Systematic Investigation of the Diagnostic and Prognostic Impact of LINC01087 in Human Cancers

Simple Summary LINC01087 is a recently described long non-coding RNA. Its potential implication within oncology is being increasingly acknowledged. Here, we explored the clinical interest of LINC01087 in the diagnosis and prognostication of multiple cancer types. A series of in-depth in silico analyses revealed LINC01087 as a potential diagnostic indicator of some hormone-related tumors, including breast cancer (BC), ovarian carcinoma, and testicular germ cell tumors, as well as other cancer histotypes, such as esophageal and stomach cancers. Moreover, LINC01087 appears as a prognostic indicator in BC and renal papillary cell carcinoma. Therefore, the quantitation of LINC01087 in tissue specimens might have a clinical utility to improve the management of these pathologies in the future. Abstract (1) Background: Long non-coding RNAs may constitute epigenetic biomarkers for the diagnosis, prognosis, and therapeutic response of a variety of tumors. In this context, we aimed at assessing the diagnostic and prognostic value of the recently described long intergenic non-coding RNA 01087 (LINC01087) in human cancers. (2) Methods: We studied the expression of LINC01087 across 30 oncological indications by interrogating public resources. Data extracted from the TCGA and GTEx databases were exploited to plot receiver operating characteristic curves (ROC) and determine the diagnostic performance of LINC01087. Survival data from TCGA and KM-Plotter directories allowed us to graph Kaplan–Meier curves and evaluate the prognostic value of LINC01087. To investigate the function of LINC01087, gene ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed. Furthermore, interactions between LINC01087 and both miRNA and mRNA were studied by means of bioinformatics tools. (3) Results: LINC01087 was significantly deregulated in 7 out of 30 cancers, showing a predominant upregulation. Notably, it was overexpressed in breast (BC), esophageal (ESCA), and ovarian (OV) cancers, as well as lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD), and uterine carcinosarcoma (UCS). By contrast, LINC01087 displayed downregulation in testicular germ cell tumors (TGCT). ROC curve analyses identified LINC01087 as a potential diagnostic indicator in BC, ESCA, OV, STAD, and TGCT. Moreover, high and low expression of LINC01087 predicted a favorable prognosis in BC and papillary cell carcinoma, respectively. In silico analyses indicated that deregulation of LINC01087 in cancer was associated with a modulation of genes related to ion channel, transporter, and peptide receptor activity. (4) Conclusions: the quantification of an altered abundance of LINC01087 in tissue specimens might be clinically useful for the diagnosis and prognosis of some hormone-related tumors, including BC, OV, and TGCT, as well as other cancer types such as ESCA and STAD. Moreover, our study revealed the potential of LINC01087 (and perhaps other lncRNAs) to regulate neuroactive molecules in cancer.

only in a few oncological indications (i.e., BC, glioma, esophageal squamous cell carcinoma, and thyroid cancer) [32,[36][37][38][39]. Recent evidence reported LINC01087 to be overexpressed in glioma patients with respect to healthy subjects [36,37]. Mechanistically, it has been demonstrated that LINC01087 participates in the development of glioma by targeting some microRNAs (miRNAs) (i.e., miR-384 and miR-1277-5p) and, therefore, altering the related signaling pathways [36,37]. For instance, it has been demonstrated in vitro that a high expression of LINC01087 reduces the level of Bcl2 by acting as a miR-384 sponge, thus promoting apoptosis in glioma cells [36]. In addition, its overexpression promotes proliferation, migration, and invasion of thyroid cancer cells through the upregulation of the gene protein phosphatase, Mg2 + /Mn2 + dependent 1E (PPM1E) [38]. Furthermore, LINC01087 demonstrated a strong interest in the stratification and prognosis of BC patients [32]. Indeed, the abundance of LINC01087 can discriminate between the luminal and triple-negative (TNBC) subtypes of BC. Furthermore, a high tumor level of this lncRNA is an indicator of prolonged overall survival in BC patients [32].
