Identification and validation of a ferroptosis-related long non-coding RNA signature as a prognostic biomarker for hepatocellular carcinoma

Introduction

Primary liver carcinomas are among the most common malignancies worldwide and contribute significantly to cancer-related deaths (1). Despite significant therapeutic advances in recent decades, the 5-year overall survival (OS) rate of hepatocellular carcinoma (HCC) patients has not improved significantly (2,3). Conventional HCC biomarkers, such as alpha-fetoprotein (AFP), osteopontin, glypican-3, and Des-γ-carboxy prothrombin, exhibit limited diagnostic accuracy due to suboptimal sensitivity and specificity (4). Thus, further insight into the molecular biology of HCC is urgently needed to personalize treatment and improve prognosis.

Ferroptosis, an iron-dependent form of regulated cell death, differs biochemically, genetically, and morphologically from other forms of cell death, such as necroptosis, apoptosis, and autophagy (5,6). It is driven by the lethal accumulation of lipid peroxidation (7). Recent research suggests that ferroptosis plays a role in the progression of various malignancies (8-10). According to Zhang et al. (11), long non-coding RNA (lncRNA)-HEPFAL (Hepatic Fibrosis AssociatedlncRNA) promotes ferroptosis, and inhibits tumor progression and metastasis by downregulating the expression of solute carrier family 7 member 11 (SLC7A11), an oncogene. Moreover, ferroptosis-related long non-coding RNAs (FRlncRNAs) have been found to be closely related to the prognosis of several cancers. For example, Zheng et al. (12) established a 12-FRlncRNA prognostic signature for gastric cancer, while Mao et al. (13) established a 13-FRlncRNA prognostic signature for lung adenocarcinoma.

In the present study, we developed a novel FRlncRNA signature based on data from The Cancer Genome Atlas (TCGA) database. We assessed the prognostic role of this signature and its correlation with the clinical characteristics of HCC patients. Additionally, we constructed a nomogram to predict patient prognosis. Further, we explored the relationship between the signature and immune function. Finally, we investigated the underlying molecular mechanisms of the FRlncRNA signature in HCC using a gene set enrichment analysis (GSEA). Among the seven FRlncRNAs in the signature, LINC01063 was validated as an oncogene. We present this article in accordance with the ARRIVE and TRIPOD reporting checklists (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-360/rc).

Methods

Patients data

The transcriptome sequencing and clinical data of 365 HCC patients were obtained from the TCGA database (available online: https://cdn.amegroups.cn/static/public/jgo-2025-360-1.xlsx). Only patients with complete clinical and transcriptomic data were included in the study. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of FRlncRNAs

Using the FerrDb database, 382 ferroptosis-related genes (FRGs) were identified. A Pearson’s correlation co-expression analysis of the FRGs and lncRNAs was performed, and FRlncRNAs were identified based on the following selection criteria: |R|>0.3 and P<0.05.

Construction and validation of a prognostic FRlncRNA signature

The cohort was randomly divided into training (n=184) and testing (n=181) datasets. A univariate Cox regression analysis was conducted to identify the lncRNAs associated with OS. Least absolute shrinkage and selection operator (LASSO) regression was used to construct the final prognostic model. The risk score was calculated as follows:

Riskscore=∑i=1n(Expi∗Coei)[1]

where Expi represents the expression degree, and Coei represents the regression parameter of every FRlncRNA.

The patients were classified into high-risk (HR) and low-risk (LR) groups based on the median risk score. Kaplan-Meier (KM) survival analysis and time-dependent receiver operating characteristic (ROC) curves were used to assess the performance of the model in both cohorts.

Construction of the nomogram model

A nomogram was constructed that incorporated clinical factors and the risk score to predict 1-, 3-, and 5-year OS. Calibration curves were used to evaluate the predictive accuracy of the nomogram.

Immune infiltration and functional enrichment analysis

The immune cell infiltration levels were analyzed by a single-sample gene set enrichment analysis (ssGSEA), and the related immune pathways and checkpoints were compared between the HR and LR groups. A GSEA was conducted to identify the enriched pathways associated with the FRlncRNAs.

