 (p < 0.001). Differentially expressing lncRNAs associated with pyroptosis were identified using the limma package by logFC > 2 and p-value < 0.05.

Building and validating prognostic pyroptosis-associated lncRNA signature

We randomly assigned 370 HCC patients into 2 groups, and the training set (n=177) as well as testing set (n=166) of HCC patients were obtained after excluding patients who survival less than 30 days. Pyroptosis-related lncRNA signature was built by univariate as well as LASSO Cox regression analysis in the training set. The risk score was obtained by the sum of each pyroptosis-related lncRNA expression level multiplied by the corresponding coefficient. In the training set, we grouped patients by the median of the calculated risk scores. We use the same grouping method for the testing set. Our model was validated based on Kaplan-Meier survival analysis and ROC curve.

Independent prognostic analysis of the signature

A nomogram was developed to predict the occurrence of HCC. In the training and testing sets, we depicted the risk score distribution, survival status and lncRNA expression heatmap. We applied uni- and multi-Cox regression analyses to assess the relevance of the signature to the HCC patient prognosis.

Immune cell Infiltration and drug responsiveness

The level of immune cell infiltration in each HCC sample was obtained by CIBERSORT (https://cibersort.stanford.edu/index.php). The relationship of immune cell expression levels between the two groups was determined using the R software, and heatmap, histogram and violin plot was generated for visualization. Applying pRRophetic package to predict drug sensitivity by estimating IC 50 of the samples using ridge regression. Differential expressed gene (DEGs) between the two groups were identified by logFC > 2 and p-value < 0.05. We screened small molecule drugs by the CMAP database (https://portals.broadinstitute.org/cmap).

Gene set enrichment analysis (GSEA)

GSEA was conducted according to DEGs between the two groups. GO and KEGG enrichment pathways for differential genes were obtained using Metascape (https://metascape.org/). Enrichment pathways were screened based on p-values less than 0.01, overlap greater than 3 and minimum enrichment degree greater than 1.5. In addition, PPI network maps of the enrichment pathways were obtained.

Somatic variants analysis

Mutation data was also downloaded from TCGA. We used the maftools package to analyze the somatic mutation data from the training set and visualize the results.

Identification of prognostically relevant pyroptosis -associated lncRNAs

Figure 1 illustrates the flow chart of our research program. We first screened 33 pyroptosis-related genes and extracted the gene expression data in TCGA-LIHC database. Then, we used Pearson correlation analysis to identify 785 lncRNAs associated with pyroptosis. Subsequently, we identified 172 differentially expressed lncRNAs associated with pyroptosis in tumor samples and non-tumor samples. We used univariate Cox regression to screen 15 lncRNAs associated with prognosis (Figure 2), and they were all associated with poor prognosis (HR>1).

Establishment and verification of pyroptosis-related lncRNA prognostic signature for HCC

In the training set, four prognostic lncRNAs (AL357079.1, SNHG4, AL163953.1 and AL442125.2) associated with pyroptosis were identified from the 15 lncRNAs characterized above to build a model for predicting HCC prognosis. Risk score formula is as follows: Risk score = AL357079.1 expression * 0.090679 + SNHG4 expression * 0.332627 + AL163953.1 expression * 0.045756 + AL442125.2 expression * 0.777904, where the coefficients for each lncRNA were obtained from the LASSO regression model.

The high-risk group had greater expression of four pyroptosis-related lncRNAs, poorer patient living conditions (Figure 3A), and significantly shorter OS (Figure 4A). We use the same grouping method in the testing set and the findings are similar to the training set (Figure 3B, Figure 4B). Notably, the AUC values of the ROC curves for the training and testing sets OS were 0.746 (Figure 4C) and 0.740 (Figure 4D) respectively, indicating good specificity and sensitivity of the constructed signature.

Independent prognostic analysis of the pyroptosis-related lncRNA signature

Next, we performed uni-Cox and multi-Cox regression analyses on the constructed signature. Uni-Cox regression analysis identified Stage, T classification and risk score as independently factors of HCC prognosis (Figure 5A-B). In contrast, multi-Cox regression analysis identified only the risk score as an independently factor for HCC prognosis (Figure 5C-D).

Based on the clinicopathological and identified model, we constructed a nomogram (C-index: 0.71) to predict HCC patient prognosis at 1, 2, and 3 years (Figure 5E).

Differences in gene mutation and immune infiltration

Somatic cell variant analysis revealed the top 20 mutated genes and the top five were TP53, CTNNB1, TTN, MUC16 and MUC4. The most frequent type of mutation was missense mutation. Interestingly, we found that TP53 was more prone to mutation in the high-risk group (Figure 6).

The composition of immune cell subpopulations is associated with the anti-tumor effect of immunotherapy in the tumor microenvironment. We plotted immune cell histogram and heatmap of HCC patients. The result showed that M0, M1, M2, CD8T and CD4T resting cells significantly infiltrated in hepatocellular carcinoma cell (Figure 7A). Moreover, we found that M0 macrophage was correlated significantly and negatively with CD8T cell (Figure 7B). The macrophages M0 infiltration in the high-risk group was significantly increased. Conversely, the proportion of Monocytes, Mast cells resting, T cells CD8 as well as NK cells resting infiltration in the low-risk group was significantly increased (Figure 7C).

