Given the pivotal role that ECM components play in tumour onset and progression, a comprehensive understanding of their biophysical and biochemical effects and remodelling processes seems particularly imperative to uncover promising biomarkers for diagnostic and therapeutic applications. Database mining using various algorithms and accurate prediction models in the context of big data are of great help in the discovery of lncRNAs that are relevant to ECM. Therefore, this study identified ECMrlncRNAs to provide a prognostic prediction approach for various risk populations to rationally guide clinical decision-making.
Despite the existence of several valuable signatures for the prognostic analysis of HCC patients, [19,20,21,22,23,24,25,26,27,28], our study differs from previous studies based on various features, such as the prognostic value of the signature, the ability to accurately differentiate samples and the correlation with immune landscape. The principal cause of this difference was based on our selection of a lncRNA set that was coexpressed with ECM-related genes. Two signatures consisting of ECM-related genes have been previously reported [24, 25]; however, these signatures were not equivalent to our lncRNA signature. Indeed, comprehensive studies were performed to evaluate the prognostic value of the models generated using these signatures, and these signatures also predicted the immune landscape as well as drug sensitivity. In contrast to the study by Tang et al., our study focused on assessing the differences in drug sensitivity to sorafenib in the risk subgroup obtained from LIHC samples. Given that sorafenib is currently the first-line drug for HCC treatment, it would be more clinically useful to construct a model that demonstrates a better association with this drug. More importantly, following a series of screening processes according to the developed criteria, two lncRNAs, AL031985.3 and MKLN1-AS, were eventually identified and incorporated into the signature. We subsequently validated these two lncRNAs at the cellular and tissue levels and further examined their prognostic value in HCC. Thus, our findings are more reliable and valuable. The upregulation of AL031985.3 and MKLN1-AS in HCC was identified through database mining but was also confirmed in a series of in vitro experiments. Few experimental studies have been performed on AL031985.3, and its function in HCC requires further exploration. Comparatively, several recent studies demonstrated phenotypic alterations mediated by MKLN1-AS. MKLN1-AS is potentially involved in the regulation of cell proliferation, angiogenesis, migration and invasion [26,27,28] and implicated in poor survival [28]. Mechanistically, MKLN1-AS may function as a competing endogenous RNA to induce pro-oncogenic effects during HCC progression [28]. Overall, the signature had favourable prognostic performance, as evidenced by the ROC and corresponding AUC values at 1, 3 and 5 years. Accordingly, we divided HCC populations into high- and low-risk groups based on the risk score cut-off value. Our signature was also applicable to HCC patients with specific clinicopathological characteristics. Patients with lower risk scores generally had a better prognosis, regardless of stratification by age, sex, grade or TNM stage. In addition, the DEGs identified between the two risk groups were then entered into the GO analysis, and these genes had robust associations with ECM and immune functions.
Considering the poorly characterized immune microenvironment in HCC [29], the risk score determined in this study is a valuable marker in the differentiation of populations, particularly with respect to tumour-infiltrating immune cells, immune-related functions and immune checkpoint genes. We noted that the high-risk group was generally accompanied by a significant enrichment of immune cells associated with more immune functions and, more importantly, positively correlated with the expression level of immune checkpoint genes. Here, we identified DCs, Tregs, macrophages and CAFs as four hallmark immune cells given their remarkable abundance in the high-risk set. DCs in the tumour microenvironment (TME) may be associated with T-cell dysfunction through mediator release or checkpoint ligand expression, a mechanism by which tumours cooperate with their microenvironment to eclipse immune surveillance [30, 31]. Specifically, in HCC, Treg cells can be recruited into tumour tissues, and this accumulation is mainly mediated by the chemokine CCL22, which is secreted by intratumoural DCs [32]. Tumour-infiltrating DCs and Tregs promote immunotolerance and immune escape by suppressing effector T-cell responses, but high intratumoral numbers of these immune cells may be associated with poor prognosis [32, 33]. This notion is closely linked to our finding that increased levels of DCs and Tregs accumulated in the high-risk group (a high risk score was associated with