SOX9/MKLN1-AS Axis Induces Hepatocellular Carcinoma Proliferation and Epithelial-Mesenchymal Transition

Profibrotic role of the SOX9-MMP10-ECM biosynthesis axis in the tracheal fibrosis after injury and repair

Fibroblast activation and extracellular matrix (ECM) deposition play an important role in the tracheal abnormal repair process and fibrosis. As a transcription factor, SOX9 is involved in fibroblast activation and ECM deposition. However, the mechanism of how SOX9 regulates fibrosis after tracheal injury remains unclear. We investigated the role of SOX9 in TGF-β1-induced fibroblast activation and ECM deposition in rat tracheal fibroblast (RTF) cells. SOX9 overexpression adenovirus (Ad-SOX9) and siRNA were transfected into RTF cells. We found that SOX9 expression was up-regulated in RTF cells treated with TGF-β1. SOX9 overexpression activated fibroblasts and promoted ECM deposition. Silencing SOX9 inhibited cell proliferation, migration, and ECM deposition, induced G2 arrest, and increased apoptosis in RTF cells. RNA-seq and chromatin immunoprecipitation sequencing (ChIP-seq) assays identified MMP10, a matrix metalloproteinase involved in ECM deposition, as a direct target of SOX9, which promotes ECM degradation by increasing MMP10 expression through the Wnt/β-catenin signaling pathway. Furthermore, in vivo, SOX9 knockdown ameliorated granulation proliferation and tracheal fibrosis, as manifested by reduced tracheal stenosis. In conclusion, our findings indicate that SOX9 can drive fibroblast activation, cell proliferation, and apoptosis resistance in tracheal fibrosis via the Wnt/β-catenin signaling pathway. The SOX9-MMP10-ECM biosynthesis axis plays an important role in tracheal injury and repair. Targeting SOX9 and its downstream target MMP10 may represent a promising therapeutic approach for tracheal fibrosis.

Prognosis and chemotherapy drug sensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature

Disulfidptosis, a new type of regulated cell death associated with the actin cytoskeleton, provides a new therapeutic tool for cancers. The direct relationship between disulfidptosis-related lncRNAs(DRLs) in liver hepatocellular carcinoma(HCC) remains unclear. We acquired transcriptomic data, corresponding clinical data, and tumor mutation data of HCC from the TCGA database. First of all, DRLs were determined through correlation analysis. Then, a prognostic model containing six DRLs was created by adopting univariate Cox regression, LASSO algorithm and multivariate Cox regression analysis. Based on the model, 424 HCC patients were divided into high- and low-risk groups. Next, we structured ROC curves and PCA through combining the model and clinical data. Enrichment analysis and immune infiltration analysis were adopted to further explore the relationship between the model and prognosis. In addition, we explored the relationship between the model and tumor mutation burden (TMB). There were significant differences between high- and low- risk groups, and patients in the high-risk group showed poor prognosis. Enrichment analysis suggested that metabolic progress was obviously different between the two groups. According to the analysis of immune infiltration, there were several differences in immune cells, function, and checkpoints. Patients with high-risk and high TMB demonstrated the least favorable prognosis. The two risk groups both manifested visiblly in chemotherapy drug sensitivity. To sum up, we set up a DRL-based signature and that may provide a predictable value for the prognosis and use of chemotherapy drugs for HCC patients.

Prognostic Signature Constructed of Seven Ferroptosis-Related lncRNAs Predicts the Prognosis of HBV-Related HCC

BACKGROUND

Ferroptosis and lncRNAs both play crucial roles in cancers. But the roles of ferroptosis-related lncRNAs (FRLncs) in HBV-related HCC (HBV-HCC) remain ambiguous.

METHODS

The gene expression profile and clinical data were originated from the Cancer Genome Atlas (TCGA) database. The risk signature was constructed by FRLncs based on the Cox regression analysis. The survival curve, Cox regression analysis, and time-dependent receiver operating characteristic (ROC) curve were adopted to verify the independence and reliability of the signature. A nomogram was established. Immune-infiltrating cells, immune functions, and checkpoints were analyzed.

