The Risk Signature of Pyroptosis-Related Long Noncoding RNAs Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular carcinoma

doi:10.21203/rs.3.rs-4065411/v1

Download PDF

Research Article

The Risk Signature of Pyroptosis-Related Long Noncoding RNAs Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-4065411/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Pyroptosis can cause inflammasome activation through Caspase-1/NOD-like receptor 3 (NLRP3) pathways, which is closely related to hepatocellular carcinoma (HCC). The immunotherapy of HCC has a good overall effect and has been widely used in clinical, but the exact effect varies from person to person. Dysregulation of lncRNA can lead to inflammatory and abnormal immune microenvironment in HCC. However, few studies have reported the role for pyroptosis-related lncRNAs (PRlncRNAs) in HCC. In this study, we constructed and validated a risk model based on 8 PRlncRNAs to predict the prognosis of patients and immune cell infiltration in HCC using bioinformatics approaches and experiments. Our risk model showed that patients in a high-risk group exhibited a poor prognosis and risk score is an independent prognostic factor. AUC analysis proved that our risk signature more accurately predicted the prognosis of patients than traditional clinicopathological indexes. The results of GSEA and (ss)GSEA showed that high-risk group genes were mainly enriched in immune pathways and that their executive function depended on inhibitory immune checkpoints. Furthermore, downregulation of AC009283.1 expression accelerated the HCC cells progression, reduced NLRP3/Caspase-1 expression and infiltration of CD3. While si-LINC00942 got the opposite results. Overall, the novel signature has a high credibility in predicting the patient prognosis and evaluating the immune status, which has important significance for guiding immunotherapy and developing precise treatments to benefit HCC patients.

hepatocellular carcinoma

lncRNA

pyroptosis

tumour immunity

Hepatocellular carcinoma (HCC) is a common pathological pattern of primary liver cancer and ranks fourth among deadly tumors worldwide[1, 2]. The majority of HCC patients are diagnosed at a late stage, after the best treatment options are no longer available[3–5]. Immunotherapy has been clinically applied, but it has an efficacy of ~ 20% and the most population showing no sensitivity[6, 7]. How to distinguish patients who can benefit of immunotherapy and expand the groups are challenges facing researchers. Therefore, establishing a signature to predict HCC prognosis and immune infiltration is greatly beneficial for the diagnosis and therapy of HCC.

Pyroptosis is a special kind of programmed cell death[8–10]. The classical pyroptosis pathway is activated by inflammasomes which relies on Caspase-1 and NLRP3[11, 12]. Studies have shown that pyroptosis exhibits antitumor properties against HCC[13–15]. Berberine promotes pyroptosis by activating Caspase-1, thereby inhibiting HCC cell activity[16]. The pyroptosis-related genes (PRGs) directly regulate HCC progression and their prognostic roles have not been adequately reported.

LncRNA, a new long non-coding RNA, can participate in the regulation of tumorigenesis and immune progression[17, 18]. Notably, SNHG7 inhibited the expression of NLRP3/Caspase-1 and then suppressed pyroptosis in HCC [19]. There are few reports on the relationship between lncRNA's direct regulation of pyroptosis and immune infiltration in HCC.

In this study, we constructed a risk signature based on 8 PRlncRNAs, analyzed and verified its value for evaluating the prognosis and tumor immune infiltration of HCC patients by using bioinformatic approaches. We further select AC009283.1 and LINC00942 to examined the effects on pyroptosis and immune infiltration, thus predicted the patient survival and sensitivity to immunotherapy in HCC.

Data acquisition

We acquired RNA-sequencing data, the clinical information of 966 HCC patients and lncRNA expression profiles from The Cancer Genome Atlas (TCGA) database[18]. The disease-free survival (DFS) data were acquired from the cBioPortal database. In addition, PRGs were obtained from GeneCards[20].

GO and KEGG Analysis of PR-DEGs

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the function of pyroptosis-related differentially expressed genes (PR-DEGs)[21].

