Comprehensive characterization of LINE-1 antisense promoter-driven genes across human cancers

doi:10.21203/rs.3.rs-2799071/v1

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Research Article

Comprehensive characterization of LINE-1 antisense promoter-driven genes across human cancers

https://doi.org/10.21203/rs.3.rs-2799071/v1

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Journal Publication

published 18 Sep, 2023

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Background

Long interspersed nuclear element-1 (LINE-1 or L1) comprises 17% of the human genome. As the only autonomous and active retrotransposon, L1 plays an essential role in cancer initiation and progression. The studies of L1 in cancer mainly focus on the impact of L1 insertion into the new genome locus. The L1 5´ untranslated region (UTR) also contains antisense promoter (ASP) activity, generating L1-gene chimeric transcripts to a neighbor exon. Some of these ASP-driven genes have been reported to be overexpressed in cancer and promote cancer cell growth. However, little is known about overall expression patterns and the roles of L1 ASP-driven genes in human cancers.

Results

L1 ASP-driven genes were frequently dysregulated in cancer and associated with the cell cycle, the P13K/AKT pathway, and the GTPase signaling pathway. The expression of L1 ASP-driven genes was correlated with tumor patient prognosis. Hub L1 ASP-driven genes CENPU and MCM2 showed a correlation with immune infiltration, clinical T stage, and cancer stemness in pan-cancer. Knockdown of L1 ASP-driven gene LINC00491 resulted in a significant decrease in the ability of tumor growth and migration.

Conclusions

The expression of L1 ASP-driven genes is significantly dysregulated at the pan-cancer level, which is closely related to the tumor microenvironment and progression, as well as patient prognosis. Hub genes CENPU and MCM2 are expected to be new tumor diagnostic markers and therapeutic targets.

LINE-1 transposon

LINE-1 antisense promoter-driven gene

Pan-cancer

prognosis

tumor microenvironment

Transcription of transposable elements interspersed in the genome is controlled by complex interactions between their regulatory elements and host factors (Mätlik, Redik et al. 2006). The foundation of the transposable elements (TEs) revised and improved our understanding of genes.

Long interspersed Element-1 (LINE-1 or L1) is the human genome’s only autonomously active, transposable element. L1 sequences comprise approximately 17% of the human genome, but only the evolutionarily recent, human-specific subfamily is retrotransposition competent(Criscione, Theodosakis et al. 2016). Long Terminal Repeat (LTR) and Non-LTR retrotransposons are the two most abundant classes of transposable elements that contain regulatory regions (promoter, enhancer, and polyadenylation signal) necessary for their transcription and transposition(Mätlik, Redik et al. 2006). Scott E. Devine conducted targeted sequencing of L1 insertion sites in 20 samples of early lung cancer and found 9 tumor-specific L1 insertions in 6 of them(Iskow, McCabe et al. 2010). Peter J. Park found 183 L1 insertions in colon, prostate, and ovarian cancer through a genome-wide study of various tumor tissues, with varying numbers of L1 insertions for each tumor, from an average of 4 in ovarian cancer to 106 in single colon cancer(Shukla, Upton et al. 2013).

Previous studies have revealed that LTR retrotransposons can influence the transcription of adjacent genes(Rakyan, Blewitt et al. 2002, Kashkush, Feldman et al. 2003). Hancks et al think many somatic insertions of L1 may be benign, but under the onslaught of cancer priming and other types of mutations (including deletions), these insertions have the potential to optimize different cellular networks, or to seed new adaptations full-length during cancer progression(Hancks and Kazazian 2016). The ASP is located in the L1 5’UTR (positions 400–600) and drives transcription in the opposite direction to that of the L1 internal promoter(Nigumann, Redik et al. 2002). L1 ASP-driven genes pose various deleterious effects on the structure and function of the host genome. A previous study suggests that the L1 ASP-driven transcription is a common phenomenon not only for tumor cells but also for normal ones and may involve transcriptional interference or epigenetic control of different cellular genes(Nigumann, Redik et al. 2002).

The insertion of L1-ASP in the tested samples will cause the corresponding gene mutation or dysregulation, suggesting that this way may be one of the causes of L1-ASP carcinogenesis. Transcription driven by L1 ASP may have an important effect on the regulation of cellular gene expression. However, little is known about overall expression patterns and the roles of L1 ASP-driven genes in human cancers.

