Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

The expression landscape and clinical significance of methyltransferase-like 17 in human cancer and hepatocellular carcinoma: a pan-cancer analysis using multiple databases

BACKGROUND

Methyltransferase-like (METTL) family protein plays a crucial role in the progression of malignancies. However, the function of METTL17 across pan-cancers, especially in hepatocellular carcinoma (HCC) is still poorly understood.

METHODS

All original data were downloaded from TCGA, GTEx, HPA, UCSC databases and various data portals. First, we comprehensively analyzed RNA-seq data from the HPA database of 25 human tissues. An array of bioinformatics methods was employed to explore the potential oncogenic roles of METTL17, including analyzing its related prognosis, mutation, landscapes, tumor stemness index, immune cell infiltration, and other factors among different tumors. Additionally, gene set enrichment analysis (GSEA) was used to analyze pathways associated with METTL17 in HCC. Immunohistochemistry (IHC) was performed on clinical samples to validate the differential expression of METTL17 in HCC and normal tissues. Ultimately, we constructed a METTL17-related risk-score model of HCC and validated its prognostic classification efficiency. Survival rates were calculated using the Kaplan-Meier method. Statistical significance was defined as P < 0.05.

RESULTS

METTL17 was differentially expressed in various cancers. METTL17 maintained strong correlations with the cancer patient's prognosis, genetic alterations, tumor stemness index, and immune-infiltrated cells, etc. In addition, IHC experiments verified that METTL expression was significantly decreased in liver tissues of HCC patients compared to normal liver tissue. GESA analysis indicated METTL17 mainly involves oncogenic and immune-related pathways among HCC. MRPS5, CHCHD2, NCBP1, LRPPRC, DAP3, and BMS1 were included in a prognostic model based on METTL17's interaction networks. Kaplan-Meier survival analysis of the prognostic model showed that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) of the 1-year, 3-year, and 5-year OS were 0.747, 0.671, and 0.631, respectively.

CONCLUSIONS

METTL17 may serve as a novel prognostic marker and therapeutic target for human tumors, offering a theoretical foundation for formulating more effective and tailored clinical treatment options for cancers, particularly HCC.

Long Noncoding RNAs MALAT1 and HOTTIP Act as Serum Biomarkers for Hepatocellular Carcinoma

BACKGROUND

Circulating tumor markers with satisfactory sensitivity and specificity play crucial roles in cancer diagnosis and therapy. This prospective study aimed to evaluate the potential of circulating lncRNAs as biomarkers for hepatocellular carcinoma (HCC).

METHODS

A total of 74 patients with HCC and 94 healthy controls were enrolled. The expression levels of candidate genes in serum were detected by qRT-PCR. Receiver operating characteristic (ROC) curve analysis and logistic regression were employed to investigate the diagnostic capacity of lncRNAs. The analysis of 3-year overall survival (OS) was conducted using the Kaplan-Meier method and log-rank test.

RESULTS

Of the 9 candidate genes, 6 lncRNAs could be stably detected in serum. The expression levels of circulating MALAT1 and HOTTIP in HCC patients were significantly higher than those in controls (P < 0.001). ROC analysis showed that MALAT1 and HOTTIP were more effective than alpha-fetoprotein (AFP) (P < 0.010) in the diagnosis of HCC, with AUCs of 0.896 and 0.899, respectively. Additionally, a panel consisting of MALAT1, HOTTIP, and AFP was constructed to obtain an AUC of 0.968 with a sensitivity of 87.8% and specificity of 94.7% in HCC diagnosis. Moreover, the upregulation of MALAT1 was not only related to multiple tumor lesions, HCV infection, AST level, and AFP level, but also suggested shorter OS. A high expression level of HOTTIP was associated with metastasis.

CONCLUSION

Serum MALAT1 and HOTTIP play indicative roles as non-invasive biomarkers for HCC.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

A cuproptosis-related lncRNA signature for predicting prognosis and immune response in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis. Cuproptosis is a novel type of cell death, which differs from previously reported types of cell death such as apoptosis, autophagy, proptosis, ferroptosis, necroptosis, etc. Long non-coding RNAs (lncRNAs) play multiple roles in HCC.