Cell Counting Kit-8 (CCK-8) assay

To estimate cell viability, the CCK-8 assay was used in accordance with the manufacturer’s instructions. The HCC cells were seeded into 96-well plates at a density of 1×10⁴ cells per well. After the required incubation period, CCK-8 reagent was added to each well. Optical density was measured at 450 nm using a microplate reader to assess cell viability.

Colony formation assay

HCC cells (approximately 800 per well) were seeded into six-well plates and incubated for 2 weeks to allow colony formation. The colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. The stained colonies were counted and imaged to evaluate clonogenic potential.

Transwell assay

The migration and invasion abilities of the HCC cells were assessed by transwell assay. For the invasion assays, the chambers were pre-coated with Matrigel. The cells were seeded into the upper chamber, while the lower chamber contained a chemoattractant medium. After 48 hours of incubation, the non-migrated or non-invaded cells were removed. The migrated or invaded cells were fixed with paraformaldehyde, stained with 0.1% crystal violet, and imaged under a microscope.

Animal experiments

Nude BALB/c mice from Gemparmatech were subcutaneously injected with 5×10⁶ LINC01063-knockdown or control HCC cells. Tumor growth was monitored, and the tumor volume was calculated using the following formula: tumor volume = (length × width²)/2. After 28 days, the mice were sacrificed, and the tumors were excised for further analysis. All animal experiments were approved by the Animal Care Committee of the First Affiliated Hospital of Nanjing Medical University (No. IACUC-2201039), in compliance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. A protocol was prepared pre-study but not registered.

Statistical analysis

Statistical analyses were performed using Perl tools and R software (version 4.2.1). The Chi-squared test was used to compare clinicopathological characteristics [e.g., age, gender, tumor-node-metastasis (TNM) stage] between the training and testing cohorts. The Mann-Whitney U test was employed to analyze differences in survival time and continuous variables across cohorts. Survival curves were generated using the Kaplan-Meier method, and statistical significance was assessed via the log-rank test. The correlation between ferroptosis-related lncRNA expression and immune cell infiltration levels was evaluated using Spearman correlation analysis. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated to validate the prognostic signature’s predictive accuracy. For all analyses, a two-sided P<0.05 was considered statistically significant.

Results

Identification of FRlncRNAs in HCC

TCGA HCC cohort comprised 382 FRGs and 14,178 lncRNAs. Using a co-expression analysis, the lncRNAs closely related to the FRGs are shown in https://cdn.amegroups.cn/static/public/jgo-2025-360-2.xlsx.

Establishment and validation of a prognostic FRlncRNAs signature

In the training cohort (n=184), the univariate Cox regression analysis identified 16 potentially prognostic FRlncRNAs in HCC (Figure 1A). The differential expression analysis revealed that 14 of these FRlncRNAs were significantly differently expressed in the tumor tissues compared to the adjacent normal tissues (Figure 1B,1C). The LASSO regression identified seven FRlncRNAs (Figure 1D,1E), which were used to construct the final prognostic model.

Figure 1 Selection of FRlncRNAs using univariate Cox and LASSO regression analyses. (A) Forest plot showing the prognostic FRlncRNAs. (B,C) Identification of 16 prognostic FRlncRNAs in the HCC tissues. (D,E) LASSO-penalized Cox regression analysis. *, P<0.05; ***, P<0.001. FRlncRNAs, ferroptosis-related long non-coding RNAs; HCC, hepatocellular carcinoma; LASSO, least absolute shrinkage and selection operator.

The univariate survival analysis of the training cohort showed that the patients in the HR group had a significantly shorter OS than those in the LR group (P<0.001) (Figure 2A). Figure 2B,2C show the higher mortality and risk scores of the HR group compared to the LR group. The seven FRlncRNAs were all highly expressed in the high-risk HCC group (Figure 2D). The risk score exhibited robust predictive accuracy, outperforming available, conventional clinical factors (Figure 2E,2F). Validation in the testing cohort yielded consistent results. Figure 2G-2L show the results of the univariate survival analysis, survival outcomes, risk distribution, expression of the seven FRlncRNAs, and time-dependent ROC analysis in the testing cohort.