In this study, we obtained RNA-seq data from the TCGA database and comprehensively analyzed the differential expression of L1 antisense promoter-related genes in 23 common tumors and the distribution of these significant differential genes in each tumor. Then, we explored the relationship between differentially expressed genes and patient prognosis in 10 tumors with a large number of L1 ASP-driven up-regulated genes and explored the biological functions and involved pathways of these differentially expressed genes by GO and KEGG enrichment analysis. Then we elucidated the influence of these differentially expressed genes on the tumor immune microenvironment by immune-infiltration analysis. We also explored the specific causes of the dysregulation of L1 ASP-driven genes from the perspective of gene methylation level and copy number variation. Through protein interaction network analysis, we discovered two hub genes, CENPU and MCM2, that play important roles in pan-cancer species and are closely related to tumor T staging and tumor stemness in pan-cancer. Finally, LINC00491, a long non-coding RNA driven by L1 ASP was selected to experimentally study its oncogenic effect in ovarian and thyroid cancer.

L1 ASP-driven genes were frequently dysregulated in cancer and are associated with various cancer pathways.

The flowchart was shown in Fig. 1. We first characterized the gene types of ASP-driven genes identified previously. 903 L1 ASP-driven genes, including 808 protein-coding genes and 95 long non-coding RNAs (Fig. 2A, Table S1). We investigated the functional involvement of the 903 gene list in 23 cancer, functional enrichment analysis revealed that these genes were closely associated with tumor growth, cell division, cell cycle, and cellular senescence (Fig. 2B). We further analyzed the dysregulated in 23 cancers for the 903 L1 ASP-driven genes to determine the overall expression pattern across 23 cancer types from TCGA. We found that the L1-ASP driver gene was widely regulated in most cancers. In general, the number of up-regulated gene expressions was more than the number of down-regulated genes (Fig. 2C). Our analysis identified differential expression of L1 ASP-drive in each type of cancer. These results suggest that the L1 ASP-driven gene was widely over-expressed in at least 10 cancers (Fig. 2D). We further focused on up-regulated genes because the up-regulated genes were more abundant than the down-regulated genes. Our analysis identified several previously known carcinogenic L1 ASP-driven genes, such as MMP1 and GTSE1, which were up-regulated in multiple cancer types. MMP1 was regulated separately in 15 types of cancer (Gobin, Bagwell et al. 2019). GTSE1 was upregulated in breast cancer, lung cancer, and colon cancer(Tian, Zhang et al. 2011, Stelitano, Peche et al. 2017, Zhang, Meng et al. 2021). Overall, our findings suggest that the L1 ASP-driven genes may be involved in cancer development and progression.

To further explore the differences in L1 ASP-driven gene expression across various cancer types, we generated volcano plots that displayed the overall distribution of expression multiples and the significance of differences. Our analysis revealed numerous up-regulated genes, including COL23A1, SDS, and NNMT, and down-regulated genes, such as GPC5 and SLC4A8, that are associated with the L1 ASP-driven genes in KIRC. In LUSC, we observed regulated genes like CASC9 and GNS1 and down-regulated genes such as COH5 and LMO7 that were associated with the L1 ASP-driven genes. In BRCA, we found up-regulated genes like MMP1 and GTSE1, as well as down-regulated genes such as SVEP1 and STAR09, to be associated with the L1 ASP-driven genes (Fig. 2E, Figure S1). These data revealed that L1 ASP-driven genes are frequently dysregulated in cancer and are associated with various cancer pathways.

The expression of L1 ASP-driven genes was correlated with patient prognosis.

