Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top

Challenges in the clinical management of acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is a life-threatening acute inflammatory lung injury that causes hypoxaemia and bilateral pulmonary infiltrates on chest imaging that cannot be primarily attributed to volume overload, left ventricular dysfunction or chronic lung disease [1]. ARDS has been recognised as a major clinical problem; it has a mortality rate of 40–60% and remarkably high long-term morbidity [2, 3]. Worldwide, ARDS accounts for 10% of all intensive care unit (ICU) admissions, 23% of which require mechanical ventilation [3, 4]. The hospital admissions, morbidity and mortality associated with this syndrome significantly impact the global economic burden and result in high healthcare costs [5].

ARDS is a clinical entity that develops in the setting of diverse infectious and noninfectious conditions (figure 1), illustrating its pathogenetic complexity. Of the more than 50 causes associated with the development of ARDS, the most common is sepsis; it accounts for approximately 40% of cases, followed by shock, pneumonia, aspiration of gastric contents, multiple blood transfusions and trauma [4]. The role of viral infections such as severe acute respiratory syndrome coronavirus (SARS-CoV), H1N1 influenza, Middle East respiratory syndrome coronavirus and SARS-CoV-2 in the development of ARDS should also be highlighted [6].

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FIGURE 1

Underlying factors contributing to acute respiratory distress syndrome (ARDS). Diagram illustrating the pathogenetic complexity of ARDS. The most common causes are depicted. Adapted from the template “Risk Factors of Dementia” by BioRender.com (2023) (retrieved from https://app.biorender.com/biorender-templates).

Despite great advances in the field, the clinical management of ARDS remains challenging, mainly due to its enormous clinical heterogeneity. Several classifications have been proposed. In 1994, the American–European Consensus Conference defined ARDS as the most severe hypoxic subset of acute lung injury (ALI) [7]. This clinical definition was replaced in 2012 by the Berlin definition [8], which classifies ARDS into three physiological categories of severity depending on the degree of hypoxaemia, the time of onset, the origin of the oedema and the presence of diffuse bilateral opacities on chest radiographs [8, 9]. Since then, subsequent updates and adaptations of the Berlin definition of ARDS have been published, aiming to refine its clinical utility and accuracy [10, 11].

Existing strategies for the treatment of ARDS focus mainly on supportive interventions, with mechanical ventilation with low tidal volumes as the main life-saving strategy [12]. Nevertheless, mechanical ventilation may initiate or aggravate lung injury [13], leading to ventilator-induced lung injury [14]. Although it is essential to avoid injurious overdistension during mechanical ventilation to improve ARDS patient outcomes, no specific biomarkers of mechanical stretching have been described to date. Thus, adjustment of tidal volume or positive end-expiratory pressure still relies on inaccurate estimations of respiratory mechanics at the bedside rather than on the biological response of the lungs. Along these lines, it has not yet been demonstrated that the available pharmacotherapeutic options provide clear benefits regarding the risk of fatal events in patients with ARDS [15]. The use of corticosteroids, which are among the most extensively evaluated, has generated a long-standing controversy [16–18]. The timing of initiation of corticosteroid therapy may be fundamental in determining the beneficial or harmful effects of the therapy [19].

Overall, the major barrier in the management of ARDS is the heterogeneity of the syndrome; its diverse aetiologies contribute to a “common phenotype” that potentially develops due to many different underlying pathogenetic mechanisms. In a seminal study, Calfee et al. [20], using latent class analysis, identified two ARDS subphenotypes with different clinical evolutions. A hyperinflammatory subphenotype was associated with worse clinical outcomes and fewer ventilator-free days. Clinical categorisation constitutes an important framework for the management of ARDS [21] because of the differences in the biological characteristics, clinical features, therapeutic responses and risk of adverse outcomes associated with each ARDS subphenotype [22, 23].

The accumulation of prior findings underscores the urgent necessity of integrating biomarkers into the clinical decision-making arena. The use of biomarkers stands to offer invaluable insights into the intricate pathophysiological drivers of ARDS, thereby enhancing diagnostic precision, risk stratification, therapy allocation and ongoing patient monitoring. Biological markers are also crucial for identifying patient subgroups that share pathophysiological characteristics, a distinction that ultimately could be critical in the success of patient management [24].