Figure 2 Construction and validation of the prognostic FRlncRNA signature. (A) Survival curve of HCC patients based on the risk score in the training cohort. (B) Distribution of the risk scores in the training cohort. (C) Distribution of survival time in the training cohort. (D) Heatmap showing the expression levels of the seven prognostic FRlncRNAs between the HR and LR groups. (E,F) ROC curve analysis of the signature in the training cohort. (G) Survival curve of the HCC patients based on the risk score in the testing cohort. (H) Distribution of the risk scores in the testing cohort. (I) Distribution of survival time in the testing cohort. (J) The heatmap of the 7 FRlncRNAs between the two groups. (K,L) ROC curve analysis of the signature in the testing cohort. AUC, area under the curve; FRlncRNA, ferroptosis-related long non-coding RNA; HCC, hepatocellular carcinoma; HR, high-risk; LR, low-risk; ROC, receiver operating characteristic.

Independent prognostic value of the FRlncRNA signature in predicting the OS of HCC patients

Univariate and multivariate Cox regression analyses were performed in both the training and testing cohorts to evaluate whether the risk score was an independent prognostic factor. A HR score and an advanced stage were identified as unfavorable prognostic factors in the training cohort (Figure 3A), and this finding was validated in the testing cohort (Figure 3B). The multivariate regression analysis further confirmed that a HR score was an independent adverse prognostic factor for HCC in both cohorts (Figure 3C,3D). To further investigate the applicability of the FRlncRNA signature, we performed a stratification analysis based on clinical characteristics. Compared with the low-risk group, the high-risk group exhibited poorer OS across most subgroups, including age (≤65 vs. >65 years), sex (female vs. male), T stage (T1–2 vs. T3–4), N stage (N0 vs. N1), grade (G1–2 vs. G3–4), and TNM stage (I–II vs. III–IV). However, no significant differences in OS were observed in the female, N1, M1, and G3–4 subgroups (Figure 3E-3J).

Figure 3 Correlations between different clinicopathological characteristics of the HCC patients and the risk score. (A-D) The forest plots of univariate (A) and multivariate (C) Cox regression analyses in the training cohorts, the forest plots of univariate (B) and multivariate (D) Cox regression analyses in the testing cohorts. (E-J) Clinical stratification analysis of OS in HCC patients based on risk scores, stratified by age (E), gender (F), grade (G), N stage (H), TNM stage (I), and T stage (J). HCC, hepatocellular carcinoma; N stage, nodal stage; OS, overall survival; T stage, tumor stage; TNM stage, tumor-node-metastasis stage.

The establishment of a prognostic nomogram

To enhance the survival prediction of the HCC patients, an OS nomogram was developed based on the patients’ clinical characteristics and risk score (Figure 4A). Calibration plots confirmed the accuracy and predictive capacity of the nomogram (Figure 4B).

Figure 4 Construction and assessment of a clinical prognostic nomogram. (A) Nomogram for predicting 1-, 3-, and 5-year OS probabilities. (B) Calibration plot for the nomogram. *, P<0.05. N, node; M, metastasis; OS, overall survival; T, tumor.

Assessment of the immune-infiltrating condition of the FRlncRNA signature

An analysis was conducted to explore the correlation between the immune status of the HCC patients and the FRlncRNA signature. The HR patients exhibited statistically significantly greater infiltration of immune cells, such as B cells, CD4⁺T cells, neutrophils, and macrophages (Figure 5A). The ssGSEA revealed significant differences in the cytolytic activity and type II interferon (IFN) response between the LR and HR groups (Figure 5B). Additionally, the HR patients had elevated expression levels of immune checkpoint genes (Figure 5C).

Figure 5 Immune-related characteristics in the LR and HR groups. (A) Heatmap of immune cell infiltration in the LR and HR groups. (B) Immune function differences based on the ssGSEA. (C) Expression levels of immune checkpoints between the LR and HR groups. *, P<0.05; **, P<0.01; ***, P<0.001; ns, not significant. APC, antigen presenting cell; CCR, chemokine receptor; HLA, human leukocyte antigen; HR, high-risk; LR, low-risk; MHC, major histocompatibility complex; ssGSEA, single-sample gene set enrichment analysis.

Functional enrichment analysis of the FRlncRNAs signature

To investigate the molecular mechanisms underlying the FRlncRNA signature in HCC, a GSEA was performed. The results revealed that several signaling pathways were enriched in the HR group (Figure 6A-6F), indicating that the signature was closely associated with HCC development.