Construction of a Risk Signature

We evaluated the relationship between PR-DEGs and lncRNAs with the R package limma, and constructed a signature of 8 PRlncRNAs by univariate and multivariate Cox analyses. The formula to calculate each patient's risk score was as follows:

$$\text{R}\text{i}\text{s}\text{k} \text{s}\text{c}\text{o}\text{r}\text{e}=\sum _{i=1}^{n}(\text{C}\text{o}\text{e}{f}_{i}\times {x}_{i})$$

where Coe${f}_{ }$ and ${x}_{ }$ are the regression coefficients and PRlncRNA expression values, respectively[22].

Functional Enrichment Analysis of the PRlncRNA Predictive Signature

Patient with HCC data were allocated into two groups ground on median risk scores. A gene set enrichment analysis (GSEA) program. The immune cell infiltration levels were determined by single-sample GSEA (ssGSEA) with the R package GSVA[23].

Cell Culture and Transfection

MHCC-97H and Hep-3B cell lines were bought purchased from ATCC were cultured in DMEM medium (Invitrogen). All cells were cultivated in complete medium containing 10% FBS (Invitrogen) at 37°C with 5% CO₂. siRNAs (si-AC009283.1 and si-LINC00942) were purchased from GenePharma (Shanghai, China). Lipofectamine 2000 was the transfection reagent (Invitrogen, USA).

Reverse Transcription and Real-Time Quantitative PCR

Total of 1000ng of cells and tissues of HCC RNA were gained (Invitrogen) by TRIzol reagent and translated into cDNA by reverse transcription using PrimeScript® RT Master Mix Perfect Real-Time (TaKaRa, Inc). Information about primers were listed in Table 1.

CCK-8, EdU and Colony Formation Assays

The proliferative capacity of the treated cells was detected by the CCK-8 assay. Each 2000 cells in the 96-well plate were incubated with 100ul complete medium and 10ul of Cell Counting Kit-8 solution (Topscience) for 2h and the absorbance value was detected at OD 450nm.

1×10⁴ cells/well in 24-well plate were incubated with mixture (2X preheated treated EdU solution and serum-free medium were equal volume, C0071S, Beyotime, China) for 2 h at 37°C, and fixed in Immunol Staining Fix Solution at 15 min. After permeabilization with Enhanced Immunostaining Permeabilization Buffer, these cells were reacted with the Click Additive Solution for 30 min. Subsequently, the cells nuclei were stained with Hoechst 33342 for 10 min, and the EdU positive cells were captured and quantified by fluorescence microscopy.

500 cells were seeded in six-well plates and incubated in complete medium for 2 weeks and then fixed with 4% polymethanol for 20 minutes and stain with 0.5% crystal violet for 15 minutes. The results were analyzed by ImageJ software. Each experiment was biologically replicated several times.

Cell Migration and Invasion Evaluated via Transwell Assay

2×10⁵ cells in 200µL serum-free medium was transferred to the upper Transwell chamber (Millipore, USA). The lower chamber was covered with 500µl complete medium. After 24 h of incubation, the staining process of the chamber was the same as colony formation staining. For cell invasion assay, the Matrigel Matrix (#354234, Corning, USA) added to the chamber and the cell transfection for 48 h. Other the experimental procedures were similar to cell migration assay. Randomly selected 3 fields in exterior of the chamber were used for cell counting under a microscope.

Western Blot Analysis and Immunofluorescence Staining

After transfection for 72h, the protein was extracted, total proteins were separated by SDS-PAGE and then transferred to a polyvinylidene fluoride membrane (Millipore, Burlington, MA, USA) by electroblotting. 5% fat-free milk were used to block the membrane for 90 mins at room temperature. The membranes were bound with specific primary antibodies overnight on 4°C in shaker. Then, the membrane after washed by TBST at three times was incubated with the corresponding secondary antibody at RT for 2 h, and imaged by ChemiDoc MP Imaging System (Bio-Rad, USA).

Paraffin-embedded human liver cancer samples were selected. After deparaffinization and dehydration, antigen retrieval of the samples was performed with EDTA antigen retrieval buffer (pH 8.0) at 100°C for 25 min. The samples were blocked with goat normal serum for 30 min and incubated overnight at 4°C with primary antibodies. After washing three times with PBS, these samples were incubated with secondary antibodies at RT for 50 min in the dark. The nuclei stained with DAPI were blue under ultraviolet light excitation, and the cells labelled with fluorescein were identified as follows: NLRP3-positive cells emitted red, Caspase-1-positive cells emitted pink and CD3-positive cells emitted green fluorescence.