RESULTS

A risk signature composed of 7 FRLncs (LINC00942, AC131009.1, POLH-AS1, AC090772.3, MKLN1-AS, AC009403.1, AL031985.3) was constructed and divided HBV-HCC patients into high- and low-risk groups. Patients in the high-risk group showed a poor prognosis. The area under curves (AUC) of the signature for 1-, 3-, and 5-year was satisfactory. A nomogram composed of gender, stage, age, grade, and risk signature was established. The risk signature and nomogram displayed appreciable independence and reliability in HBV-HCC patients. The T-cell CD8 + , monocyte, and macrophage M1 were expressed differently significantly in HCC patients, while macrophage M2 showed an obvious difference in the HBV-HCC patients between the different risk groups. PDCD1 and CTL4 were expressed higher in the high-risk group of HCC patients.

CONCLUSION

A 7-lncRNA signature was identified as a potential prognostic predictor for HBV-HCC patients. Immune therapy may be a promising strategy for HCC patients, especially HBV-HCC patients.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma

BACKGROUND

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC).

METHODS

Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments.

RESULTS

The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis.

CONCLUSIONS

This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

Integrating bulk and single-cell RNA sequencing data to establish necroptosis-related lncRNA risk model and analyze the immune microenvironment in hepatocellular carcinoma

Background

The increasing evidence suggests that necroptosis mediates many behaviors of tumors, as well as the regulation of the tumor microenvironment. Long non-coding RNAs (lncRNAs) are involved in a variety of regulatory processes during tumor development and are significantly associated with patient prognosis. It suggests that necroptosis-related lncRNAs (NRlncRNAs) may serve as biomarkers for the prognosis of hepatocellular carcinoma (HCC).

Methods

lncRNA expression profiles of HCC were obtained from TCGA database. LncRNAs associated with necroptosis were extracted using correlation analysis. Prognostic models were constructed based on least absolute shrinkage and selection operator algorithm (LASSO) and multivariate Cox regression analysis. The differences of tumor microenvironment between high-risk and low-risk groups were further analyzed. Single-cell RNA sequencing data of HCC was performed to assess the enrichment of necroptosis-related genes in immune cell subsets. Finally, real-time RT-PCR was used to detect the prognosis-related lncRNAs expression in different HCC cell lines.

Results

We constructed a prognostic signature based on 8 NRlncRNAs, which also showed good predictive accuracy. The model showed that the prognosis of patients with high-risk score was significantly worse than that of patients with low-risk score (P < 0.05). Combined with the clinical characteristics and risk score of HCC, Nomogram was drawn for reference in clinical practice. In addition, immune cell infiltration analysis and single cell RNA sequencing analysis showed that a low level of immune infiltration was observed in patients at high risk and that there was a significant correlation between NRlncRNAs and macrophages. The results of RT-qPCR also showed that 8 necroptosis-related lncRNAs were highly expressed in HCC cell lines and human liver cancer tissues.

Conclusion

This prognostic signature based on the necroptosis-related lncRNAs may provide meaningful clinical insights for the prognosis and immunotherapy responses in patients with HCC.

The important role of miR-1-3p in cancers

Cancer is a malignant tumor that seriously threatens human life and health. At present, the main treatment methods include surgical resection, chemotherapy, radiotherapy, and immunotherapy. However, the mechanism of tumor occurrence and development is complex, and it produces resistance to some traditional treatment methods, leading to treatment failure and a high mortality rate for patients. Therefore, exploring the molecular mechanisms of tumor occurrence, development, and drug resistance is a very important task. MiRNAs are a type of non-coding small RNA that regulate a series of biological effects by binding to the 3'-UTR of the target mRNA, degrading the mRNA, or inhibiting its translation. MiR-1-3p is an important member of them, which is abnormally expressed in various tumors and closely related to the occurrence and development of tumors. This article introduces miR-1-3p from multiple aspects, including its production and regulation, role in tumor occurrence and development, clinical significance, role in drug resistance, and approaches for targeting miR-1-3p. Intended to provide readers with a comprehensive understanding of the important role of miR-1-3p in tumors.