Figure 6 Enriched pathways in the HR group identified using the signature. (A-F) Significantly enriched KEGG pathways in the HR group. HR, high-risk; KEGG, Kyoto Encyclopedia of Genes and Genomes.

LINC01063 knockdown suppresses HCC progression

Functional experiments were conducted to assess the role of LINC01063. The knockdown of LINC01063 significantly inhibited cell proliferation (Figure 7A) and disrupted colony formation ability of HCC cells (Figure 7B,7C). Additionally, the migration and invasion capacities of the HCC cells were significantly reduced following the knockdown of LINC01063 (Figure 7D,7E).

Figure 7 LINC01063 facilitates HCC progression. (A) LINC01063 expression was measured in LINC01063-knockdown (sh-LINC01063) HCC cells and their negative control (sh-NC) cells using qRT-PCR. (B,C) CCK-8 (B) and colony formation (C) assays were used to assess the cell proliferation ability of the cells in the indicated groups (original magnification, 1×, crystal violet-stained). (D,E) Transwell assay was used to verify the effect of knockdown of LINC01063 on the migration (D) and invasion (E) abilities of the HCC cells (original magnification, 20×, crystal violet-stained). (F) Xenograft assay was used to examine the effect of LINC01063 knockdown on HCC growth in vivo. *, P<0.05; **, P<0.01; ***, P<0.001. CCK-8, Cell Counting Kit-8; HCC, hepatocellular carcinoma; NC, negative control; OD, optical density; qRT-PCR, quantitative reverse transcription polymerase chain reaction.

The in vivo experiments results further supported these findings. Specifically, the nude BALB/c mice injected with the LINC01063-knockdown HCC cells exhibited reduced tumor growth compared to the controls (Figure 7F). Together, these results indicate that LINC01063 functions as an oncogene in HCC progression.

Discussion

LncRNAs have been recognized as pivotal regulators in the progression of various cancers, including HCC (14,15). There is increasing evidence that lncRNAs modulate cancer progression by influencing key cellular processes, such as ferroptosis, a unique form of iron-dependent cell death. Ferroptosis has been implicated in tumor suppression, and its regulation by lncRNAs provides an emerging avenue for cancer therapeutics. For example, Zhang et al. (16) showed that nuclear paraspeckle assembly transcript 1 (NEAT1), an upregulated lncRNA in HCC, promotes ferroptosis, highlighting its potential as a therapeutic target. While Kang et al. (17) showed that LINC01134 modulates the expression of GPX4 (Glutathione Peroxidase 4), a crucial inhibitor of ferroptosis, thereby contributing to oxaliplatin resistance in HCC. Despite these insights, the prognostic implications of FRlncRNA in HCC remain largely unexplored (18-20).

In this study, we aimed to construct a robust prognostic model based on FRlncRNAs to predict the outcomes of HCC patients and uncover potential therapeutic targets. Using a univariate Cox regression analysis, we found that several FRlncRNAs were significantly associated with patient prognosis. Through a subsequent LASSO regression analysis, we established a seven-lncRNA signature comprising AC114488.1, AC068506.1, AP003469.2, PIK3CD-AS2, AC108752.1, RHPN1-AS1, and LINC01063. The KM survival analysis revealed that the patients in the HR group had a significantly shorter OS than those in the LR group in both the training and testing cohorts. Moreover, the regression analyses confirmed the independence of the risk score as a prognostic factor when assessed together with available clinical and pathological variables. A nomogram was developed to integrate the risk score with clinicopathological parameters, and it demonstrated excellent accuracy in predicting the 1-, 3-, and 5-year OS of the HCC patients. Stratified analyses further validated the stability and reliability of the signature across diverse clinical subgroups.

The tumor immune microenvironment plays a critical role in HCC progression and the therapeutic response (21). Our study found that the HR patients exhibited greater infiltration of several types of immune cells. Further, the ssGSEA revealed significant differences in immune-related functions such as cytolytic activity and the type II interferon response between the HR and LR groups. Notably, the HR group had elevated expression levels of immune checkpoint genes, including CD86, TNFSF9, and CTLA4, indicating the potential for immunotherapeutic interventions in this subgroup.