Statistical Analysis

All data were analysis by R software (version 4.0.2) and GraphPad Prism 8 software. Differences between two different groups were evaluated for significance by Student′s t test. p < 0.05 (*), p < 0.01 (**) and p < 0.005 (***) were defined as statistically significant.

GO and KEGG Enrichment Analysis of PR-DEGs

Experimental design flowchart of this article is shown in Figure S1. From the GeneCards, TCGA and cBioportal databases, we obtained 76 PR-DEGs, including 71 highly expressed PR-DEGs and 5 lowed expressed PR-DEGs (Fig. 1A). GO and KEGG analyses were performed on the PR-DEGs, results showed that PR-DEGs were mainly associated with pyroptosis and Toll-like receptor and NF-κB signalling pathway(Fig. 1B-C).

Risk Model was Constructed Based on 8 PRlncRNAs

We identified 8 PRlncRNAs (AC015908.3, AC009283.1, LINC00942, AL365203.2, NRAV, AC099850.3, LINC01138, AC145207.5) were found to be strongly linked to the prognosis of HCC patients by Cox regression. Then, the predictive risk signature was constructed. PRlncRNAs expression levels in HCC tissues and normal tissue were shown in Fig. 2A. The connected PRlncRNAs to prognosis by LASSO regression were shown in Fig. 2B-C, and mRNA‒lncRNA coexpression network showed AC015908.3, AC009283.1 and AC099850.3 were identified as protective factors, while LINC00942, AL365203.2, NRAV, LINC01138 and AC145207.5 were identified as risk factors (Fig. 2D-E).

The Risk Score can Serve as an Independent Predictor of HCC Patient Prognosis

We allocated patients into high-risk and low-risk groups by calculating risk scores based on median values, and high-risk group had a poor prognosis in OS (Fig. 3A). As the number of patients dying increase the survival times decreased, and the risk scores increased (Fig. 3B-C). In this model, we found that clinicopathological indexes, including T/N/M stage, stage and grade, exhibited significant differences in high-risk and low-risk groups (Fig S2). To determine whether risk score is an independent prognostic factor in HCC, Cox regression analyses were performed on different clinicopathological indexes. The univariate and multivariate Cox regression analysis revealed that only risk score was an independent predictor (Fig. 3E). The AUC of risk score with a value of 0.764 showed superior predictive power compared to the other characteristics in the prognosis signature (Fig. 3F). The ROC areas, which were evaluated to validate the predictive prognosis ability of the risk signature with respect to 1-, 3- and 5-year OS, were 0.782, 0.736 and 0.766, respectively (Fig. 3G).

To further test our hypothesis, we performed analytical validation of patient OS in different risk groups by training and validation sets from TCGA database. The results showed the OS of patients in the high-risk group was worse in both the training and validation sets (Fig. 4A-B). The 1 -, 3 -, and 5-year survival rates of patients in the training cohort were significantly higher than those in the validation cohort (Fig. 4C-D). With the increase of the risk score of patients, the number of deaths of patients gradually increased, and the survival time gradually decreased (Fig. 4E-H). A nomogram was generated to detect the 1-, 3- and 5-year prognosis in the light of the risk score and clinicopathological indexes (Fig S3A). The OS at 1-, 3- and 5-years was consistent with the predicted survival, which indicated that the model showed acceptable reliability and validity when compared with calibration curves. These results showed that the forecasting precision of the risk score exhibited high stringency with respect to patient survival (Fig S3B-D). We explore the relevance between the prognosis signature and different clinicopathological indexes (Fig. 5). The results showed that patients with ages < = 65, male, stage1/3, T1/3, N0, M0 and grade2/3 had significant differences when the risk score was used as the criterion for OS, while patients with ages > 65, female, stage2, T2 and grade1 had meaningless statistical results. In general, the high-risk group had poor OS in all the different groups. Therefore, we cannot ignore that some individual factors. This is because the patient's own physical condition will have an impact on patient's survival and our prognosis score. Therefore, it is necessary to consider the applicability of patient' own conditions when including risk factors into an independent prognostic factor to test patient's prognosis.