Antler-derived microRNA PC-5p-1090 inhibits HCC cell proliferation, migration, and invasion by targeting MARCKS, SMARCAD1, and SOX9

The capability of microRNAs (miRNAs) to regulate gene expression across species has opened new avenues for miRNA-based therapeutics. Here, we investigated the potential of PC-5p-1090 (miR-PC-1090), a miRNA found in deer antlers, to control the malignant phenotypes of hepatocellular carcinoma (HCC) cells. Using Cell Counting Kit-8 and transwell assays, we found that heterologous expression of miR-PC-1090 inhibited HCC cell proliferation, migration, and invasion. Bioinformatics analysis indicated that predicted miR-PC-1090 targets, including MARCKS, SMARCAD1, and SOX9, were significantly elevated in HCC tissues, and their high expressions were associated with poor overall survival of HCC patients. Moreover, mechanistic investigations revealed that miR-PC-1090 promoted the degradation of MARCKS and SMARCAD1 mRNAs and hindered the translation of SOX9 mRNA by recognizing their 3' untranslated regions. Subsequent loss-of-function and rescue experiments confirmed the involvement of MARCKS, SMARCAD1, and SOX9 in miR-PC-1090-suppressed HCC cell proliferation, migration, and invasion. Notably, MARCKS knockdown induced the downregulation of phosphorylated MARCKS and a corresponding upregulation of phosphorylated AKT in HCC. Conversely, miR-PC-1090 repressed MARCKS phosphorylation and effectively circumvented the activation of the PI3K/AKT pathway. Furthermore, miR-PC-1090 regulates the Wnt/β-catenin pathway through SMARCAD1- and SOX9-mediated reduction of β-catenin expression. Overall, our results illustrate the tumor-suppressive activity and molecular mechanism of antler-derived miR-PC-1090 in HCC cells, indicating its potential as a multiple-target agent for HCC treatment.

Identification of a cuproptosis and copper metabolism gene-related lncRNAs prognostic signature associated with clinical and immunological characteristics of hepatocellular carcinoma

Background

The relationship between cuproptosis and HCC is still in the exploratory stage. Long noncoding RNAs (lncRNAs) have recently been linked to the progression of hepatocellular carcinoma (HCC). However, the clinical significance of lncRNAs associated with cuproptosis remains unclear.

Methods

Based on The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma (LIHC) dataset, we identified characteristic prognostic lncRNAs by univariate, LASSO, and multifactorial regression analysis, and constructed a prognostic signature of cuproptosis-related lncRNAs in HCC. The role of lncRNAs were identified through CCK-8, clone formation in Huh-7 cells with high expression of FDX1. Prognostic potential of the characteristic lncRNAs was evaluated in each of the two cohorts created by randomly dividing the TCGA cohort into a training cohort and a test cohort in a 1:1 ratio. Immune profiles in defined subgroups of cuproptosis-related lncRNA features as well as drug sensitivity were analyzed.

Results

We constructed a multigene signature based on four characteristic prognostic lncRNAs (AL590705.3, LINC02870, KDM4A-AS1, MKLN1-AS). These four lncRNAs participated in the development of cuproptosis. HCC patients were classified into high-risk and low-risk groups based on the median value of the risk score. The receiver operating characteristic curve area under the curve values for 1-, 3-, and 5-year survival were 0.773, 0.728, and 0.647, respectively, for the training cohort, and 0.764, 0.671, and 0.662, respectively, for the test cohort. Univariate and multifactorial regression analyses indicated that this prognostic feature was an independent prognostic factor for HCC. Principal component analysis plots clearly distinguished between low- and high-risk patients in terms of their probability of survival. Furthermore, gene set enrichment analysis showed that a variety of processes associated with tumor proliferation and progression were enriched in the high-risk group compared with the low-risk group. Moreover, there were significant differences in the expression of immune cell subpopulations, immune checkpoint genes, and potential drug screening, which provided distinct therapeutic recommendations for individuals with various risks.

Conclusions

We constructed a novel cuproptosis-associated lncRNA signature with a significant predictive value for the prognosis of patients with HCC. Cuproptosis-associated lncRNAs are associated with the tumor immune microenvironment of HCC and even the efficacy of tumor immunotherapy.