To investigate the potential prognostic value of L1-ASP in various cancers, we examined 10 cancers in which the L1 ASP-driven genes were dysregulated more extensively. We further analyzed the relationship between the up-expression of the L1-ASP driver gene and the overall survival of tumor patients. The most significant cancer in which L1 ASP-driven genes were up-regulated was Lung squamous cell carcinoma (LUSC). By analysis of Uni-variate COX regression and Lasso-COX regression, we identified 10 L1ASP-driven genes (POPDC3, FEZF1-AS1, ZNF711, VRK1, GDAP1, CYP2C18, RCN2, LSM7, GABRR1, and CCDC51) that were related to the patient’s prognostic of LUSC. Then we used these genes to build a prognostic model by multivariable COX regression to assess the prognostic significance in 489 samples (Fig. 3A, Table S2). We found that POPDC3 was an independent factor associated with the prognosis of LUSC patients (Fig. 3B). Moreover, the survival analysis indicated that the low-risk group of the predictive model had a better survival prognosis than the high-risk group(p < 0.01). (Fig. 3C). The ROC curve showed that the AUC of the survival prognosis at year 1, 3, and 5 was above 0.6, indicating that the L1 ASP-driven genes may predict the clinical prognosis of cancer patients. (Fig. 3D, Figure S2) These findings suggest that the L1 ASP-driven genes have the potential as a prognostic biomarker for cancer patients.

The L1 ASP-driven genes were involved in various cancer progression-related pathways.

To understand how the L1-ASP driver gene contributes to cancer initiation and progression, we conducted a functional enrichment and pathway analysis of differentially expressed L1-ASP driver genes across multiple cancer types, we further focus the analysis on LUSC, because the dysregulation is most significant in LUSC. Through GO and KEGG pathway enrichment analysis, we revealed that the L1-ASP driver genes were involved in the GTPase activity signaling pathway, which has been reported to regulate various cellular processes involved in tumor initiation and progression, including proliferation, apoptosis, metabolism, senescence, and cancer stemness(Crosas-Molist, Samain et al. 2022) (Fig. 4A, B, Table S3). Furthermore, the GSEA enrichment analysis showed that the L1-ASP driver gene is involved in the PI3K-AKT-mTOR signaling pathway, which was frequently deregulated in human cancer and regulates many hallmarks of cancer(Sheppard, Kinross et al. 2012) (Fig. 4C, D, Figure S3). Activation of this pathway plays an important role in the tumor progression of various cancer types. Remarkably, PI3K inhibitors have been clinically used to treat breast cancer (Pande, Bondy et al. 2014). These findings suggested that the L1-ASP-driven genes may play a crucial role in cancer formation and progression and may hold therapeutic significance for cancer treatment.

Analysis of methylation and copy number variation of L1 ASP-driven genes.

We conducted a comprehensive analysis of the top 20 L1-ASP driver genes associated with tumor patient prognosis in various cancers (Fig. 5A, Table S4). Further functional enrichment analysis suggested the involvement of classical cancer pathways such as the P53 signaling pathway (Fig. 5B). The p53 protein is a classical tumor suppressor frequently mutated across multiple cancer types. As a pivotal gatekeeper in tumorigenesis, p53 is frequently dysregulated by either mutation or cross of the function of the p53 protein. The inactivation of the p53 pathway results in the dysregulation of a larger number of genes involved in the cell cycle, DNA damage, and apoptosis (Wang, Zhang et al. 2019). Previous studies have shown that a critical role of tumor LINE-1 hypomethylation is in the aggressive behavior of esophageal cancer, which in turn leads to an unfavorable prognosis(Kosumi, Baba et al. 2020). So, we further analyzed the methylation profiles of these genes in pan-cancer, and we found that most of them, such as SPP1, RHBDF1, and NSMCE2, were significantly demethylated, which was consistent with the high expression of these genes specifically in tumors. Meanwhile, we also found that the methylation level of COL11A1 was significantly increased compared with normal tissues, which contradicted its widely high expression in tumors, indicating that there were other ways of expression regulation at the minor genes (Fig. 5C). We next performed a protein interaction network study of the top 20 genes using the STRING database. The results showed that MCM2 and CENPU were the key genes (hub genes) among them (Fig. 5D). We further selected four representative genes, SPP1 and MCM2 to study the mutation and copy number variation profiles at the pan-cancer level. Our surprising finding was that the SPP1 showed a significant decrease in copy number in the three isogenic tumors but a significant increase in copy number in the PAAD, BRCA, etc., suggesting that the same gene may be specifically expressed in different tumors through different mechanisms of expression regulation (Fig. 5E). MCM2 was significantly upregulated in copy number in many tumors, which was consistent with its widely upregulated expression in Pan-cancer (Fig. 5F). Taken together, transcription regulation may be related to the L1 ASP-driven genes promoter. Our study provided valuable insights into the mechanisms of L1 ASP-driven genes’ dysregulation in tumors and their potential roles in cancer development.