Biomarkers useful in the clinical management of ARDS

Numerous investigations have systematically examined diverse reservoirs of molecular data with the aim of enhancing the clinical management of ARDS. These studies aim to offer new routes to precision-based care and patient phenotyping beyond the limited spectrum of biomarkers that are currently proposed for specific clinical scenarios and are routinely obtainable within clinical laboratories, such as procalcitonin [25]. The spectrum of explored biomarkers encompasses tissue degradation products, inflammatory mediators, plasma-derived markers and genetic polymorphisms [26].

Despite the advances, no commercially available biomarkers for ARDS have obtained approval from the US Food and Drug Administration (FDA) and/or the European Medicines Agency. Additional efforts are necessary to develop novel biological indicators that have potential utility in the clinical management of ARDS patients. The literature suggests the noncoding transcriptome and in particular long noncoding RNAs (lncRNAs) as an emerging reservoir of molecular information that can assist in medical decision making [27–30]. Surprisingly, the usefulness of lncRNAs in ARDS has not been comprehensively reviewed.

Here, we first focus on the current understanding of circulating lncRNAs as biomarkers. Subsequently, we offer an exhaustive overview of the latest advances in the study of lncRNAs, positioning them as pioneering tools for clinical decision making within the sphere of ARDS. Ultimately, we identify critical gaps in the progression and clinical integration of knowledge about lncRNA-based markers and propose essential considerations to guide forthcoming investigations within this domain.

Long noncoding RNAs

Over the past century, the convergence of breakthroughs in sequencing technologies and computational biology has revealed that although 80% of the human genome is transcribed, only 2% of the human genome encodes for proteins [31]. The vast majority of the genome consists of noncoding genes that contain little or no information on coding peptides [32]. An extensive body of literature offers compelling evidence that demonstrates the pivotal regulatory role of the noncoding transcriptome not only in normal development and physiology but also in the intricate pathogenic pathways associated with numerous conditions [33].

Noncoding transcripts have conventionally been classified into two categories: small ncRNAs (those <200 nucleotides in length) and long ncRNAs (those >200 nucleotides). The latter group has attracted the attention of both basic and clinical researchers. lncRNAs constitute a highly heterogeneous collection of transcripts that show considerable variation in their biogenesis, structure and function. In some cases, lncRNAs show mRNA-like properties such as splicing, polyadenylation and 7-methyl guanosine capping. In contrast, other lncRNAs are not capped or polyadenylated [34]. Compared to mRNAs, lncRNAs are generally expressed at lower levels, exhibit less sequence conservation and display greater cell and tissue specificity [35].

lncRNAs constitute a substantial portion of the noncoding transcriptome. The GENCODE database (Release 43, February 2023; www.gencodegenes.org) [36] estimates around 17 000 human transcripts and the FANTOM consortium estimates approximately 30 000 [37]. However, only a minority of lncRNAs have been functionally annotated, leaving the biological relevance of the majority of them yet to be explored. The classification of lncRNAs is primarily based on their genomic location relative to protein-coding genes. Consequently, lncRNAs can be categorised as sense, antisense, bidirectional, intergenic or intronic transcripts [33]. Some authors include circular RNAs (circRNAs) within the category of lncRNAs. circRNAs have gained significant attention in biomarker development [38]. Due to their unique covalently closed-loop structure, these ncRNAs are protected from degradation by exonucleases, resulting in a longer half-life compared to linear RNAs. circRNAs have been detected in clinical specimens and their expression exhibits dynamic regulation in response to stress and various disease states [39]. For simplicity, this group of transcripts is not discussed in the current review.

lncRNAs play crucial roles in both the nucleus and the cytoplasm. Their extensive functional repertoire encompasses the regulation of gene expression networks, including both activation and repression, by exerting control over nuclear architecture, chromatin remodelling, transcription, translation and post-translational modifications [40]. Moreover, lncRNAs serve as scaffolds for protein complexes and act as decoys for specific target molecules [41]. Recent studies have begun to unveil the fundamental roles of lncRNAs in processes such as cell proliferation and differentiation, metabolism, inflammation and immune responses [42–44]. As such, aberrant lncRNA expression is intricately linked to disease causation and progression. In this context, prior research has demonstrated the involvement of lncRNAs in the pathology of ARDS. However, this topic falls beyond the scope of the current review.