Here, we shed light on the value of LINC01087 as an indicator for the diagnosis and prognostication of multiple cancer types. Using bioinformatics methods, we first assessed the level of expression of LINC01087 across several tumors. Then, we validated the diagnostic value of LINC01087 using receiver operating characteristics (ROC) curves analysis. We correlated its altered expression with patient survival and clinicopathological features. Finally, the potential functions of LINC01087 were identified by building coexpression networks and gene ontology (GO) enrichment analyses. Our results revealed that LINC01087 is mainly upregulated in multiple cancers, particularly in BC, esophageal, stomach, and ovarian carcinomas, as well as in testicular germ cell tumors. Additionally, LINC01087 could serve as a prognostic indicator in BC and papillary renal cell carcinoma. Interestingly, in silico analyses revealed a potential role for LINC01087 in carcinogenesis through the regulation of gene products that sense or exchange extracellular molecules.

Transcriptomics Data Analysis
RNA-sequencing (RNA-seq) and clinical data of 24 primary tumors and paired normal tissues were extracted from The Cancer Genome Atlas (TCGA) pan-cancer 2018 online repository using TCGAbiolinks package in R [23,24,40]. RNA-seq data of 27 types of healthy organs/tissues were downloaded from Genotype-Tissue Expression (GTEx) portal (http://www.gtexportal.org/home/datasets, 1 August 2022). To perform the differential expression analysis of LINC01087, we compared the transcriptomic data of primary tumors extracted from TCGA to paired normal tissues (also retrieved from TCGA) combined with healthy samples of the tissue of origin (extracted from GTEx). The TCGA and GTEx datasets originally comprised 32 and 35 tissues, respectively. However, we excluded several breast cancer and liver subtypes (total of 8 tissues) from both datasets, as well as some organs/tissues with no data (n = 3) from GTEx, resulting in 24 (TCGA) and 27 (GTEx) tissues, reflecting a total of 30 distinct tissues. Of note, only datasets with information about LINC01087 expression were included. Samples with some unassigned values (i.e., "NA") were removed. Analysis of the differential expression of LINC01087 between normal and malignant tissues was performed using DeSeq2 R/Bioconductor in the 30 types of organs/tissues. LINC01087 was considered as significantly deregulated in cancer when log 2 fold change (FC) absolute value ≥ 1.5 and p-value ≤ 0.05 (Wilcoxon test). For each dataset, the differential expression of LINC01087 was calculated based on the dataset median value used as a threshold. The association between the expression of LINC01087 in different tumor types and the clinicopathological parameters (i.e., age, gender, tumor stage, tumor size, tumor status, lymph node, and metastasis involvement) was calculated using Fisher's exact test. For each dataset, a high versus low discrimination of LINC01087 expression was achieved, using the dataset median value as a threshold.

Survival Curve Analysis
To evaluate the effect of LINC01087 on the overall survival (OS) across patients affected with various cancer histotypes, we used the pan-cancer RNA-seq data from TCGA (https://portal.gdc.cancer.gov 15 September 2022) and the Kaplan-Meier Plotter (KM-Plotter) datasets (https://kmplot.com, 2 July 2022) [41,42]. In KM-Plotter, survival curves were generated by selecting the three following filters: median, JetSet best probe set, and all datasets. Survival R package v.3.4 and Survminer R package v.0.4.9 were applied to TCGA datasets in order to calculate the hazard ratio (HR), 95% confidence intervals (CI), and p-values. For each TCGA dataset, depending on the number of available samples, patients were subdivided into two groups according to the median expression level of LINC01087: high or low. A p-value ≤ 0.05 was considered statistically significant.

ROC Curve Analysis
ROC curves were generated and the area under the curve (AUC) was calculated using the R packages MASS and pROC [43,44]. All tests were performed in R statistical software (ver. 4). AUC ≥ 0.7 and p-value ≤ 0.05 were applied as a cut-off criterion to consider LINC01087 as a good diagnostic biomarker. For each dataset, a high versus low discrimination of LINC01087 expression was considered based on the dataset best cut-off value.