To further elucidate the molecular mechanisms underlying the prognostic signature, we performed a GSEA. The HR patients exhibited enrichment in pathways related to the cell cycle, DNA replication, Notch signaling, mismatch repair, p53 signaling, and mechanistic target of rapamycin (mTOR) signaling. These pathways are well-documented contributors to HCC pathogenesis and progression (22-26). For example, the Notch signaling pathway is associated with enhanced tumor growth and metastasis, while the p53 signaling pathway is a key regulator of cellular apoptosis and genomic stability. The enrichment of these pathways in HR patients provides insights into potential therapeutic targets for this subgroup of patients.

Among the seven lncRNAs in the prognostic signature, LINC01063 emerged as a key regulator. Consistent with our findings, LINC01063 has been previously reported to promote melanoma progression via the miR-5194/SOX12 axis (27). In the present study, we confirmed that LINC01063 was significantly upregulated in HCC tissues and facilitates tumor progression through in vitro and in vivo experiments. The knockdown of LINC01063 inhibited cell proliferation, colony formation, migration, and invasion in the HCC cells. In vivo, the knockdown of LINC01063 significantly suppressed tumor growth in a murine xenograft model, underscoring its role as an oncogenic driver in HCC.

Conclusions

Our study provides compelling evidence that FRlncRNAs are closely associated with HCC progression, tumor immunity, and patient prognosis. Our seven-FRlncRNA signature has significant potential in the clinical application of risk stratification and personalized treatment planning. Further, the immune landscape and enriched signaling pathways identified in this study provide valuable insights into the underlying mechanisms of HCC and highlight opportunities for targeted therapeutic interventions. Future research should focus on validating these findings in larger, independent cohorts, and exploring the therapeutic potential of targeting key lncRNAs, such as LINC01063. Integrating multi-omics data may further refine our understanding of FRlncRNA-mediated regulation in HCC, paving the way for novel therapeutic strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the ARRIVE and TRIPOD reporting checklists. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-360/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-360/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-360/prf

Funding: This research was supported by the Natural Science Foundation of Jiangsu Province (No. BK20210031).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-360/coif). M.L. reports unrestricted research funding from Scandion Oncology A/S, Copenhagen, Denmark, and is advisory board member of Alivia AB, Stockholm, Sweden. The other authors have no conflicts of interest to declare.