Extracellular lncRNAs as biomarkers with clinical applications

In addition to their intracellular location, lncRNAs have been identified in the extracellular space and in various body fluids [45]. The lncRNAs found in the latter locations are referred to as extracellular and/or circulating lncRNAs (for those detected in the circulation). lncRNAs appear in the extracellular milieu primarily encapsulated in extracellular vesicles, in particular exosomes and microvesicles [46], which protect these transcripts from nuclease-mediated degradation. Approximately 16 000 lncRNAs have been estimated to be present in extracellular vesicles obtained from various human biofluids, including blood, urine, bile and cerebrospinal fluid [47]. Their release, either independently or in conjunction with other typical RNA carriers such as apoptotic bodies, proteins and lipoproteins, remains a topic of debate. Mounting evidence suggests that extracellular lncRNAs play a role in cell-to-cell communication, at least at the paracrine level, influencing the phenotype of recipient cells [48].

To date, studies of extracellular ncRNAs as potential biomarkers have predominantly centred around microRNAs (miRNAs). However, lncRNAs have recently gained prominence in this arena. Given their considerable and diverse biological properties, lncRNAs can potentially provide insights into the pathogenic mechanisms associated with various conditions. Notably, their tissue- and condition-specific nature, coupled with their stability, which is primarily attributable to their protection within vesicles, renders these transcripts attractive as biomarkers. Indeed, Arita et al. [49] demonstrated the resilience of plasma lncRNAs to degradation induced by repetitive freeze‒thaw cycles and prolonged exposure to temperatures ranging from 45°C to room temperature, reaffirming their relatively high stability in the bloodstream. Despite significant limitations in their quantification (as detailed in the “Perspectives and challenges” section), extracellular lncRNAs can be measured in samples routinely processed in clinical laboratories, such as plasma, serum or urine, using accessible techniques like reverse transcription quantitative PCR (RT-qPCR). Furthermore, advances in high-throughput technologies enable the reliable detection of thousands of transcripts, facilitating the discovery phase of biomarker studies.

Extracellular lncRNAs are becoming increasingly important because they can provide objective blood-based biomarkers that are useful in medical decision making as diagnostic, prognostic and predictive indicators in several conditions, including cancer, cardiovascular disease and respiratory diseases [29, 50, 51]. A prominent example of a clinically useful lncRNA-based biomarker is PCA3 (prostate cancer antigen 3), approved by the FDA to aid in prostate cancer diagnosis and reduce the need for biopsies [52]. Circulating lncRNA-based biomarkers are currently under evaluation in clinical trials for the management of various diseases, e.g. sepsis, heart failure and long COVID (ClinicalTrials.gov: NCT04427371, NCT03268135 and NCT05672602, respectively). Several research groups are also actively developing lncRNA-based tools that have potential clinical application in the medium and long term [53].

Circulating lncRNAs as potential tools in the clinical management of ARDS

Table 1 provides a comprehensive overview of the key attributes and outcomes of studies that have explored the utility of using blood-based lncRNAs, encompassing both the cellular and cell-free components, as biomarkers for clinical decision making in ARDS.

The first evidence on the potential clinical application of circulating lncRNAs in ARDS was published in 2018 by Wan et al. [54], who analysed plasma samples from 85 patients classified as having mild, moderate or severe ARDS and 49 age- and sex-matched controls admitted to the ICU. All subjects underwent protective mechanical ventilation. The levels of lnc-IL7R (interleukin (IL)-7 receptor α subunit gene) were downregulated in ARDS patients, especially in those with severe ARDS (arbitrary units (AU): 1.396±0.15, 0.826±0.56, 0.346±0.29 and 0.056±0.07 for healthy controls, mild, moderate and severe ARDS, respectively). The lncRNA showed an optimal discriminative value for ARDS (area under the curve (AUC) 0.87 (95% CI 0.81–0.93). Supporting a plausible prognostic value, higher plasma levels of lnc-IL7R were found in survivors of ICU admission 28 days after discharge than in nonsurvivors (0.63±0.08 versus 0.33±0.08). Plasma lnc-IL7R level was also inversely correlated with indicators of ARDS severity such as Acute Physiology and Chronic Health Evaluation II (APACHE II) (ρ= −0.25) and the oxygenation index (ρ= −0.61).