Network and Pathway Enrichment Analyses of LINC01087
For the identification of interactions between LINC01087 and mRNAs and miR-NAs, we employed a dual strategy. In the first step, we performed a Spearman correlation test to identify mRNAs and miRNAs that correlated with LINC01087 expression in BC, ESCA, OV, STAD, and TGCT by extracting data from TCGA datasets. We defined significant associations as having a correlation coefficient (R) > 0.4 and a p-value < 0.05. Volcano plots and Venn diagrams were drawn using EnhancedVolcano and Ven-nDiagram R packages, respectively. In the second step, we used the web tool DIANA-LncBASE v3 (https://diana.e-ce.uth.gr/lncbasev3, 14 October 2022) to identify candidate miRNA targets of LINC01087 [45]. Then, we took advantage of the miRTarBase (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php, 20 October 2022) to predict mRNA targets of the miRNAs derived from both analyses [46]. Finally, the target mRNA candidates were compared with the mRNAs that correlated with LINC01087 expression in the initial analysis.
To investigate the biological pathways and processes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) term enrichment analyses were performed using the clusterProfiler R package on genes significantly associated with LINC01087 expression (R > 0.1 p < 0.05) in BC, ESCA, STAD, and TGCT tumor tissues derived from TCGA (p-value were adjusted using the Benjamini-Hochberg procedure).
Finally, we investigated whether the deregulation of LINC01087 could be associated with clinicopathological features (i.e., age, gender, tumor stage, tumor size, tumor status, lymph node, and metastasis involvement). To address this point, we focused on cancers in which LINC01087 showed both up-or down-regulation (|log2FC|> 1.5, p < 0.05) and diagnostic performance (AUC > 0.7, p < 0.05), when such information was available. These criteria restricted the analysis to BC, ESCA, OV, STAD, and TGCT (Tables 2 and S1). LINC01087 expression appeared to be significantly associated with the tumor status (p = 0.04) and tumor size (p = 0.01) in BC ( Table 2). In TGCT, LINC01087 significantly correlated with the size of tumor (p = 0.05) ( Table 2). Altogether, these results imply that the perturbation of LINC01087 expression might play a critical role in carcinogenesis. Moreover, LINC01087 shows great potential as an indicator for the diagnosis of BC, as well as esophageal carcinoma and stomach adenocarcinomas, OV and TGCT. These findings require further experimentation to validate such interest.

Prognostic Value of LINC01087 in Cancer
Subsequently, we examined the prognostic value of LINC01087 by evaluating the relationship between the expression of LINC01087 and patient overall survival (OS). For this purpose, cohorts gathered in the TCGA directory were used for derivation, while inputs of the KM-Plotter database initially served as validation cohorts.
Further interrogation of the KM-Plotter database revealed that a low abundance of LINC01087 matched an extended OS in HCC, as well as in uterine corpus endometrial carcinoma (Table 1).

Impact of LINC01087 on Cancer-Related Pathways
To capture significant insights into LINC01087 functions in cancers, we performed a correlation-based expression analysis extracting data from TCGA datasets. In particular, we looked for genes that significantly correlated (Spearman correlation test: R ≥ |0.4|, p ≤ 0.05) with LINC01087 expression in each of the tissue types where LINC01087 demonstrated a strong diagnostic potential (log2 FC ≥ |1.5|, p ≤ 0.05, and AUC ≥ 0.7, p ≤ 0.05), namely BC, ESCA, OV, STAD, and TGCT (Analysis 1- Table 1, Figure S1, and Tables S2-S6). We found significant positive (R ≥ 0.4, p ≤ 0.05) correlations between the expression of LINC01087 and 574 transcripts in BC, 691 in ESCA, 141 in OV, 773 in STAD, and 636 in TGCT (Tables S2-S6). LINC01087 was associated with pseudogenes, microRNAs (miR-NAs), and other lncRNAs, as well as with protein-coding genes. Out of the 10 transcripts that were the most significantly correlated with LINC01087 in BC, 6 were pseudogenes, 2 were lncRNAs, and 2 were protein-coding genes belonging to the POTE family (POTEH, POTEI). Similarly, in ESCA, among the top 10 genes associated with LINC01087 were pseudogenes and lncRNAs, as well as the neurofilament heavy polypeptide (NEFH) gene, whose deregulation had been associated with the development of several malignancies, such as BC [47]. Several genes were negatively and modestly (R ≤ −0.2, p ≤ 0.05) correlated with LINC01087; therefore, they were not considered for further analysis. To refine our analysis, we compared the correlating gene sets of every cancer type (R ≥ 0.4, p ≤ 0.05) for overlapping genes. We identified 2 transcripts that were positively associated with LINC01087 expression in all 5 cancers: the pseudogene POTE ankyrin domain family member K (POTEKP) and the uncharacterized lncRNA AC093838.1 (Figure 2A).  (Tables S2-S6). In addition, the transcript of mediator complex subunit 15 pseudogene 4 (MED15P4) showed a strong association with LINC01087 in 3 out of the 5 tumor types (i.e., OV, STAD and TGCT), and a moderate correlation in ESCA (Tables S2-S6).