The expression of L1 ASP-driven genes MCM2 and CENPU was associated with the magnitude of immune infiltration.

We conducted a pan-cancer analysis to investigate the immune effects of L1 ASP-driven genes, specifically MCM2 and CENPU, in the tumor immune microenvironment. Our findings showed that the MCM2 expression was significant negative correlations with immune infiltration in 15 cancer species (p༜0.01), such as GBM, UCEC, BRCA, LUAD, HNSC, LUSC, THYM, LIHC, OV, etc. (Fig. 6A, Table S5). Additionally, the CENPU expression was significant negative correlations with immune infiltration in 20 cancer species (p༜0.01), such as GBM, CESC, LUAD, LAML, BRCA, STES, SARC, STAD, UCEC, HNSC, LUSC, THYM, LIHC, THCA, etc. (Fig. 6B). Furthermore, MCM2 was inversely correlated with the infiltration of CD8⁺ T cells in LAML, STES, THCA, BRCA, PRAD, and PAAD (Fig. 6C). In the same way, the CENPU was negatively correlated with the infiltration of CD8⁺ T cells in LAML, STES, THCA, BRCA, PRAD, and PAAD (Fig. 6D). These findings suggested that the L1 ASP-driven genes might promote tumor development by inhibiting the infiltration of immune cells in the tumor microenvironment. Therefore, our study may provide some new insights into the immunotherapy of cancer.

Hub L1 ASP-driven genes show a correlation with clinical T stage and cancer stemness in pan-cancer.

We further investigated the potential roles of hub genes, MCM2 and CENPU, driven by L1 ASP-driven genes in tumorigenesis. Our analysis revealed that these genes were significantly upregulated in most tumors (Fig. 7A, B), and their expression positively correlated with clinical T stage in tumors like BRCA, KIRP, KIPAN, PRAD, KIRC, and LIHC (Fig. 7C, D). Moreover, high expression of MCM2 and CENPU was also closely associated with tumor stemness in several tumors, including GBMLGG, CHOL, STAD, PAAD, LUSC, PRAD, etc., indicating their potential as key targets for tumor treatment (Fig. 7E, F). However, further investigation is needed to unravel the underlying molecular mechanisms.

Experimental study of L1 ASP-driven genes in cancer cells.

To further investigate the biological function of the L1 ASP-driven genes in tumors, we randomly selected a long-chain non-coding RNA gene for experimental investigation in cancer cell lines. We utilized siRNA to knock down LINC00491 in OV8 and BHT101 cell lines and then measured the knockdown efficiency using RT-qPCR (Fig. 8A, C). Next, we evaluated the proliferation and tumorigenesis of tumor cells using MTT assay and clonal cluster formation assay. The results indicated that knocking down LINC00491 significantly reduced the cell vitality and the number of clones in OV8 and BHT101 cells compared to the control group (Fig. 8B, D, E, F). Additionally, we used a transwell assay to measure the migration ability of OV8 and BHT101 cells after LINC00491 knockdown (Fig. 8G, H). The results showed that the migration ability of tumor cells decreased significantly after LINC00491 knockdown compared to the control group, suggesting that LINC00491 plays an important role in ovarian and thyroid cancers. However, the specific biological mechanism remains to be further studied. These findings indicated that dysregulation of L1 ASP-driven genes was closely associated with the occurrence and development of tumors.

Long Interspersed Element-1 (L1) is a retro-transposable element in the human genome that plays a significant role in genome evolution and diversity remodeling. It accounts for approximately 17% of the human genome and is the only autonomously active retro-transposable element in humans. The L1 promoter is bidirectional and consists of a sense promoter and an antisense promoter, both of which drive the transcription of the two proteins required for reverse transposition(Wheelan, Aizawa et al. 2005, Burns and Boeke 2012, Huang, Burns et al. 2012)

The L1 sense promoter exhibits high transcriptional activity in many cancers, resulting in increased expression of L1 ORF1 and protein abundance(Criscione, Zhang et al. 2014, Rodić, Sharma et al. 2014). On the other hand, the L1 antisense promoter drives the transcription of chimeric transcripts that splice onto exons of neighboring genes, generating new splicing sites and gene isomers(Speek 2001) (Miglio, Berrino et al. 2018). This can enhance or inhibit the expression of genes associated with insertion sites and affect the human transcriptome.