Construction of a prognostic model for HCC based on ferroptosis-related lncRNAs expression and its potential to predict the response and irAEs of immunotherapy

Background: Ferroptosis is an iron-dependent programmed cell death process, and studies have confirmed that it plays an important regulatory role in the occurrence and development of various malignancies including hepatocellular carcinoma (HCC). In addition, the role of abnormally expressed long non-coding RNAs (lncRNAs) in regulating and driving the occurrence and development of HCC has attracted more and more attention. However, there is still a lack of research on the role of ferroptosis-related lncRNAs in the prognosis prediction of HCC patients.

Method: In this study, we used the Pearson test method to analyze the association between differentially expressed lncRNAs and ferroptosis-related genes in HCC and normal tissues obtained from The Cancer Genome Atlas (TCGA), and found 68 aberrantly expressed and prognosis-related ferroptosis-related lncRNAs. Based on this, we established an HCC prognostic model composed of 12 ferroptosis-related lncRNAs. In addition, HCC patients were divided into a high-risk group and a low-risk group according to the risk score of this 12 ferroptosis-related lncRNAs prognostic model. Gene enrichment analysis indicated that ferroptosis-related lncRNA-based expression signatures may regulate HCC immune microenvironment signaling pathways through ferroptosis, chemical carcinogenesis-reactive oxygen species, and NK cell-mediated cytotoxicity pathways. In addition, immune cell correlation analysis showed that there were significant differences in immune infiltrating cell subtypes, such as Th cells, macrophages, monocytes, and Treg cells between the two groups. In addition, the expression of multiple immune checkpoint molecules was found to be significantly increased in the high-risk group (eg, PD1, CTLA-4, CD86, etc.).

Results: Our research provides a new method for predicting prognosis using a ferroptosis-related lncRNA expression signature prognostic model in hepatocellular carcinoma. And it provides new tools for predicting patient response and adverse effects of immunotherapy.

Conclusion: In conclusion, ferroptosis-related lncRNA expression signatures can be used to construct a prognostic prediction model to predict the overall survival of HCC patients, and can be used as an independent influencing factor for prognosis. Further analysis showed that ferroptosis-related lncRNAs may affect the efficacy of immunotherapy in patients with HCC by altering the tumor microenvironment, so this model may serve as a new indicator of the response and irAEs of HCC to immunotherapy.

An integrative analysis revealing cuproptosis-related lncRNAs signature as a novel prognostic biomarker in hepatocellular carcinoma

Background: Cuproptosis is a newly defined form of cell death, whether cuproptosis involved in hepatocellular carcinoma (HCC) remains elusive. Method: We obtained patients' RNA expression data and follow-up information from University of California Santa Cruz (UCSC) and The Cancer Genome Atlas (TCGA). We analyzed the mRNA level of Cuproptosis-related genes (CRGs) and performed univariate Cox analysis. Liver hepatocellular carcinoma (LIHC) was chosen for further investigation. Real-Time quantitative PCR (RT-qPCR), Western blotting (WB), Immunohistochemical (IHC), and Transwell assays were used to determine expression patterns and functions of CRGs in LIHC. Next, we identified CRGs-related lncRNAs (CRLs) and differentially expressed CRLs between HCC and normal cases. Univariate Cox analysis, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to construct the prognostic model. Univariate and multivariate Cox analysis was used to assess whether the risk model can act as an independent risk factor of overall survival duration. Different risk groups performed immune correlation analysis, tumor mutation burden (TMB), and Gene Set Enrichment Analysis (GSEA) analysis were performed in different risk groups. Finally, we assessed the performance of the predictive model in drug sensitivity. Results: CRGs expression levels have significant differences between tumor and normal tissues. High expression of Dihydrolipoamide S-Acetyltransferase (DLAT) correlated to metastasis of HCC cells and indicated poor prognosis for HCC patients. Our prognostic model consisted of four cuproptosis-related lncRNA (AC011476.3, AC026412.3, NRAV, MKLN1-AS). The prognostic model performed well in predicting survival rates. The results from Cox regression analysis suggested that risk score can serve as an independent prognostic element for survival durations. Survival analysis revealed that low risk patients have extended survival periods compared with those with high risk. The results of the immune analysis indicated that risk score has a positive correlation with B cell and CD4⁺ T cell Th2, while has a negative relationship with endothelial cell and hematopoietic cells. Besides, immune checkpoint genes have higher expression folds in the high-risk set than in the low-risk set. The high-risk group had higher rates of genetic mutation than the low-risk set while having a shorter survival time. GSEA revealed the signaling pathways enriched in the high-risk group were mostly immune-related, while metabolic-related pathways were enriched in the low-risk group. Drugs sensitivity analysis indicated that our model has the ability to predict the efficacy of clinical treatment. Conclusion: The Cuproptosis-related lncRNAs prognostic formula is a novel predictor of HCC patients' prognosis and drug sensitivity.