Differences in the response to chemotherapy and small molecule drugs

Because of the survival differences between the two groups, we hypothesized that their sensitivity to chemotherapeutic agents was different. Therefore, we further analyzed the different outcomes of the two groups of HCC patients in terms of response to chemotherapy (Figure 8A). Low-risk group patients had lower 50% inhibitory concentrations (IC50) and greater drug sensitivity to gemcitabine, Adriamycin and mitomycin C. In contrast, high-risk group patients were more sensitive to sorafenib. In conclusion, the signature we constructed provides direction for the development of chemotherapy strategies for HCC patients.

We screened 395 DEGs between the two groups according to the prognostic model and analyzed them using the CMAP database. Eight small molecule drugs, including SC-560, anisomycin and vancomycin, were screened, suggesting that they may be potential drug targets for the high-risk group patients (Figure 8B).

Identification of biological pathways of pyroptosis-related lncRNA signature

We show 20 GO-enriched pathways and 10 KEGG-enriched pathways (Figure 9A, B). The most enriched pathway for GO analysis was cell-cell adhesion, and the most enriched pathway for KEGG analysis was calcium signaling pathway. In addition, pathways including epithelial cell differentiation, cell adhesion molecules, and gap junction also showed different levels of enrichment. Based on these enriched pathways, we identified seven PPI functional groups and characterized the pathway for each functional group (Figure 9C). Wnt/β-catenin signaling pathway plays an essential role in MCODE1 (Figure 9D).

Until now, the pathogenesis of HCC is unclear, leading to a lack of effective treatments. We integrate multiple biomarkers into a single model to improve the accuracy of prediction and help develop personalized treatment plans.

As an intrinsic inflammatory process, pyroptosis has a crucial role in release of proinflammatory cytokines and cell lysis when the host resists infection [6]. Recently studies have reported the involvement of pyroptosis in HCC. As a cellular expression product of the innate immune system, NLRP3 consists of three main structural domains: NACHT domain, leucine-rich repeats (LRRs), and a pyranine domain (PYD) [19, 20]. As previously described, HBx and E2 promote hepatocyte pyroptosis under oxidative stress via NLRP3 [9, 10]. Interestingly, SNHG7 inhibited miR-34a expression thereby enhancing SIRT1 expression and suppressing NLRP3-dependent pyroptosis [14]. In addition, it was shown that metformin inhibits the development of HCC by inducing FOXO3-induced apoptosis and pyroptosis [21]. These studies suggest that pyroptosis may be one of the new targets for the treatment of HCC.

Among the identified lncRNAs, AL357079.1, SNHG4, AL163953.1 and AL442125.2 were associated with tumor progression. The association of SNHG4 with hepatocellular carcinoma was reported in studies [22, 23]. SNHG4 is upregulated in hepatocellular carcinoma. Patients with low SNHG4 expression were associated with better OS and RFS. Furthermore, multivariate analysis showed that SNHG4 was an independently factor for poor prognosis in HCC patients. This is also consistent with our findings. However, studies on the prognostic value and relationship with pyroptosis of four lncRNAs (AL357079.1, SNHG4, AL163953.1, and AL442125.2) in hepatocellular carcinoma are still insufficient. Therefore, exploring the mechanisms of these several lncRNAs in HCC may yield interesting results.

Our model successfully identified differences in chemotherapy drug sensitivity among different subgroups of HCC patients. In several clinical trials, gemcitabine in combination with other chemotherapeutic drugs has shown good therapeutic effects in the HCC treatment [24–26]. Lactosaminylated doxorubicin albumin couples accumulate in the liver in a rapid and accurate manner [27]. A54 peptide-modified doxorubicin glycolipid adduct micelles also show high selectivity for HCC cells [28]. Moreover, studies have shown that mitomycin C combined with chemoembolization of hepatocellular carcinoma is the treatment of choice to obtain local control and better PFS [29]. Sorafenib is a kinase inhibitor with multiple targets and has anti-angiogenic and cytotoxic effects [30]. Also, Sorafenib promotes the inhibitory effect of NK cells on HCC by inducing MΦ cell pyroptosis [8]. This provides new insights when selecting chemotherapeutic agents for the therapy of HCC patients in different risk groups.