The previous signature didn’t take the effect of DFS into account the prognosis, data of 676 HCC patients from the cBioPortal database was also constructed the predictive model for DFS in HCC. The results were the same as OS model. Kaplan-Meier analysis indicated that the DFS was shorter in high-risk group, not in low-risk group (Fig S4A). The ROC curve areas to validate the predictive ability of the signature were AUC (1-,3-,5-) = 0.702, 0.674 and 0.734, respectively (Fig S4D). We randomly divided patients into the first and second internal validation cohort. The applicability of the DFS signature was further verified in the two internal validation sets (Fig S4B-C and S4E-F). These results showed that regardless of how HCC patients are divided into high- and low- risk groups, which is not affected the consistency of the results, indicating that the DFS was shorter in the high-risk group than in the low-risk group in HCC patients and the AUCs of 1-, 3-, and 5-years survival were high reliability.

Then, we identified 6 PRlncRNAs, AC015908.3, AC145207.5, AL365203.2, LINC00942, LINC01138 and NRAV, to use for testing our hypothesis that gene expression influences clinicopathological variables (Fig S5). Significant differences mainly in T, stage and N were identified for different gene expression and risk scores. These results indicated that different clinical characteristics exert a certain effect on the expression levels of PRlncRNAs and related risk scores, while the patient pathological status need to be taken into consideration using this risk model. In summary, the results indicated that our prognostic model exhibited clear predictive ability based on different clinicopathological indexes.

PRlncRNAs Were Enriched Mainly in Tumour- and Immunity-Related Pathways in the High-Risk Group

We performed a principal components analysis (PCA) on the risk model to explore differences between the two risk groups. Fig S6A-D respectively were the 3D distribution of the whole genome, the PRGs sets, PRlncRNAs and risk gene. We performed a GSEA of the PRlncRNAs in different risk groups. KEGG results showed that primary immunodeficiency, and T-cell receptor signalling pathway were enriched in the high-risk group (Fig. 6A), while metabolism pathway was enriched mainly in the low-risk group (Fig. 6B). These results suggested that PRlncRNAs in the high-risk group were enriched mainly in tumour- and immunity-related pathways. The activation of immune responses, which involves immune checkpoints, plays a bidirectional role in the immune process. Blockade of immune checkpoint-related genes (such as programmed cell death-1 (PDCD1), cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT)) were evident in cancer, which leads to a suppressed antitumour immune response. These blockade immune checkpoints were highly expressed in the high-risk group (Fig. 6C).

The High-Risk Group Showed Low Levels of Immune Cell Infiltration in the Prognostic Model

In order to study further the relationship between the risk score and immune cells and function, we compared differences in immune cell subpopulations and immune-related pathways via ssGSEA. The results suggested that DCs, iDCs, macrophages, Tfh cells, Th2 cells and Tregs in the high-risk group showed higher levels of tumour infiltration, while the infiltration of B cells, mast cells and NK cell was lower (Fig. 6D). The antigen-presenting cell (APC)-costimulated pathway, comprising CCR, check point and MHC class I molecules, showed greater activity in the high-risk group, while the rates were lower in the high-risk group of both Type I and Type II INF (Fig. 6E). The reduced Type I/Type II IFN-induced response rates in the high-risk group implicated inhibited IFN activity in increased uncontrolled cell proliferation, enhanced immune checkpoint tolerance and decreased sensitivity of checkpoint proteins to inhibitors. These results suggest that immune function in the high-risk group was more easily activated. These differences may be leveraged for immunotherapy in HCC.