In summary, this orthogonal investigation revealed that LINC01087 might impact cancer progression by modulating the ability of cells to interact with their environment.  (Tables S2-S6). In addition, the transcript of mediator complex subunit 15 pseudogene

LINC01087 Functions as Competing Endogenous RNA
LncRNAs participate in carcinogenesis by modulating gene expression via either direct binding to mRNAs, or indirectly by functioning as a competing endogenous RNA (ceRNA) in the cytoplasm. This latter mechanism is characterized by the binding of lncRNAs to miRNAs, thus preventing their interaction with target mRNAs [48]. LINC01087 displays a cytoplasmic distribution as predicted by lncLocator database (score = 0.76, Table S9). Therefore, we investigated the potential of LINC01087 to act as a miRNA sponge.
To construct a potential LINC01087-miRNA-mRNA network, we used a dual strategy. In a first step based on the TCGA datasets, we screened for miRNAs the expression of which correlated with that of LINC01087 in the 5 types of cancer (R ≥ |0.4|, p ≤ 0.05) (Analysis 2- Figure 3). Sixty-four miRNAs were positively correlated with the expression of LINC01087 (Figure 3, Table S10). The top 5 transcripts demonstrated a moderate correlation (0.64 ≤ R ≤ 0.72) and comprised miR-5692B, miR-4666A, miR-31199-1, miR-1252, and miR-7157 (Table S10). Then, the miRTarBase was interrogated, and 2396 potential mRNA targets of these 64 miRNAs were identified ( Figure 3). Finally, we compared this list of candidates (n = 2396, Analysis 2) with the initial clusters of genes positively correlated with LINC01087 in the 5 tumor types from TCGA (n = 2458, Analysis 1). A total of 31 genes overlapped between the two datasets ( Figure 3, Table S11).
In the second step, we used DIANA-LncBASE v3 to discover miRNAs interacting with LINC01087. A total of 20 miRNAs were identified as potential targets of LINC01087 (Analysis 3- Figure 3, Table S12). Most of them had already been involved in carcinogenesis, such as miR-19a-3p and miR-181-5p [49,50]. Furthermore, 4 of the 20 miRNAs belonged to the let-7 miRNA family (i.e., hsa-let-7a-5p, hsa-let-7d-5p, hsa-let-7e-5p, hsa-miR-98-5p). However, no overlapping miRNAs were observed between the DIANA-and TCGA-based analyses. Then, we executed the miRTarBase to detect mRNA targets of these 20 miRNAs. A total of 5532 mRNA targets were identified in Analysis 3. Sixty-eight of them were common with the list of 2458 genes identified in Analysis 1 (Figure 3, Table S13).  . Schematic representation of the strategy used to characterize the LINC01087-miRNA-mRNA network. A Spearman correlation test was applied to TCGA datasets to identify 2458 transcripts (Analysis 1) and 64 miRNAs (Analysis 2), the expression of which correlated with that of LINC01087 in BC, ESCA, OV, STAD, and TGCT. In parallel, the DIANA-LncBASE v3 toolbox allowed to discover 20 miRNAs that interacted with LINC01087 (Analysis 3). Next, the miRTarBase predicted 2396 mRNA targets of the miRNAs identified in Analysis 2, and 5532 mRNA targets of the miRNAs identified in Analysis 3. The expression of 31 among these mRNA targets from Analysis 2 and 68 mRNA targets from Analysis 3 had been correlated with that of LINC01087 in Analysis 1. Among them, 10 messenger RNAs were shared between the three analyses, hence representing the core of the genes regulated by LINC0187 and related miRNAs. The resulting LINC01087-miRNA-mRNA network is detailed in Table 3  . Schematic representation of the strategy used to characterize the LINC01087-miRNA-mRNA network. A Spearman correlation test was