One year later, Wang et al. [55] evaluated THRIL (tumour necrosis factor (TNF)-related and heterogeneous nuclear ribonucleoprotein L-related immunoregulatory lncRNA) levels in plasma from 32 sepsis-ARDS patients and 77 non-ARDS-sepsis patients admitted to the ICU. The expression of THRIL was upregulated in ARDS patients, presenting a moderate discriminative capacity (AUC 0.706 (95% CI 0.602–0.809)), sensitivity (68.7%) and specificity (71.4%). THRIL expression also correlated with increased disease severity, APACHE II (ρ=0.518) and Sequential Organ Failure Assessment (SOFA) (ρ=0.364) scores and predicted the risk of ARDS among sepsis patients compared to nonsepsis patients (OR 1.511). Furthermore, lncRNA THRIL levels were elevated in nonsurvivors compared to survivors (median (interquartile range (IQR)) relative expression 4.262 (1.983–5.582) and 1.784 (0.928–2.849), respectively), showing an interesting value for predicting mortality in sepsis patients (AUC 0.790 (95% CI 0.621–0.958)). More recently, the presumed role that THRIL plays in the development of ARDS has been corroborated. Comparison of 18 serum samples from ARDS patients in the ICU with those from 14 healthy subjects showed that THRIL expression levels were significantly upregulated in ARDS patients [56]. In addition, the level of expression of the pro-inflammatory factor IL-17 was highly increased in ARDS patients and was positively correlated with THRIL levels (ρ=0.60). These findings align with experimental data indicating an association between THRIL and the production of pro-inflammatory cytokines as well as the number of inflammatory cells in a mouse model of sepsis [57].

One of the most extensively studied lncRNAs in ARDS and other diseases is the lncRNA MALAT1 (metastasis associated lung adenocarcinoma transcript 1), also known as NEAT2 (nuclear-enriched abundant transcript 2). MALAT1 levels were first analysed in peripheral blood mononuclear cells (PBMCs) from 39 neonatal ARDS and 25 control newborns of different gestational ages who had or had not experienced infection exposure [58]. MALAT1 was upregulated in ARDS newborns who had been exposed to infection, independent of gestational age [58]. Similar findings were observed in adult ARDS patients [59]. Blood samples from 65 ARDS patients were collected within the first 24 h after the patients met the Berlin diagnostic criteria. Healthy controls (n=36) were free of lung, cardiac, infectious, allergic and chronic diseases. MALAT1 levels were found to be upregulated in plasma and in PBMCs but not in plasma exosomes [59]. The results of independent investigations are consistent with these findings. Plasma levels of MALAT1 were upregulated in 41 ICU sepsis patients with ARDS compared to 111 sepsis patients without ARDS [60]. The receiver operating characteristic (ROC) curve analysis showed that MALAT1 may identify ARDS with an AUC of 0.674 (95% CI 0.581–0.766). At the best cut-off point, the sensitivity and specificity were 65.9% and 68.5%, respectively. MALAT1 expression also correlated with disease severity: APACHE II (ρ=0.252) and SOFA (ρ=0.256) [60]. Yao et al. [61] determined the levels of this lncRNA in plasma samples of 46 hospitalised patients with ARDS and 46 age-matched controls. Control patients did not show symptoms or signs of pulmonary abnormalities (confirmed by chest radiography examination). In this population, MALAT1 expression was upregulated in the ARDS group compared to the control group. An association between MALAT1 and inflammation in other tissues has been reported. For example, in cardiomyocytes, IL-6 induces MALAT1 overexpression in response to lipopolysaccharide (LPS), and MALAT1 can enhance TNF-α expression [62]. MALAT1 may play a pro-inflammatory role in the pathology of ARDS, supporting its potential value for predicting the risk of ARDS.