For example, the transcriptional decompression of the L1 chimeric transcript LCT13 is related to the silencing of homologous transcript TFPI-2, a tumor suppressor in various human malignancies(Cruickshanks, Vafadar-Isfahani et al. 2013). However, the effects of L1 chimeric antisense transcripts on the human transcriptome and the role of L1 antisense promoter-driven genes in the development of human tumors are not fully understood.

In this study, we comprehensively examined the differential expression, immune infiltration, methylation levels, and copy number variation of 903 genes(Criscione, Theodosakis et al. 2016) associated with L1 chimeric antisense transcripts in 23 common tumors. Our findings shed light on the potential role of L1 antisense promoter-driven genes in tumor development and the impact of L1 chimeric transcripts on the human transcriptome.

Our study revealed that numerous L1 ASP-driven genes exhibited significant differential expression in most tumors compared to normal tissues (p < 0.01). Specifically, we identified over 100 L1 ASP-driven genes that were highly expressed in 11 tumors, such as LUSC, KIRC, CHOL, LUAD, UCEC, and LIHC, indicating that aberrant activity of the L1 antisense promoter may play a role in tumor development. Moreover, we observed that the expression specificity of certain L1 ASP-driven genes, such as CENPU, was associated with the prognosis of tumor patients in various tumors. Previous studies have shown that CENPU participates in tumor proliferation and metastasis, and the results of our study are consistent with these findings. We also discovered that the L1 ASP-driven genes were closely linked to the PI3K-AKT signaling pathway, cell cycle, and cell senescence. Abnormal activation of the PI3K/AKT pathway was known to be involved in tumor development.

Furthermore, we found that the dysregulation of L1 ASP-driven genes was associated with the formation of the tumor immune microenvironment, and this provided a new perspective for targeted immunotherapy. Specifically, our analysis of hub gene CENPU and MCM2 indicated that their high expression was negatively correlated with the prognosis of multiple tumors and the immune infiltration of tumors. These results provide valuable insight into the role of L1 ASP-driven genes in tumor development and may become the new potential therapeutic target. Of course, More research on mechanisms is needed.

Abnormal gene expression can be attributed to various factors, and our study found that L1 antisense promotor-related genes are differentially expressed in most tumors compared to normal tissues. Specifically, we observed that more than 100 L1 ASP-driven genes were highly expressed in 11 different tumors, including LUSC, KIRC, CHOL, LUAD, UCEC, and LIHC. This suggests that the abnormal activity of the L1 antisense promoter may be involved in tumor development. Furthermore, we found that the specific high expression of these genes was closely related to the prognosis of tumor patients. For instance, the expression of CENPU, a gene involved in the proliferation and metastasis of various tumors, was found to be closely related to patient prognosis in 15 different types of cancer.

Our study also discovered that differentially expressed genes related to L1 and ASP were closely associated with the PI3K-AKT signaling pathway, cell cycle, and cell senescence. These pathways play an important role in normal cell activities such as growth, proliferation, and metabolism, as well as apoptosis, and its abnormal activation is known to contribute to tumor development.

To confirm our results, we conducted cell function experiments and found that the knockdown of LINC00491 significantly inhibited the proliferation and migration of ovarian and thyroid cancer cell lines. It has been reported that LINC00491 plays a cancer-promoting role in various other cancers as well. Additionally, multiple L1 ASP-driven genes have been identified as potential biomarkers or therapeutic targets for tumors. For instance, the immunotherapeutic effect of colorectal cancer patients with high expression of SPP1 is closely related.

Although our study has made significant contributions to the understanding of the relationship between L1 ASP-driven genes and tumorigenesis, further research is needed to validate our findings. Due to limitations such as time and experimental conditions, we were unable to explore the specific mechanism of L1 ASP-driven genes in detail or verify our results in animal and clinical specimens. We aim to conduct more in-depth studies in the future.