a negative impact on clinical outcomes). Moreover, macrophages also showed increased enrichment in the high-risk group. The characterized enrichment of macrophages is notably linked to the survival inferiority of LIHC [30], indicating that this type of tumour-infiltrating macrophage can serve as a potential candidate cell for cancer therapies. Cancer-associated fibroblasts (CAFs) are also key players in the pathogenesis of HCC. In the HCC TME, CAF-derived CLCF1 enhances the secretion of the chemokines CXCL6 and TGF-β in HCC cells, which are subsequently capable of activating the ERK 1/2 signalling pathway in CAFs and stimulating the increased production of CLCF1. This positive feedback mechanism undoubtedly accelerates HCC progression and predicts a poor prognosis [34]. Additional findings also revealed a positive feedback loop between CAFs and the FOXQ1/NDRG1 axis that drives the initiation of HCC in tumour cells [35]. Furthermore, loss of CAF-derived exosomal miR-320a may partly contribute to HCC tumour proliferation and metastasis [36]. These observations objectively confirmed our assumption that high-risk HCC patients might have an advanced cancer stage with reduced survival probability. NK cells were present at significantly decreased levels in high-risk samples in our analysis, and these cells were also decreased in HCC tissues [37]. Of note, the number of intrahepatic NK cells is positively associated with poor clinical outcomes [38]. In addition to the abovementioned immune cells, differential expression of all included immune checkpoint genes was observed between the two groups. On the one hand, our results demonstrated that the accuracy and importance of patient clustering based on this criterion (risk score) is self-evident. In addition, the differential expression levels of these genes in the two risk sets revealed insight into the precise management of HCC. The most prominent immune checkpoint gene is cytotoxic T lymphocyte protein 4 (CTLA-4), which mediates immunosuppression and inhibits T-cell proliferation and the release of cytotoxic mediators [39,40,41,42]. Furthermore, it is also particularly important to perform correlation analysis between our models and chemokines and cytokines. Chemokines comprise a large family of at least 47 structurally related small cytokines, and their interaction with the extracellular matrix is specifically essential in controlling the directional migration of cells. In contrast, cytokines are key mediators of cellular communication in the TME and are closely associated with tumour development, progression and metastasis as well as the response to tumour therapy [42]. Overall, analysis of LIHC samples revealed underlying migration patterns of immune cells and regulatory mechanisms of immune surveillance. Given their crucial role in the regulation of immune responses, infiltrating immune cells, immune checkpoint genes, chemokines and cytokines represent attractive targets for tumour immunotherapy.
A critical and novel project introduced in this study was the evaluation of the correlation between TMB and the model. ICIs have revolutionized the field of cancer management by providing a new paradigm. Several attempts have been made to determine valuable predictive biomarkers, the most intriguing of which is TMB [17]. In this study, we examined the mutation frequencies of the top genes and focused on the survival probability of patients based on various groupings. Through our comparative analysis of TMB in different risk groups along with exploration of differences in the expression levels of immune checkpoint genes, precise and effective management of ICIs in HCC treatment will be possible. Moreover, the findings of the drug sensitivity analysis of chemotherapeutic agents, targeted agents and immunotherapeutic agents also provide a reference for the rational choice of HCC treatment. More importantly, regarding the evaluation and prediction of immunotherapy efficacy, researchers found that the TIDE score was the best predictor of immune checkpoint inhibitor therapy compared to all candidate biomarkers as well as two single indicators of dysfunction and rejection as reported in the literature [18]. Based on the scoring results presented here, the high TIDE scores in the high-risk group strongly indicated that an increase in risk was also accompanied by increased resistance to immunotherapy to the extent that it might be associated with lower patient survival under ICI treatment.
However, some limitations associated with this study should be noted. First, all analyses were performed by mining public databases. Although we validated of the expression levels of lncRNAs associated with model construction, studies on their mechanistic roles and associated phenotypes are lacking. Moreover, practical application of our model remains an issue that needs to be addressed, emphasizing the desperate need for in-depth work in the real world.