We identified eight small molecule target drugs by CMAP database. As a COX-1 inhibitor, SC-560 increased ERK1/2 phosphorylation exerting antitumor activity in hepatocellular carcinoma cells [31]. Interestingly, SC-560 induces apoptosis by decreasing XIAP and survivin levels and activating caspase-3 and caspase-7 in hepatocellular carcinoma cells [32]. Anisomycin was an antibiotic compound derived from gray streptomycin, and treatment of hepatocellular carcinoma cells with anisomycin resulted in increased CD58 expression level recognized by NK cells, thereby enhancing the effects of NK cell-mediated immunotherapy [33]. A case report showed that HCC patients with hepatic encephalopathy could improve hepatic functional reserve with oral vancomycin [34]. In a mouse model of HCC, a selective absence of intestinal flora in mice can be observed after vancomycin treatment, and this remodeling of flora can effectively prevent the development of hepatocellular carcinoma [35]. However, more experiments are also required to elucidate the role played by other drugs in hepatocellular carcinoma.

Somatic cell variant analysis revealed the top 20 mutated genes, with the most frequently mutated genes being TP53 and CTNNB1, affecting 25–30% of HCC patients [36]. Furthermore, studies have shown that oncogene-activated hepatocytes secrete CCL2 to attract macrophages. Macrophages act synergistically with T lymphocytes to remove senescent hepatocytes, thereby halting the progression of HCC [37, 38]. Conversely, recruitment of macrophage in established tumor model causes the accumulation of immunosuppressive tumor-related macrophages, which accelerate tumor growth via suppressing NK-cell and CD8 T-cell activation. Consistently, in the current study, there was a significant decrease in Monocytes, NK cells resting, Mast cells resting and CD8 T-cell infiltration and a significant increase in the proportion of M0 macrophages with increasing risk score. The interaction between the tumor immune microenvironment and pyroptosis-associated lncRNAs requires further studies to elucidate. In conclusion, the relationship between tumor pyroptosis-related lncRNAs and immune infiltration implies that modulating innate and acquired immune cell infiltration by targeting tumor pyroptosis-associated lncRNAs is a potential new strategy to enhance anti-tumor immunotherapy in liver cancer.

We found that calcium signaling pathway, intercellular adhesion, epithelial cell differentiation, cell adhesion molecules and gap junction are closely related to invasion, migration and metastasis of hepatocellular carcinoma [39–42]. Interestingly, MCODE1 enriches three pathways, including cell-cell signaling by wnt, wnt signaling pathway and cell surface receptor signaling pathway involved in cell-cell signaling. Wnt/β-catenin signaling pathway is considered to be a pro-oncogenic factor in many tumors including hepatocellular carcinoma [43]. β-catenin is considered to be a multifunctional protein, encoded by the human CTNNB1. Our study showed that the second most mutated in HCC patients is CTNNB1, and CTNNB1 gene mutation and overexpression contribute to hepatocellular carcinoma progression. In addition, β-catenin prevents apoptosis and enhances HCC cell migration by inducing EMT and upregulating MMP levels [44]. Clinical studies have confirmed the role of CTNNB1 mutations and β-catenin in HCC progression [45, 46]. Therefore, targeting the wnt/β-catenin signaling pathway may benefit HCC patients.

In this study, we integrated biomarkers of four lncRNAs associated with pyroptosis for the construction of a signature to assess HCC patient prognosis and to reveal drug targets, immune microenvironmental differences and specific signaling pathways in HCC. However, our model lacks more independent cohort studies and experiments to predict the function of pyroptosis-related lncRNAs. There are also several limitations in our study: our results have not been validated by basic and clinical trials, and the small sample size leads to insufficient data, which limits the accuracy of our model.

we build a new pyroptosis-associated lncRNA signature to predict HCC patient prognosis. This study offers novel perspectives for the development of credible and precise cancer prognostic prediction tools.

HCC, hepatocellular carcinoma; LncRNA, long non-coding RNA; TCGA, The Cancer Genome Atlas; LASSO, the least absolute shrinkage and selection operator; CLIP, Italian Liver Cancer Program; BCLC, the Barcelona Clinical Liver Cancer; HBx, Hepatitis B virus X; DEGs, Differential expressed genes. GSEA, Gene set enrichment analysis; GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; IC50, 50% inhibitory concentrations.

Ethics approval and consent to participate

Our study has been approved by the medical ethics committee of The First Affiliated Hospital of Sun Yat-Sen University. All the methods performed in studies were in accordance with the ethical standards of the The First Affiliated Hospital of Sun Yat-Sen University and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Consent for publication

All authors have seen the manuscript and approved of publication.

Availability of data and materials

HCC patient data was downloaded from TCGA gdc (https://portal.gdc.cancer.gov/). The level of immune cell infiltration in each HCC sample was obtained by CIBERSORT (https://cibersort.stanford.edu/index.php). GO and KEGG enrichment pathways for differential genes were obtained using Metascape (https://metascape.org/).

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

It is not applicable for that section.

Authors’ contributions

KZ and WH conceived and designed the study. ZO and HL performed the data collection. QL, LC and CT performed the data analysis. WH and QL drafted the manuscript. KZ revised the manuscript. All authors contributed to the manuscript and approved the submitted version.

Acknowledgements

We acknowledge TCGA database for providing their platforms and contributors for uploading their meaningful datasets.

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No competing interests reported.

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Identification of a Pyroptosis-Related LncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

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