Our research has uncovered crucial insights into the dysregulation of L1 antisense promotor-related genes across various types of cancer, revealing their strong connection to the development of tumors. These discoveries represent a significant expansion of our current understanding of L1 ASP-driven genes and have far-reaching implications for comprehending the evolution of the human genome. Moreover, our findings suggest that L1 ASP-driven genes could hold promising potential as both diagnostic markers and therapeutic targets for cancer treatment. By shedding light on the critical role played by these genes in tumor development, our study could help pave the way for more effective and personalized cancer therapies in the future.

Data acquiring

The RNA sequencing (RNA-seq) data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov) through the UCSC Xena tool. The differentially expressed genes (DEGs) were selected using the criteria: P < 0.05 using the R package DESeq2. The data were presented in a volcano plot using the HIPLOT tool (https://hiplot.com.cn).

The gene list of L1 ASP-driven genes was identified previously by Criscione et al(Criscione, Theodosakis et al. 2016).

Survival Analysis

Survival analysis was performed in Sangerbox 3.0 (http://sangerbox.com/home.html). The patient's survival status and survival data were obtained from the TCGA database and based on Cox regression analysis to calculate the risk ratio of the relevant genes in each tumor, in the Cox univariate study, we used the R package survival, integrating survival time, survival status, and gene expression data. We also used the Cox multivariate study, in which we used the R package survival, integrated data on survival time, survival status, and characteristics of Lasso Cox analysis, and assessed the prognostic significance of these features using the Cox method.

Kaplan-Meier (KM) curve

We calculated the best cut-off value of RiskScore using the R package maxstat (maximally selected rank statistics with several p-value approximations, version 0.7–25), setting the minimum group sample size greater than 25% and the maximum group sample size less than 75%. Based on this the patients were divided into high and low groups, and the prognostic difference between the two groups was further analyzed using the surfeit function of R package survival.

Receiver Operating Characteristic (ROC)curve: We performed ROC analysis using the R package pROC (version 1.17.0.1) to obtain AUC. We obtained the follow-up time and survival status of patients, performed ROC analysis at 365-, 1095-, and 1825-days’ time points using the ROC function of pROC, and evaluated AUC and confidence intervals using the ci function of pROC to obtain the final AUC results. The time-varying ROC curve and AUC curve were used to evaluate the prediction ability and discriminant ability of the L1-ASP-driven gene model.

Protein-protein Interaction Analysis

Protein-protein interactions (PPIs) were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins dataset (STRING, version 11.5) (https://cn.string-db.org).

Pathway And Functional Enrichment Analyses

For enrichment of gene set function analysis, we use KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html) to obtain the latest KEGG Pathway gene annotation, as the background, to map genes to the background in the collection, Enrichment analysis was performed using the R package “cluster Profiler”(Yu, Wang et al. 2012) (version 3.14.3) to obtain gene set enrichment results. The minimum gene set was set as 5 and the maximum gene set as 5000, with P value < 0.05 and FDR < 0.25 being considered statistically significant.

Gene Set Enrichment Analysis (GSEA) was conducted using the "gseKEGG" and "GSEA" functions in the "cluster profiler" R package with gene sets involved in KEGG pathways. KEGG pathways were downloaded from the Molecular Signatures Database (http://software.broadinstitute.org/gsea/msigdb/index.jsp)(Liberzon, Birger et al. 2015). The false discovery rate (FDR) q-value < 0.05 and the NES (net enrichment score) > 1 were set as the significant thresholds.

Immune Infiltration Analysis

We downloaded a standardized pan-cancer dataset from the UCSC, and the R package ESTIMATE was further used to calculate stromal, immune, and ESTIMATE scores for each patient in each tumor according to gene expression. Pearson's correlation was used to calculate the coefficient of gene and immune infiltration score in each tumor to determine the significant correlation of immune infiltration score.

Methylation Analysis

DNA methylation data were obtained from Xena(https://xenabrowser.net/datapages/), and only those probes that mapped to the promoter region of the L1 ASP-Driven top 20 genes were used for subsequent analysis. For genes containing multiple probes, the mean β value of all probes was used as the methylation level. Only 20 tumor types with at least five tumor–normal pairs were retained in the differential methylation analysis, and fold changes and p values were calculated using the same method as that used for differential expression analysis.