NEAT1 (nuclear paraspeckle assembly transcript 1) is an important nuclear lncRNA that possesses pleiotropic features and plays various regulatory roles in distinct cellular, physiological, developmental and pathological processes. Yang et al. [63] compared the levels of NEAT1 in plasma isolated from 26 ARDS-sepsis patients and 76 non-ARDS-sepsis patients 24 h after hospital admission. The subjects were matched by age, sex and body mass index (BMI). Circulating levels of NEAT1 were upregulated in ARDS patients (median (IQR) AU: 3.863 (2.512–5.941) compared to controls (median (IQR) AU: 2.581 (1.573–3.824)). The ROC curves revealed the discriminative values for NEAT1 on ARDS risk in sepsis patients with an AUC of 0.707 (95% CI 0.595–0.820) [63]. Consistent with these findings, Lv et al. [64] also found upregulated plasma levels of NEAT1 in ARDS patients (n=22) compared to sex- and BMI-matched controls (n=15). The experimental controls had not been diagnosed with lung, cardiac, infectious or allergic diseases. In murine cardiac cells, NEAT1 is regulated by hypoxic pathways and triggers a subsequent downstream response that includes a profibrotic response [48]. Likewise, it is a well-characterised activator of the NLRP3, NLRC4 and AIM2 inflammasomes, which in turn amplify the inflammatory response [65]. The overexpression of NEAT1 could lead to the inflammatory tissue damage observed in ARDS. A clinical trial is currently evaluating this lncRNA as a tool for the diagnosis and treatment of ARDS patients (ClinicalTrials.gov: NCT04937855).

The lncRNA MEG3 (maternally expressed gene 3), a lncRNA linked to the progression of sepsis-induced lung injury due to its involvement in regulating lung microvascular permeability and inflammatory response [66], was quantified in plasma samples from 112 consecutive sepsis patients who had been admitted to the ICU within the previous 24 h [67]. Sepsis-related ARDS was assessed according to the Berlin definition of ARDS (n=30 ARDS). MEG3 levels were upregulated in ARDS-sepsis patients compared to those in non-ARDS-sepsis patients. The combination of this lncRNA with other independent risk factors (including age, smoking status and C-reactive protein (CRP) level) predicted ARDS risk with an optimal discriminative value (AUC 0.851 (95% CI 0.776–0.926)) and improved the individual discriminative values of MEG3 (AUC 0.775 (95% CI 0.678–0.872)) and risk factors alone (AUC maximum for CRP 0.685 (95% CI 0.564–0.807)), reinforcing its potential as an additional prognostic biomarker for ARDS patient outcomes in clinical settings.

Previous evidence has proven that SNHG5 (small nucleolar RNA host gene 5) is involved in lung-related diseases such as COPD [68]. In 2021, Wang et al. [69] evaluated the serum levels of SNHG5 in 20 ARDS subjects and 20 controls with normal physiological indicators and no history of underlying disease. The relative expression of SNHG5 was downregulated in the serum of ARDS patients compared to controls. Supporting a plausible protective role in ARDS, both in vitro and in vivo studies indicate that SNHG5 suppresses the development of ARDS by targeting the miR-205/COMMD1 axis.

In the same year, Ji et al. [70] analysed OIP5-AS1 (Opa-interacting protein 5 antisense RNA 1) levels in serum collected from 20 ARDS subjects diagnosed according to the Berlin definition and 20 controls. OIP5-AS1 expression was upregulated in the ARDS group compared to the controls. Of note, knockdown experiments demonstrated that OIP5-AS1 exacerbates LPS-induced ALI/ARDS through the miR-223/NLRP3 axis, both in vitro and in vivo.

Furthermore, elevated levels of the lncRNA PVT1 (plasmacytoma variant translocation 1) were found in plasma samples of 28 ARDS-sepsis patients compared to those in samples from 81 non-ARDS-sepsis controls [71]. The lncRNA PVT1 level presented an AUC of 0.729 (95% CI 0.624–0.835) for the detection of ARDS. Notably, in terms of AUC values, the predictive value of lncRNA PVT1 for ARDS risk was slightly higher than that provided by conventional severity scores such as the APACHE II score and the SOFA score, which had AUCs of 0.712 (95% CI 0.611–0.813) and 0.647 (95% CI 0.525–0.770), respectively.

Perspectives and challenges

As discussed, the circulating levels of lncRNAs may serve as novel biomarkers for the management of ARDS. Nonetheless, there is still a long way to go until the lncRNAs are adopted in clinical practice.