A prognostic exosome-related LncRNA risk model correlates with the immune microenvironment in liver cancer

Background: Emerging studies have shown the important roles of long noncoding RNAs (lncRNAs) in the occurrence and development of liver cancer. However, the exosome-related lncRNA signature in liver cancer remains to be clarified.

Methods: We obtained 371 tumor specimens and 50 normal tissues from the TCGA database. These samples were randomly divided into the training queue and verification queue. The exosome-related lncRNA risk model was verified by correlation analysis, Lasso regression analysis, and Cox regression analysis. The differences in the immune microenvironment in the two risk groups were obtained by analyzing the infiltration of different immune cells.

Results: Five exosome-related lncRNAs associated (MKLN1-AS, TMCC1-AS1, AL031985.3, LINC01138, AC099850.3) with a poor prognosis were identified and used to construct the signature. Receiver operating curve (ROC) and survival curves were used to confirm the predictive ability of this signature. Based on multivariate regression analysis in the training cohort (HR: 3.033, 95% CI: 1.762–5.220) and validation cohort (HR: 1.998, 95% CI: 1.065–3.751), the risk score was found to be an independent risk factor for patient prognosis. Subsequently, a nomogram was constructed to predict the 1-, 3-, 5-years survival rates of liver cancer patients. Moreover, this signature was also related to overexpressed immune checkpoints (PD-1, B7-H3, VSIR, PD-L1, LAG3, TIGIT and CTLA4).

Conclusion: Our study showed that exosome-related lncRNAs and the corresponding nomogram could be used as a better index to predict the outcome and immune regulation of liver cancer patients. This signature might provide a new idea for the immunotherapy of liver cancer in the future.

Establishment of Prognostic Signatures of N6-Methyladenosine-Related lncRNAs and Their Potential Functions in Hepatocellular Carcinoma Patients

N6-methyladenosine (m6a)-related mRNAs and lncRNAs have been explored for their functions in several cancers. The present study aimed to identify potential signatures of m6a-related lncRNAs in hepatocellular carcinoma (HCC). We downloaded the expression and clinical data from The Cancer Genome Atlas (TCGA) database. The interacted mRNAs and lncRNAs, prognosis-related lncRNAs, potential metabolic pathways of lncRNAs, immune infiltration of various cells, and CD274 (PD-L1) -related lncRNAs were analyzed. Then, in vitro experiments explored the role of AC012073.1 (LOC105377626) in HCC cell lines. We found that candidate 14 lncRNA signatures play functions in HCC maybe by affecting immune infiltration, cell cycle, Notch signaling pathway, etc. LncRNA AC012073.1 (LOC105377626) functions as oncogenic roles in affecting HCC prognosis.

Long non-coding RNA muskelin 1 antisense RNA as a potential therapeutic target in hepatocellular carcinoma treatment

Long non-coding RNAs are essential to hepatocellular carcinoma (HCC) development, progression, and incidence of drug resistance. However, the biological significance of long non-coding RNA muskelin 1 antisense RNA (MKLN1-AS) remains poorly characterized. In this study, we observed noticeable increased levels of MKLN1-AS in HCC tissues. This upregulation of MKLN1-AS was clinically associated with vascular invasion and decreased disease-free survival and overall survival of patients with HCC. Functionally, MKLN1-AS-knockdown dramatically suppressed the metastasis and growth of HCC cells in vitro and in vivo. Additionally, the knockdown of MKLN1-AS augmented the pro-apoptosis effect of lenvatinib. Taken together, our findings indicate that MKLN1-AS may be exploited as a potential prognostic predictor and therapeutic target for HCC treatment.