Methods

We downloaded information from The Cancer Genome Atlas (TCGA) database, and obtained cuproptosis-related genes from published studies. The cuproptosis-related lncRNAs were obtained by correlation analysis, and subsequently used to construct a prognostic cuproptosis-related lncRNA signature. Analyses of overall survival (OS), progression-free survival (PFS), receiver operating characteristic (ROC) curve with the area under the curve (AUC) values and the index of concordance (c-index) curve were used to evaluate the signature. The tumor microenvironment (TME) was analyzed by ESTIMATE algorithm. The immune cell data was downloaded from the Tumor Immune Estimation Resource (TIMER) 2.0 database. Immune-related pathways were analyzed by single-sample gene set enrichment analysis (ssGSEA) algorithm. Immunophenoscore (IPS) scores from The Cancer Immunome (TCIA) database were used to evaluate immunotherapy response. The "pRRophetic" was employed to screen drugs for high-risk patients. The candidate lncRNA expression levels were detected by Real Time Quantitative PCR.

Results

We constructed a cuproptosis-related lncRNA signature containing seven lncRNAs: AC125437.1, PCED1B-AS1, PICSAR, AP001372.2, AC027097.1, LINC00479, and SLC6A1-AS1. This signature had excellent accuracy, and was independent of the stratification of clinicopathological features. Further study showed that high-risk tumors under this signature had higher TMB, fewer TME components and higher tumor purity. The tumors with high risk were not enriched in immune cell infiltration or immune process pathways, and high-risk patients had a poor response to immunotherapy. Moreover, 29 drugs such as sorafenib, dasatinib and paclitaxel were screened for high-risk HCC patients to improve their prognosis. The expression levels of the candidate lncRNAs in HCC tissue were significantly increased (except PCED1B-AS1).

Conclusions

Our prognostic cuproptosis-related lncRNA signature was accurate and effective for predicting the prognosis of HCC. The immunotherapy was unsuitable for high-risk HCC patients with this signature.

A network-based approach reveals long non-coding RNAs associated with disease activity in lupus nephritis: key pathways for flare and potential biomarkers to be used as liquid biopsies

Objective

A blood-based biomarker is needed to assess lupus nephritis (LN) disease activity, minimizing the need for invasive kidney biopsies. Long non-coding RNAs (lncRNAs) are known to regulate gene expression, appear to be stable in human plasma, and can serve as non-invasive biomarkers.

Methods

Transcriptomic data of whole blood samples from 74 LN patients and 20 healthy subjects (HC) were analyzed to identify differentially expressed (DE) lncRNAs associated with quiescent disease and flares. Weighted gene co-expression network analysis (WGCNA) was performed to uncover lncRNAs with a central role (hub lncRNAs) in regulating key biological processes that drive LN disease activity. The association of hub lncRNAs with disease activity was validated using RT-qPCR on an independent cohort of 15 LN patients and 9 HC. cis- and trans-targets of validated lncRNAs were explored in silico to examine potential mechanisms of their action.

Results

There were 444 DE lncRNAs associated with quiescent disease and 6 DE lncRNAs associated with flares (FDR <0.05). WGCNA highlighted IFN signaling and B-cell activity/adaptive immunity as the most significant processes contributing to nephritis activity. Four disease-activity-associated lncRNAs, namely, NRIR, KLHDC7B-DT, MIR600HG, and FAM30A, were detected as hub genes and validated in an independent cohort. NRIR and KLHDC7B-DT emerged as potential key regulators of IFN-mediated processes. Network analysis suggests that FAM30A and MIR600HG are likely to play a central role in the regulation of B-cells in LN through cis-regulation effects and a competing endogenous RNA mechanism affecting immunoglobulin gene expression and the IFN-λ pathway.

Conclusions

The expression of lncRNAs NRIR, KLHDC7B-DT, FAM30A, and MIR600HG were associated with disease activity and could be further explored as blood-based biomarkers and potential liquid biopsy on LN.