Ethical Statement:The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All animal experiments were approved by the Animal Care Committee of the First Affiliated Hospital of Nanjing Medical University (No. IACUC-2201039), in compliance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Lin Q, Wu Z, Yue X, et al. ZHX2 restricts hepatocellular carcinoma by suppressing stem cell-like traits through KDM2A-mediated H3K36 demethylation. EBioMedicine 2020;53:102676. [Crossref] [PubMed]
3. Wang X, Qian J, Yao N, et al. Identification of metabolism-related long non-coding RNA (lncRNA) signature predicts prognosis and immune infiltrates in hepatocellular carcinoma. Ann Transl Med 2022;10:595. [Crossref] [PubMed]
4. Huang E, Ma T, Zhou J, et al. The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma. Ann Transl Med 2022;10:766. [Crossref] [PubMed]
5. Dixon SJ, Lemberg KM, Lamprecht MR, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 2012;149:1060-72. [Crossref] [PubMed]
6. Stockwell BR, Friedmann Angeli JP, Bayir H, et al. Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell 2017;171:273-85. [Crossref] [PubMed]
7. Schwabe RF, Greten TF. Gut microbiome in HCC - Mechanisms, diagnosis and therapy. J Hepatol 2020;72:230-8. [Crossref] [PubMed]
8. Fang W, Liu J, Zhang F, et al. A novel cholesterol metabolism-related ferroptosis pathway in hepatocellular carcinoma. Discov Oncol 2024;15:7. [Crossref] [PubMed]
9. Xie Y, Wang B, Zhao Y, et al. Mammary adipocytes protect triple-negative breast cancer cells from ferroptosis. J Hematol Oncol 2022;15:72. [Crossref] [PubMed]
10. Yang Z, Zou S, Zhang Y, et al. ACTL6A protects gastric cancer cells against ferroptosis through induction of glutathione synthesis. Nat Commun 2023;14:4193. [Crossref] [PubMed]
11. Zhang B, Bao W, Zhang S, et al. LncRNA HEPFAL accelerates ferroptosis in hepatocellular carcinoma by regulating SLC7A11 ubiquitination. Cell Death Dis 2022;13:734. [Crossref] [PubMed]
12. Zheng Y, Wu S, Huang X, et al. Ferroptosis-Related lncRNAs Act as Novel Prognostic Biomarkers in the Gastric Adenocarcinoma Microenvironment, Immunotherapy, and Chemotherapy. Oxid Med Cell Longev 2023;2023:9598783. [Crossref] [PubMed]
13. Mao K, Tang R, Wu Y, et al. Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas. Front Genet 2023;14:1118273. [Crossref] [PubMed]
14. Xu W, Chen L, Liu J, et al. LINC00152 induced by TGF-β promotes metastasis via HuR in lung adenocarcinoma. Cell Death Dis 2022;13:772. [Crossref] [PubMed]
15. Zhou L, Jiang J, Huang Z, et al. Hypoxia-induced lncRNA STEAP3-AS1 activates Wnt/β-catenin signaling to promote colorectal cancer progression by preventing m(6)A-mediated degradation of STEAP3 mRNA. Mol Cancer 2022;21:168. [Crossref] [PubMed]
16. Zhang Y, Luo M, Cui X, et al. Long noncoding RNA NEAT1 promotes ferroptosis by modulating the miR-362-3p/MIOX axis as a ceRNA. Cell Death Differ 2022;29:1850-63. [Crossref] [PubMed]
17. Kang X, Huo Y, Jia S, et al. Silenced LINC01134 Enhances Oxaliplatin Sensitivity by Facilitating Ferroptosis Through GPX4 in Hepatocarcinoma. Front Oncol 2022;12:939605. [Crossref] [PubMed]
18. Zheng Z, Zhang Q, Wu W, et al. Identification and Validation of a Ferroptosis-Related Long Non-coding RNA Signature for Predicting the Outcome of Lung Adenocarcinoma. Front Genet 2021;12:690509. [Crossref] [PubMed]
19. Zhang K, Ping L, Du T, et al. A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer. Front Mol Biosci 2021;8:678877. [Crossref] [PubMed]
20. Tang Y, Li C, Zhang YJ, et al. Ferroptosis-Related Long Non-Coding RNA signature predicts the prognosis of Head and neck squamous cell carcinoma. Int J Biol Sci 2021;17:702-11. [Crossref] [PubMed]
21. Walker C, Mojares E, Del Río Hernández A. Role of Extracellular Matrix in Development and Cancer Progression. Int J Mol Sci 2018;19:3028. [Crossref] [PubMed]
22. Li X, Yan X, Wang Y, et al. The Notch signaling pathway: a potential target for cancer immunotherapy. J Hematol Oncol 2023;16:45. [Crossref] [PubMed]
23. Her J, Zheng H, Bunting SF. RNF4 sustains Myc-driven tumorigenesis by facilitating DNA replication. J Clin Invest 2024;134:e167419. [Crossref] [PubMed]
24. Chen M, Liu H, Liang W, et al. Mitochondrial DNA copy number plays opposing roles in T-lymphocyte infiltration of colorectal cancer based on mismatch repair status: new directions for immunotherapy? Br J Cancer 2024;130:798-807. [Crossref] [PubMed]
25. Singhal SS, Horne D, Singhal J, et al. Activating p53 function by targeting RLIP. Biochim Biophys Acta Rev Cancer 2021;1875:188512. [Crossref] [PubMed]
26. Dong Y, Gong Y, Kuo F, et al. Targeting the mTOR Pathway in Hurthle Cell Carcinoma Results in Potent Antitumor Activity. Mol Cancer Ther 2022;21:382-94. [Crossref] [PubMed]
27. Xu J, Ou R, Nie G, et al. LINC01063 functions as an oncogene in melanoma through regulation of miR-5194-mediated SOX12 expression. Melanoma Res 2022;32:218-30. [Crossref] [PubMed]

(English Language Editor: L. Huleatt)