One prominent limitation of many studies is the use of small sample sizes and a lack of power calculation. Furthermore, the bulk of research in this area is confined to single-centre investigations, and the studies involve diverse clinical presentations, varying therapeutic strategies and disparate sample collection timelines. Investigations in which large populations and well-characterised patients and samples are studied are fundamental. In addition, the predominant focus of studies on the diagnostic utility of lncRNAs leaves a considerable gap in the understanding of their potential role in prognosis and in the prediction of therapeutic response and post-acute sequelae in ARDS survivors. Currently, optimal therapeutic selection, treatment monitoring, injurious overdistension during mechanical ventilation and post-acute sequelae prevention are pivotal challenges in the ARDS landscape, and investigations that contribute to accurate prediction in these domains are of paramount importance. In this regard, the evaluation of biomarker value should be strengthened. Studies must provide comprehensive data on biomarker performance that encompass metrics such as AUC, discrimination improvement, reclassification, sensitivity, specificity and accuracy, among others.

A noteworthy concern relates to the comparison of “healthy subjects” with “ARDS patients”. This type of design is subject to many biases and leads to overly optimistic biomarker performance due to differences in variables such as sex, age, race/ethnicity, comorbidities, pre-existing conditions and medication usage [72]. In addition, when evaluating the added value of a new biomarker, the decision is not complete without validation at least internally, but preferably externally. External validation using an expanded or independent cohort of individuals with the same disease would provide robustness. On another note, the choice of biomarker should be guided by the scientific question and by the financial resources required for its measurement. It is essential to perform a cost–effectiveness analysis to determine the feasibility of integrating a specific biomarker into clinical decision making.

With the exception of MALAT1 and NEAT1, evaluation of specific lncRNAs has mainly been confined to individual studies. While the analysis of a single lncRNA simplifies the interpretation of findings, it inherently restricts the depth of information available. The regulation of complex biological and pathological mechanisms depends on the collective modulation of multiple ncRNAs rather than on isolated changes in the expression of individual ncRNAs [33]. The comprehensive evaluation of lncRNA signatures holds promise for providing intricate insight into their clinical significance. The use of lncRNA profiles, even in combination with data from other omics, is also fundamental in relation to heterogeneous syndromes such as ARDS. This approach could allow the identification of specific clusters of lncRNAs and other biological variables, i.e. endotypes, that alone or in combination with clinical characteristics or subphenotypes allow an improvement in patient management [73]. To do that, the implementation of advanced statistical analyses, e.g. machine learning, may provide useful information. The body of evidence accumulated in recent years suggests the efficacy of integrating ncRNA signatures, which encompass lncRNAs, with machine learning techniques for the development of classifiers [74, 75].

The quantification of lncRNA in biological samples (figure 2) is not exempt from certain technical considerations at all stages. Obtaining high-quality RNA is paramount for accurate lncRNA quantification. The selection of the RNA extraction method holds particular importance, especially for complex samples like plasma or serum, as it significantly influences result quality and data robustness. Various RNA extraction methods are available, ranging from phenol-based techniques to combined phenol- and column-based approaches and pure column-based techniques. It is fundamental to recognise that each method has specific advantages and limitations [76]. Therefore, the optimisation of the RNA isolation step is pivotal in study design to ensure the generation of consistent results.

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FIGURE 2

Circulating long noncoding RNA (lncRNA) quantification. Summary of the main steps for the quantification of circulating lncRNAs. Figure created with BioRender.com.

For lncRNA detection, RT-qPCR remains the gold standard technique. Most of the methodological challenges and perspectives described in this section are focused on lncRNA quantification using this technique. The low expression of lncRNAs in body fluids probably constitutes the major limitation in the field. While this characteristic can be advantageous in the development of biomarkers for severe tissue injury, similar to the utility of troponins, it can hinder detailed evaluation of lncRNA performance. To mitigate this issue, a pre-amplification step should be employed before lncRNA quantification is performed. Nonetheless, pre-amplification is a topic of debate because it may introduce technical bias, and some circulating lncRNAs remain undetectable even after this step [77].