Downregulation of AC009283.1 and LINC00942 Inhibited/Promoted the Pyroptosis Pathways and Immune Infiltration

AC009283.1 had a low expression level in the HCC samples (Fig. 7A-B), while the expression level of LINC00942 was high (Fig. 8A-B). The results of Kaplan–Meier analysis from starBaseV3.0 and clinical samples provided by Xi-Jing Hospital showed that the low expression of AC009283.1 was related to poor prognosis with HCC patients (Fig. 7C-D). We also found that its expression level was strongly correlated with tumor number (Table 3). The high expression of LINC00942 lead to a poor prognosis (Fig. 8C-D), expression levels of LINC00942 were strongly correlated with maximal tumor size (Table 4).

We choose MHCC-97H cells and Hep-3B cells to further explore the function of AC009283.1 in vitro (Fig. 7E and Fig. 8E). After transfection with si-AC009283.1, the expression of these genes was significantly decreased (Fig. 7F and Fig. 8F). Downregulation of AC009283.1 expression promoted cells proliferation, migratory and invasion by the CCK-8 assay, EdU assay, colony formation assay and the Transwell assays (Fig. 7G-K). In addition, RNA and protein levels of NLRP3 and Caspase-1 were reduced when knockdown of AC009283.1(Fig. 7L-N). Furthermore, we examined the relationship between AC009283.1 expression and pyroptosis and immune markers in 4 clinical HCC specimens from Xi-Jing Hospital by immunofluorescence staining assay. We found a certain correlation that the low expression of AC009283.1 group had low-levels of NLRP3/Caspase-1 and CD3 (Fig. 7O), indicating that the low expression of tumor suppressor gene blocked the pyroptosis and immune response progress of tumor cells. While downregulation of LINC00942 had the opposite results (Fig. 8G-O).

HCC tends to be diagnosed in advanced stages and is associated with high mortality rates, and the prognosis of these patients after therapy is poor[24]. Immunotherapy as a promising strategy may expected to bring new hope for HCC treatment[25]. Clinically, immunotherapy for HCC has a good effect, but the benefit population only is 20%[26]. How to improve the benefit group of immunetherapy has become a big problem. LncRNAs regulated immune response in HCC had been widely reported[27–29]. Pyroptosis, which induces inflammatory by activates the classical signaling pathway, which may lead to inflammatory and immune in HCC[30, 31]. Few reports have indicated that PRlncRNAs directly regulate HCC progression, and their prognostic roles have not been adequately demonstrated. The relevance of the pyroptosis process and the immune infiltration of tumor is not clear.

In this study, we established a PRlncRNA prognostic signature for HCC patients, found that this model is more accurate in predicting the prognosis of patients than other clinical features and risk score can be used as an indicator to predict survival. We also preliminarily identified molecular function, pyroptosis process and the immune status of AC009283.1 and LINC00942. This model may offer guiding effect on evaluating the immune status of patients and improved clinical utility for immunotherapy strategies in HCC patients.

First, 76 PR-DEGs were enriched mainly in NOD-like receptor signalling pathway and NF-κB signalling pathway by KEGG analysis. Studies have suggested that NK-κB signalling pathway also plays a significant role in poptosis and pyroptosis, which can activate NLRP3/Caspase-1 signalling pathway[32, 33]. The results indicated that PR-DEGs may regulate HCC progression by triggering inflammasome and activating the NLRP3/Caspase-1-dependent pyroptosis pathway and may influencing the NF-κB signalling pathway.

Next, 8 DE-PRlncRNAs were identified and used to construct a risk signature via Cox regression and LASSO regression. Patients were grouped into high- and low-risk groups based on their median risk scores. The high-risk group exhibited shorter OS and DFS than the low-risk group. The ROC areas at 1-, 3- and 5-years indicated that the signature showed a certain level of accuracy and consistency in predicting survival. The risk score exhibited a good prognosis value than other different clinicopathological indexes. These results revealed that risk score can be served as an independent prognostic factor in predicting HCC patient prognosis.