applied to TCGA datasets to identify 2458 transcripts (Analysis 1) and 64 miRNAs (Analysis 2), the expression of which correlated with that of LINC01087 in BC, ESCA, OV, STAD, and TGCT. In parallel, the DIANA-LncBASE v3 toolbox allowed to discover 20 miRNAs that interacted with LINC01087 (Analysis 3). Next, the miRTarBase predicted 2396 mRNA targets of the miRNAs identified in Analysis 2, and 5532 mRNA targets of the miRNAs identified in Analysis 3. The expression of 31 among these mRNA targets from Analysis 2 and 68 mRNA targets from Analysis 3 had been correlated with that of LINC01087 in Analysis 1. Among them, 10 messenger RNAs were shared between the three analyses, hence representing the core of the genes regulated by LINC0187 and related miRNAs. The resulting LINC01087-miRNA-mRNA network is detailed in Table 3.
Ultimately, this study led to the construction of a core network of 10 mRNA targets and 28 miRNAs related to LINC01087 (Table 3). In Analysis 2, miRNA targets were evidenced using TCGA; In Analysis 3, miRNA targets were evidenced using DIANA-LncBASE. In both Analyses 2 and 3, mRNA targets of LINC01087-related miRNAs were identified using miRTarBase.
Altogether, these results documented a constellation of both protein-coding and translation-interfering transcripts that are directly or indirectly regulated by LINC01087 and that might contribute to the initiation and progression of neoplasms.
To examine the clinical interest of LINC01087, we first analyzed its expression across a large array of tumor types using TCGA and GTEx databases, then calculated the ROC scores to assess its diagnostic performance. Among the 30 cancers evaluated, LINC01087 displayed a significant modulation of its expression in 7 of them, mainly hormone-dependent. More precisely, LINC01087 showed an increased expression in BC, OC, and UCS, and an inversely reduced level in TGCT as compared to non-tumor/healthy control samples. Additionally, an elevation of LINC01087 expression was witnessed in LUSC, as well as in two digestive cancers, namely ESCA and STAD.
Our results were consistent with the literature regarding the abundance of LINC01087 in BC and ESCA [36][37][38]68]. Nevertheless, previous research reported a higher expression of LINC01087 in THCA patients than in normal individuals [38], which was not validated in this study. This discrepancy could originate from differences in the size of the cohorts (n = 1224 in the present work versus n = 30 in the former article by Yin et al.) as well as the detection method applied (RNA-seq versus RT-qPCR) [38].
Out of the 7 cancers harboring an aberrant expression of LINC01087, a diagnostic test revealed promising performances in 5 of them (i.e., ROC curve AUC > 0.7). In detail, the measurement of LINC01087 levels was an excellent (AUC = 1) indicator of malignancy in TGCT, and a fair (0.7 < AUC ≤ 0.8) diagnostic indicator in BC, ESCA, OV, and STAD. Of note, we reported that the measurement of the level of LINC01087 in BC also allowed us to further distinguish between the luminal and triple-negative subtypes [32].
Next, we evaluated the prognostic value of LINC01087 by comparing cancer patient OS according to its level of expression using the cohorts of TCGA and KM-plotter databases for derivation and validation, respectively. LINC01087 deregulation predicted patient outcome in two oncological indications. First, a downregulation of LINC01087 correlated with favorable survival in patients suffering from pRCC. Second, a high expression level of LINC01087 indicated a better prognosis in subjects affected with BC.