ORC6, a novel prognostic biomarker, correlates with T regulatory cell infiltration in prostate adenocarcinoma: a pan-cancer analysis

BACKGROUND

The origin recognition complex (ORC), a six-subunit DNA-binding complex, participates in DNA replication in cancer cells. Specifically in prostate cancers, ORC participates the androgen receptor (AR) regulated genomic amplification and tumor proliferation throughout the entire cell cycle. Of note, ORC6, the smallest subunit of ORC, has been reported to be dysregulated in some types of cancers (including prostate cancer), however, its prognostic and immunological significances remain yet to be elucidated.

METHODS

In the current study, we comprehensively investigated the potential prognostic and immunological role of ORC6 in 33 human tumors using multiple databases, such as TCGA, Genotype-Tissue Expression, CCLE, UCSC Xena, cBioPortal, Human Protein Atlas, GeneCards, STRING, MSigDB, TISIDB, and TIMER2 databases.

RESULTS

ORC6 expression was significantly upregulated in 29 types of cancers compared to the corresponding normal adjacent tissues. ORC6 overexpression correlated with higher stage and worse prognostic outcomes in most cancer types analyzed. Additionally, ORC6 was involved in the cell cycle pathway, DNA replication, and mismatch repair pathways in most tumor types. A negative correlation was observed between the tumor endothelial cell infiltration and ORC6 expression in almost all tumors, whereas the immune infiltration of T regulatory cell was noted to be statistically positively correlated with the expression of ORC6 in prostate cancer tissues. Furthermore, in most tumor types, immunosuppression-related genes, especially TGFBR1 and PD-L1 (CD274), exhibited a specific correlation with the expression of ORC6.

CONCLUSIONS

This comprehensive pan-cancer analysis revealed that ORC6 expression serves as a prognostic biomarker and that ORC6 is involved in the regulation of various biological pathways, the tumor microenvironment, and the immunosuppression status in several human cancers, suggesting its potential diagnostic, prognostic, and therapeutic value in pan-cancer, especially in prostate adenocarcinoma.

Combination of Hepatitis B Virus Pre-S2 Gene Deletion Mutation and Tumor-Node-Metastasis Stage Predicts Higher Hepatocellular Carcinoma Recurrence after Curative Surgical Resection

Hepatocellular carcinoma (HCC) is one of the most frequent and life-threatening human cancers worldwide. Despite curative resection surgery, the high recurrence rate of HCC leads to poor patient survival. Chronic hepatitis B virus (HBV) infection is a major etiological factor for HCC. HBV pre-S2 gene deletion mutation leads to the expression of an important oncoprotein called a pre-S2 mutant. It represents an independent prognostic biomarker for HCC recurrence. This study aimed to identify other independent prognostic biomarkers from clinicopathological characteristics of 75 HBV-related HCC patients receiving resection surgery and to validate their potential to be combined with pre-S2 gene deletion mutation as a combination biomarker for HCC recurrence. Patients with both the presence of pre-S2 gene deletion mutation and tumor-node-metastasis (TNM) stage IIIA–IIIC had a higher HCC recurrence risk than patients with either one or none of these two factors. Moreover, the combination of pre-S2 gene deletion mutation and TNM stage exhibited better performance than either of these two factors alone in discriminating patients from patients without HCC recurrence. Collectively, this study proposed that the TNM stage held significance as a combination biomarker with pre-S2 gene deletion mutation with a greater performance in predicting HCC recurrence after curative surgical resection.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Long non-coding RNAs (lncRNAs) in hepatocellular carcinoma progression: Biological functions and new therapeutic targets