The immune microenvironment mainly related to the changes of immune checkpoints, the proportions and functions of immune cell subpopulations. Therefore, we performed GSEA to research the potential path differences in the low-risk and high-risk groups, found that B-cell and T-cell receptor signalling pathway were mainly enriched in the high-risk group. The higher level of immune function in the high-risk group maybe mainly attribute to immune checkpoints[18]. Immune checkpoint-related genes (PDCD1, CTLA-4, TIGIT, HAVCR2, TNFRSF14) were highly expressed in the high-risk group, which may result in the changes of tumor cells, including decreased the infiltrating levels of immunoreactive cells and increased immunosuppressive cell. PDCD1 can protect against autoimmunity through promoting apoptosis of antigen-specific T cells in the lymph nodes and reducing apoptosis of regulatory T cells[34, 35]. TIGIT can inhibit the NK cell effect by preventing the initial death of tumor cells and releasing tumor antigens, also directly inhibit CD8 + T cell effects, or TIGIT + Treg can inhibit CD8 + T cells and prevent cancer cell clearance. High expression levels of inhibitory immune checkpoints in the high-risk group suggested that PRlncRNAs could evaluate the immune status in tumor microenvironment and improved clinical decision-making in cancer treatment.

ssGSEA analysis also showed that the low-risk group had greater infiltration of mast cells, and NK cells. NK cells and B cells are vital regulators in pyroptosis[36]. Activated NK cells have been reported to promote the production and release of GZMB, activating the Caspase-3-independent pyroptosis pathway, which leads to the death of tumour cells. Low levels of NK cells and B cells in the high-risk group reduced the inflammatory response and pyroptosis rate, leading to fewer immune cells infiltrating tumours and worsening the HCC prognosis. CCR, APC-costimulated pathway, check point and MHC class I showed greater activity in the high-risk group, and both Type I and Type II INF were lower in the high-risk group. CCRs and their receptors play significant roles in HCC cell proliferation, the tumour inflammatory microenvironment, immune evasion of tumour cells and angiogenesis[37–49].Type I IFN induces autophagy in a variety of cancer cells and regulates viral clearance and antigen presentation and the inhibition of cell proliferation[40]. The lower expression of Type I and Type II IFN decreased response in the high-risk group inhibited immune system activation and weakened antitumour immunity. The results also suggested that lower immunoreactivity in the TME may be pivotal factors contributing to poor prognosis of high-risk HCC patients.

AC009283.1 and LINC00942 were chosen to verify the function of PRlncRNA. Our studies indicated that AC009283.1 as a tumor-suppressor gene inhibited HCC development, the downregulation of AC009283.1 can reduce the expression of NLRP3, Caspase-1 and CD3, while si-LINC00942 had opposite effect, that indicating that PRlncRNA can be used to assess the pyroptosis and immune status of the patient's tumor cells.

In this study, we identified eight PRlncRNAs that were associated with HCC prognosis and then constructed a novel prognosis-predictive risk signature. We found that the risk score in the PRlncRNA signature heightened the predictive reliability of prognosis than clinical factors, which can be server as an independent predictor in our signature model and may undergo routine application in the future. In addition, lncRNAs were identified in the risk signature to evaluate the immune infiltration changes of patients, may helpful for the development of targeted kits to evaluate the prognosis and immune infiltration status of patients, predict the benefit groups of immune therapy, and improve the immunotherapy effect of liver cancer patients.

Although our risk model achieved better results, still has some shortcomings to point out. We only obtain data from the TCGA database and need to verify our model with other different databases. We examined the relation between lncRNA and pyroptosis and immune markers together for the first time beyond previous research based on the identification of the role of lncRNA and the changes of pyroptosis genes. However, more clinical samples are essential to verify the accuracy of our model.

Funding

This work was supported by the National Natural Science Foundation of China (81874051;82173047༛82203443); and the National Natural Science Foundation of Shaanxi province(2023-YBSF-416).

Author Contribution

WJ Z, C X and Q M contributed equally to this work, completed the experimental verification of the subject together. WJ Z wrote the article. C X and Q M and helped collect the published articles and edited the article. JZ Y and ZC L draw some figures. W P, HM W and HM L performed some cellular functional experiments. SB Q and KS T designed and supervised the study and revised the final manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors acknowledge the work by the TCGA-HCC data and the Department of Hepatobiliary Surgery, Xi-Jing Hospital, the Fourth Military Medical University for providing clinical samples of patients.

Data availability statement

The access to all databases used in the present study were public available.