Somatic Copy Number Alteration And Mutation Analysis

Genetic alteration data of MCM2 and CENPU, including alteration frequency, mutation type, and mutated site, were analyzed by the cBioPortal database(https://www.cbioportal.org/). The Cancer Types Summary Module was used to obtain the alteration frequency and mutation type of genes in TCGA tumors.

Cell Culture And Transfection

The human ovarian cancer cell line OV8 and thyroid carcinoma cell line BHT101 were obtained from the American Type Culture Collection (ATCC, USA) and cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. The cells were incubated at 37°C in a humidified incubator containing 5% CO2.

For transient transfections, Lipofectamine 2000 (Invitrogen) reagent was used according to the manufacturer's instructions. Small interfering RNAs (siRNAs) targeting LINC00491 were synthesized by Gene Pharma (Suzhou, China) and transfected using Lipofectamine 2000 reagent (Invitrogen, Carlsbad, USA) following the manufacturer's protocol. The sequences of the siRNAs used in the experiment were shown in Table S6 of supplement material.

Mtt Assay

OV8 and BHT101 cells were plated in 96-well plates with 200 µl media containing 10% FBS and cultured for 5 days. Cell viability was measured at 24, 72, and 120 hours using the MTT assay, which measures the metabolic activity of live cells. The absorbance was measured at a wavelength of 570 nm. The experiment was performed in triplicate to ensure the accuracy of the results.

Colony Formation Assay

The knockdown cells were seeded into 6-well plates with 1000 cells per well and routinely maintained for 1–2 weeks. Once colonies (> 50 cells per colony) were formed, they were washed with PBS, fixed with 4% paraformaldehyde, and stained with 0.1% crystal violet. The number of colonies was counted using Image J software, and all assays were performed in triplicate to ensure the accuracy of the results.

Transwell Migration Assay

The cell suspension without FBS was seeded into the upper chamber with 8x104 cells per well, while 600µl of medium containing 10% FBS was applied at the bottom of the chamber. After incubation, the cells were fixed with 4% paraformaldehyde, and stained with 0.1% crystal violet. Non-migrated cells that remained at the top layer were removed using a cotton swab, and migrated cells at the bottom of the chamber were observed and counted under a light microscope.

Rna Extraction And Rt-qpcr

Total RNA was extracted using Trizol Reagent (Invitrogen, #15596018), and 1000ng of the isolated RNA was used for reverse transcription with the PrimeScript™ RT reagent Kit (Takara, #RR047A). The cDNA templates were then analyzed using the Roche LightCycler 480II qPCR System. To ensure reliable quantification, the expression levels of each gene were normalized to GAPDH as an internal reference. Gene-specific primers synthesized by IGE Technologies and their sequences can be found in Table S6 of the supplementary material.

LINE-1 or L1: Long interspersed nuclear element-1

UTR: untranslated region

ASP: antisense promoter

TEs: transposable elements

LTR: Long Terminal Repeat

OS: Overall Survival

The abbreviations of cancer names are provided in Table S7 of the supplementary material.

Statistical analysis

Quantitative data were presented as the mean ± the standard deviation (SD) from a minimum of three independent experiments. Comparisons between the two groups were analyzed using Student’s t-test. Statistical analyses were performed with GraphPad Prism 8 (GraphPad Software Inc.) and p < 0.05 was statistically significant.

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by grants from the Guangdong Science and Technology Department (2023A1515010411 to Y.Z).

Authors’ contributions

BX, XL, and SZ contributed to data analysis, validation of results, and visualization. ML and WH performed the statistical analysis and cell examination assay. YZ was a major contributor to writing the manuscript. ZH and YW performed the editing, conceptualization, and supervision of the draft. All authors read and approved the final manuscript.

Acknowledgments

Not applicable.

Ethics approval and consent to participate.

Not applicable.

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No competing interests reported.

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Comprehensive characterization of LINE-1 antisense promoter-driven genes across human cancers

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Materials And Methods

Data acquiring

Survival Analysis

Protein-protein Interaction Analysis

Pathway And Functional Enrichment Analyses

Immune Infiltration Analysis

Methylation Analysis

Somatic Copy Number Alteration And Mutation Analysis

Cell Culture And Transfection

Mtt Assay

Transwell Migration Assay

Rna Extraction And Rt-qpcr

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