Another critical issue in the field of circulating ncRNAs is the lack of a standardised endogenous control for qPCR data normalisation [78]. Many studies still employ classical endogenous controls such as glyceraldehyde 3-phosphate dehydrogenase or β-actin, which are unsuitable given that their biological behaviour and structural characteristics are distinct from those of lncRNAs. The presence of these transcripts in circulating cell-free compartments can be indicative of low sample quality, often due to sample degradation or inappropriate centrifugation protocols. Various mathematical algorithms, including NormFinder [79], geNorm [80] and BestKeeper [81], have been designed for reference gene assessment. A careful evaluation of the selected endogenous control prior to large experiments is mandatory. In this context, the use of exogenous controls remains an underexplored area. In comparison to miRNA quantification, where the use of exogenous miRNAs derived from other organisms, such as Caenorhabditis elegans, is well established, no equivalent exogenous control has been described for quantifying lncRNAs in biofluids. Some studies have employed cel-miR-39-3p for lncRNA normalisation [82]; however, it is crucial to acknowledge the limitations of this approach, including potential differences related to RNA size during isolation. Further exploration is needed to address these considerations.

The pre-analytical and analytical parameters and their impact on intra-study variability must be thoroughly examined to achieve optimal reproducibility [78]. The stability of lncRNAs in biofluids has been explored in limited studies [49]. More recently, the stability of lncRNA expression profiles under various sample storage conditions was measured in human peripheral whole blood collected into PAXgene Blood RNA tubes (PreAnalytiX) by Wylezinski et al. [83]. By performing freeze–thaw cycles in which the RNA samples were thawed, incubated for 10 min at room temperature (21°C) and refrozen at –80°C for 30 min, the authors found that storage of the samples in PAXgene tubes maintains the stability of lncRNA expression profiles for up to 1 year. Similarly, storing total RNA or cDNA synthesised from fresh total RNA or total RNA stored at –80°C for 1 year did not result in significant changes in lncRNA expression. The choice of anticoagulant, centrifugation protocol and temperature used in sample preparation and storage should be uniform to keep the analysis consistent. These specifics should be comprehensively documented in research publications. The use of heparin tubes should be rigorously avoided, and in cases involving patients undergoing heparin therapy, an additional step involving heparinase treatment is essential [84].

A significant challenge in utilising lncRNAs as biomarkers is the existence of multiple lncRNA isoforms and variations in their functions and abundance [85]. In general, research efforts tend to prioritise variants with higher expression levels, as weakly expressed transcripts are often overlooked. While several tools have been developed to characterise isoform diversity [86], investigations into the relevance of different splicing isoforms of lncRNAs in conditions such as ARDS are lacking. This bias complicates the accurate profiling of lncRNA expression. Addressing the importance of distinguishing between different isoforms of lncRNAs and the necessity of designing assays that target specific isoforms would be beneficial.

To date, few lncRNAs have been subjected to comprehensive molecular and functional characterisation. While lncRNAs hold potential as therapeutic tools due to their capacity to regulate gene expression, knowledge concerning their utility as therapeutic targets is still in its infancy. Further research is needed to develop molecular tools that tailor lncRNAs for patient intervention. Gain- and loss-of-function studies, both in vivo and in vitro, are needed to thoroughly delineate the functional roles and mechanisms of action of the lncRNAs under investigation. Unlike protein-coding genes, where sequences are typically conserved across species, lncRNAs tend to exhibit significantly lower levels of conservation throughout evolution. The low conservation of lncRNAs across species has significant implications in biomedical research, as it complicates the extrapolation of findings from animal models. This highlights the need for caution when employing animal models to examine the role and significance of lncRNAs in human health and diseases. Finally, lncRNAs can be selectively packaged and secreted within extracellular vesicles, suggesting that they have crucial cell-to-cell signalling functions. However, the mechanisms that govern lncRNA loading into extracellular vesicles and their behaviour as endocrine signals are not fully understood.

Concluding remarks

lncRNA-based biomarkers represent an innovative approach with the potential to significantly improve patient management in ARDS. Beyond their roles as diagnostic and prognostic tools, the development of companion diagnostics based on lncRNAs offers a promising strategy for optimising patient outcomes. Additionally, the implementation of theranostic approaches based on lncRNAs could assist in ARDS treatment by facilitating assessment of treatment efficacy and enabling adjustments to therapy based on individual patient responses. Furthermore, considering their intricate regulatory roles in gene expression and cellular processes, lncRNAs also offer a novel field for targeted therapeutic interventions in ARDS.

Integrating the lncRNA transcriptome into medical decision making requires concerted efforts to address the challenges outlined. Collaboration between basic and clinical researchers, along with involvement from the biotechnology industry, is pivotal in translating these novel biomarkers into clinical practice.