Gentile D, Donadon M, Lleo A et al (2020)Surgical Treatment of Hepatocholangiocarcinoma: A Systematic Review. Liver Cancer, 9(1): 15–27
Khemlina G Ikeda Sand Kurzrock R(2017)The biology of Hepatocellular carcinoma: implications for genomic and immune therapies. Mol Cancer, 16(1): 149
Omar MA, Omran MM, Farid K et al (2023)Biomarkers for Hepatocellular Carcinoma: From Origin to Clinical Diagnosis. Biomedicines,11(7)
Guerra P, Martini A, Pontisso P et al (2023)Novel Molecular Targets for Immune Surveillance of Hepatocellular Carcinoma. Cancers (Basel),15(14)
Pinto Marques H, Gomes da Silva S, De Martin E et al (2020)Emerging biomarkers in HCC patients: Current status. Int J Surg, 82S(70–76
Xing M, Wang X, Kiken RA et al (2021)Immunodiagnostic Biomarkers for Hepatocellular Carcinoma (HCC): The First Step in Detection and Treatment. Int J Mol Sci, 22(11)
Wang W, Wei C(2020)Advances in the early diagnosis of hepatocellular carcinoma. Genes Dis, 7(3): 308–319
Dai Z, Liu WC, Chen XY et al (2023)Gasdermin D-mediated pyroptosis: mechanisms, diseases, and inhibitors. Front Immunol, 14(1178662
Wei X, Xie F, Zhou X et al (2022)Role of pyroptosis in inflammation and cancer. Cell Mol Immunol, 19(9): 971–992
Huang Y, Wang JW, Huang J et al (2022)Pyroptosis, a target for cancer treatment? Apoptosis,27(1–2): 1–13
Yu P, Zhang X, Liu N et al (2021)Pyroptosis: mechanisms and diseases. Signal Transduct Target Ther, 6(1): 128
Lu F, Lan Z, Xin Z et al (2020)Emerging insights into molecular mechanisms underlying pyroptosis and functions of inflammasomes in diseases. J Cell Physiol, 235(4): 3207–3221
Du T, Gao J, Li P et al (2021)Pyroptosis, metabolism, and tumor immune microenvironment. Clin Transl Med, 11(8): e492
Hou J, Hsu J, Mand Hung M C(2021)Molecular mechanisms and functions of pyroptosis in inflammation and antitumor immunity. Mol Cell, 81(22): 4579–4590
Loveless R Bloomquist Rand Teng Y(2021)Pyroptosis at the forefront of anticancer immunity. J Exp Clin Cancer Res, 40(1): 264
Chu Q, Jiang Y, Zhang W et al (2016)Pyroptosis is involved in the pathogenesis of human hepatocellular carcinoma. Oncotarget, 7(51): 84658–84665
Huang Z, Zhou JK, Peng Y et al (2020)The role of long noncoding RNAs in hepatocellular carcinoma. Mol Cancer, 19(1): 77
Wang T, Yang Y, Sun T et al (2022)The Pyroptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular Carcinoma. Front Cell Dev Biol, 10(779269
Chen Z, He M, Chen J et al (2020)Long non-coding RNA SNHG7 inhibits NLRP3-dependent pyroptosis by targeting the miR-34a/SIRT1 axis in liver cancer. Oncol Lett, 20(1): 893–901
Chen M, Nie Z, Li Y et al (2021)A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients. Front Cell Dev Biol, 9(699804
Li G, Zhang D, Liang C et al (2022)Construction and validation of a prognostic model of pyroptosis related genes in hepatocellular carcinoma. Front Oncol, 12(1021775
Deng M, Sun S, Zhao R et al (2022)The pyroptosis-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma. Mol Med, 28(1): 16
Xu Q, Yand Huang W W(2021)Identification of immune-related lncRNA signature for predicting immune checkpoint blockade and prognosis in hepatocellular carcinoma. Int Immunopharmacol, 92(107333
Swamy SG, Kameshwar VH, Shubha PB et al (2017)Targeting multiple oncogenic pathways for the treatment of hepatocellular carcinoma. Target Oncol, 12(1): 1–10
Zhao M, Huang H, He F et al (2023)Current insights into the hepatic microenvironment and advances in immunotherapy for hepatocellular carcinoma. Front Immunol, 14(1188277