Liver is an important organ in body that performs vital functions such as detoxification. Liver is susceptible to development of cancers, and hepatocellular carcinoma (HCC) is among them. 75-85% of liver cancer cases are related to HCC. Therefore, much attention has been directed towards understanding factors mediating HCC progression. LncRNAs are epigenetic factors with more than 200 nucleotides in length located in both nucleus and cytoplasm and they are promising candidates in cancer therapy. Directing studies towards understanding function of lncRNAs in HCC is of importance. LncRNAs regulate cell cycle progression and growth of HCC cells, and they can also induce/inhibit apoptosis in tumor cells. LncRNAs affect invasion and metastasis in HCC mainly by epithelial-mesenchymal transition (EMT) mechanism. Revealing the association between lncRNAs and downstream signaling pathways in HCC is discussed in the current manuscript. Infectious diseases can affect lncRNA expression in mediating HCC development and then, altered expression level of lncRNA is associated with drug resistance and radio-resistance. Biomarker application of lncRNAs and their role in prognosis and diagnosis of HCC are also discussed to pave the way for treatment of HCC patients.

Identification of m6A- and ferroptosis-related lncRNA signature for predicting immune efficacy in hepatocellular carcinoma

Background

N6-methyladenosine (m6A) methylation and ferroptosis assist long noncoding RNAs (lncRNAs) in promoting immune escape in hepatocellular carcinoma (HCC). However, the predictive value of m6A- and ferroptosis-related lncRNAs (mfrlncRNAs) in terms of immune efficacy remains unknown.

Method

A total of 365 HCC patients with complete data from The Cancer Genome Atlas (TCGA) database were used as the training cohort, and half of them were randomly selected as the validation cohort. A total of 161 HCC patients from the International Cancer Genome Consortium (ICGC) database were used as external validation (ICGC cohort).

Results

We first identified a group of specific lncRNAs associated with both m6A regulators and ferroptosis-related genes and then constructed prognosis-related mfrlncRNA pairs. Based on this, the mfrlncRNA signature was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Notably, the risk score of patients was proven to be an independent prognostic factor and was better than the TNM stage and tumor grade. Moreover, patients with high-risk scores had lower survival rates, higher infiltration of immunosuppressive cells (macrophages and Tregs), lower infiltration of cytotoxic immune cells (natural killer cells), poorer immune efficacy (both immunophenoscore and score of tumor immune dysfunction and exclusion), higher IC₅₀, and enrichment of the induced Treg pathway, which confirmed that the mfrlncRNA signature contributed to survival prediction and risk stratification of patients with HCC.

Conclusions

The mfrlncRNA signature, which has great prognostic value, provides new clues for identifying “cold” and “hot” tumors and might have crucial implications for individualized therapy to improve the survival rate of patients with HCC.

The Pyroptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular Carcinoma

Pyroptosis was recently demonstrated to be an inflammatory form of gasdermin-regulated programmed cell death characterized by cellular lysis and the release of several proinflammatory factors and participates in tumorigenesis. However, the effects of pyroptosis-related long noncoding RNAs (lncRNAs) on hepatocellular carcinoma (HCC) have not yet been completely elucidated. Based on the regression coefficients of ZFPM2-AS1, KDM4A-AS1, LUCAT1, NRAV, CRYZL2P-SEC16B, AL031985.3, SNHG4, AL049840.5, AC008549.1, MKLN1-AS, AC099850.3, and LINC01224, HCC patients were classified into a low- or high-risk group. The high-risk score according to pyroptosis-related lncRNA signature was significantly associated with poor overall survival even after adjusting for age and clinical stage. Receiver operating characteristic curves and principal component analysis further supported the accuracy of the model. Our study revealed that a higher pyroptosis-related lncRNA risk score was significantly associated with tumor staging, pathological grade, and tumor-node-metastasis stages. The nomogram incorporating the pyroptosis-related lncRNA risk score and clinicopathological factors demonstrated good accuracy. Furthermore, we observed distinct tumor microenvironment cell infiltration characteristics between high- and low-risk tumors. Notably, based on the risk model, we found that the risk score is closely related to the expression of immune checkpoint genes, immune subtypes of tumors, and the sensitivity of HCC to chemotherapy drugs and immunotherapy. In conclusion, our novel risk score of pyroptosis-related lncRNA can serve as a promising prognostic biomarker for HCC patients and provide help for HCC patients to guide precision drug treatment and immunotherapy.