We proceeded by evaluating the potential functional significance of LINC01087 in the 5 cancer types in which it was most upregulated, namely, BC, ESCA, OV, STAD, and TGCT. At first sight, we screened for genes correlated with LINC01087 expression in the 5 tumor types. Such analysis identified 2 transcripts overlapping in the 5 cancer types, namely POTEKP and AC093838.1. These genes are both located on chromosome 2 but their role in human malignancies has not been elucidated.
Interestingly, GO annotation and KEGG enrichment analyses performed on the genes positively correlated with LINC01087 expression in BC, ESCA, OV, STAD, and TGCT, found a spectrum of genes and pathways associated with the regulation of ion channel activities and neurotransmission. This cluster consisted mainly of genes related to the receptors for GABA (i.e., GABRG1, GABRG2, GABBR2, GABRA2, GABRA6) and α-amino-3hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) (i.e., GRIN3A, GRIA2, GRIA4, and GRIN1). Based on this information, we speculate that the involvement of LINC010187 in tumorigenesis might be associated with the "neuro-cancer axis" [69]. Of note, neuronal cells and immune and cancer cells can synthesize and release neurotransmitters such as GABA [70][71][72][73]. GABA exerts its multiple functions through ionotropic GABA A receptors and metabolotropic GABA B receptors. The aberrant expression of GABA and GABA receptors has been associated with cancer development, metastasis, and suppression of immunosurveillance [71,74]. In this scenario, lncRNAs, as well as miRNAs, may regulate neuronal activity and target GABA receptors [59,75,76]. For instance, one recent study examined the role of the lncRNA Gm37494 in the context of osteoarthritis, focusing on its capability to target GABRA1 by binding to miR-181a-5p [76]. Therefore, it appears plausible, yet remains to be demonstrated, that LINC01087 (and probably other lncRNAs) regulate GABA receptor activity during the formation and progression of specific cancer types.
LncRNAs can regulate gene expression by interacting with DNA, RNA, and proteins [23]. Notably, they can either directly alter the transcription of neighboring and distant protein-coding genes, or indirectly, by targeting and thereby modulating the expression of miRNAs acting as ceRNAs. Therefore, we constructed a LINC01087-miRNA-mRNA network by interrogating the TCGA and DIANA-LncBASE datasets. Next, miRTarBase was used to discover possible interactions between miRNAs and mRNA targets in both datasets. Finally, we overlapped the mRNA targets with the cluster of genes correlated with LINC01087 expression. Our analysis yielded a list of 28 miRNAs and 10 mRNAs that constituted a core panel of putative targets of LINC01087.
Concerning the protein-encoding genes identified in the LINC01087 network, several of them were described as potential cancer-relevant biomarkers. Indeed, PLAG1 was reported as an immunohistochemical marker in pleomorphic adenoma [52]. Moreover, PLAG1 was found to be upregulated in OV, in which its silencing promoted chemosensitivity [50]. Another example concerns the role of HOOK3 as a predictor of poor prognosis in prostate cancer [51]. Furthermore, in accordance with our data, He and colleagues predicted HOOK3 as a target of miR-181-5p [101]. Similarly, upregulation of POLI predicted poor prognosis in esophageal squamous cell carcinoma (ESCC) patients [68][69][70]. Furthermore, the single amino acid polymorphism Thr706Ala in POLI was indicative of a higher risk of developing lung adenocarcinoma and squamous cell carcinoma in individuals of <61 years [102]. The aforementioned investigations support a possible role of the LINC01087-POLI interaction in cancer, in particular ESCA.
Here, we found a high expression of SLC2A3 in ESCA. SLC2A3, also known as glucose transporter 3 (GLUT3), is a member of the SLC2 family, mediating glucose transport across the plasma membrane. The oncogenic potential of SLC2A3 has been extensively investigated. An overexpression or copy number gain of SLC2A3 has been witnessed in liquid and solid tumors [103][104][105][106][107][108]. For instance, high levels of SLC2A3 were associated with unfavorable outcomes in CRC, gastric (GC), and head and neck squamous cell carcinoma (HNSCC) [105][106][107]. Additionally, SLC2A3 seems directly targeted by miRNAs along tumorigenesis [109][110][111]. For instance, miR-29c inhibited cell growth and glucose metabolism in prostate cancer by targeting SLC2A3 [110]. Moreover, a decreased expression of miR-106a increased the expression of SLC2A3, thereby contributing to progression and poor prognosis in glioblastoma patients [109]. In GC, cell glucose metabolism and growth are regulated by the miRNA-129-5p/SCL2A3 axis via the phosphatidylinositol 3-kinase/protein kinase B (PI3K-Akt) and mitogen-activated protein kinase (MAPK) signaling pathways [111]. Accordingly, we observed a relationship between LINC01087 and certain SLC membrane transporters (e.g., SLC28A1, SLC9A3, SLC4A9, and SLC17A8) in luminal and TNBC BC subtypes [32]. These similarities support an intimate link between the solute carrier family and LINC01087 in cancer.