Kotsari M, Dimopoulou V, Koskinas J et al (2023)Immune System and Hepatocellular Carcinoma (HCC): New Insights into HCC Progression. Int J Mol Sci, 24(14)
Afra F, Mahboobipour AA, Salehi Farid A et al (2023)Recent progress in the immunotherapy of hepatocellular carcinoma: Non-coding RNA-based immunotherapy may improve the outcome. Biomed Pharmacother, 165(115104
Verma S, Sahu B, Dand Mugale M N(2023)Role of lncRNAs in hepatocellular carcinoma. Life Sci, 325(121751
Klingenberg M, Matsuda A, Diederichs S et al (2017)Non-coding RNA in hepatocellular carcinoma: Mechanisms, biomarkers and therapeutic targets. J Hepatol, 67(3): 603–618
Zou Z, Zhao M, Yang Y et al (2023)The role of pyroptosis in hepatocellular carcinoma. Cell Oncol (Dordr), 46(4): 811–823
Garcia-Pras E, Fernandez-Iglesias A, Gracia-Sancho J et al (2021)Cell Death in Hepatocellular Carcinoma: Pathogenesis and Therapeutic Opportunities. Cancers (Basel), 14(1)
Liu T, Zhou YT, Wang LQ et al (2019)NOD-like receptor family, pyrin domain containing 3 (NLRP3) contributes to inflammation, pyroptosis, and mucin production in human airway epithelium on rhinovirus infection. J Allergy Clin Immunol, 144(3): 777–787 e9.
Man SM, Karki Rand Kanneganti T D(2017)Molecular mechanisms and functions of pyroptosis, inflammatory caspases and inflammasomes in infectious diseases. Immunol Rev, 277(1): 61–75
Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 12(4):252–264
Richardson N, Wootton GE, Bozward AG et al (2022)Challenges and opportunities in achieving effective regulatory T cell therapy in autoimmune liver disease. Semin Immunopathol, 44(4): 461–474
Ohue Y, Nishikawa H(2019)Regulatory T (Treg) cells in cancer: Can Treg cells be a new therapeutic target? Cancer Sci, 110(7): 2080–2089
Zheng Y, Li Y, Feng J et al (2021)Cellular based immunotherapy for primary liver cancer. J Exp Clin Cancer Res, 40(1): 250
Zilio S, Bicciato S, Weed D et al (2022)CCR1 and CCR5 mediate cancer-induced myelopoiesis and differentiation of myeloid cells in the tumor. J Immunother Cancer, 10(1)
Bolko L, Jiang W, Tawara N et al (2021)The role of interferons type I, II and III in myositis: A review. Brain Pathol, 31(3): e12955
Broggi A Granucci Fand Zanoni I(2020)Type III interferons: Balancing tissue tolerance and resistance to pathogen invasion. J Exp Med, 217(1)

Tables 1 to 4 are available in the Supplementary Files section.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

The Risk Signature of Pyroptosis-Related Long Noncoding RNAs Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Data acquisition

GO and KEGG Analysis of PR-DEGs

Construction of a Risk Signature

Functional Enrichment Analysis of the PRlncRNA Predictive Signature

Cell Culture and Transfection

Reverse Transcription and Real-Time Quantitative PCR

CCK-8, EdU and Colony Formation Assays

Cell Migration and Invasion Evaluated via Transwell Assay

Western Blot Analysis and Immunofluorescence Staining

Statistical Analysis

Results

GO and KEGG Enrichment Analysis of PR-DEGs

Risk Model was Constructed Based on 8 PRlncRNAs

The Risk Score can Serve as an Independent Predictor of HCC Patient Prognosis

PRlncRNAs Were Enriched Mainly in Tumour- and Immunity-Related Pathways in the High-Risk Group

The High-Risk Group Showed Low Levels of Immune Cell Infiltration in the Prognostic Model

Discussion

Declarations

Funding

Author Contribution

Acknowledgements

Data availability statement

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1