EFNA5, a member of the ephrinA ligand family, is deregulated (up or down) in a variety of cancers [112][113][114][115][116]. A recent study explored the mRNA and protein expression, as well as DNA methylation, immune-related signature, and prognostic value of the EFNA family using a multi-omics integrative approach [56]. EFNA5 mRNA was found to be overexpressed in most tumors compared to normal tissues. Moreover, an association between the mRNA level of EFNA5 and DNA methylation of its promoter were documented for ccRCC, chRCC, pRCC, mesothelioma, prostate cancer, thymoma, and diffuse large B-cell lymphoma. At the protein level, EFNA5 was downregulated in glioblastoma multiforme and ccRCC, but highly expressed in endometrial carcinoma. In addition, a high abundance of EFNA5 in both TCGA and gene expression omnibus (GEO) databases correlated with poor OS in patients with advanced LUAD [56].
The gene SNRPA1, a spliceosome component responsible for processing pre-mRNA into mRNA, showed strong prognostic potential in prostate cancer, HCC, and ccRCC [55,117,118]. Its oncogenic role in CRC has also been demonstrated [119]. Here, we observed high expression of SNRPA1 in STAD, as well as a positive correlation between LINC01087 and SNRPA1. Interestingly, Chen and colleagues reported the interaction between the lncRNA RMRP (RNA component of mitochondrial RNA-processing endoribonuclease) and the protein SNRPA1 as a mediator of TP53 inactivation and as a promoter of cell proliferation in CRC [120].
IQCG is a protein-encoding gene involved in sperm axoneme assembly. Here, we reported an association between LINC01087, miR-181a-5p, and IQCG mRNAs in cancer. The link between miR-181a-5p and IQCG has previously been observed in endometrial carcinoma using an in silico approach [101].
Conversely, there is limited knowledge on C8orf37 and PCP4L1 in malignancies. While C8orf37 has been only associated with genetic disorders, the possible involvement of PCP4L1 in CRC and prostate cancer progression requires further confirmation [121][122][123][124].
Based on our results and previous observations, we speculate that the deregulation of LINC01087 might be related to the carcinogenesis of BC, ESCA, OV, STAD, and TGCT. However, the mechanisms behind LINC01087 involvement in the development/progression of these malignant diseases remain to be explored experimentally.
Since its discovery, interest in and functional knowledge of LINC01087 in the field of oncology have rapidly expanded. Further bench-to-bedside investigations are required to demonstrate the clinical relevance of LINC01087 as a diagnostic and prognostic biomarker.

Conclusions
In summary, the deregulation of LINC01087 plays a potential role in the diagnosis and prognosis of different tumor types. Notably, a high expression of LINC01087 exhibits a strong diagnostic value in breast, esophageal, stomach, and ovarian carcinomas. Moreover, a decreased expression of LINC01087 could be used for the diagnosis of malignant testicular germ tumors. LINC01087 also appears to predict the survival of patients suffering from breast and papillary kidney malignancies. Furthermore, the involvement of LINC01087 in cancer development might be related to its direct and indirect (miRNA-mediated) role in modulating biochemical exchanges between the intracellular milieu and the surrounding microenvironment. Future studies must shed light on the biological activity of LINC01087 and confirm its clinical utility as a diagnostic and/or prognostic biomarker. Ultimately, the quantitation of LINC01087 may ease clinical decision-making and improve the management of these oncological pathologies.