Long Non-Coding RNAs Related to N6-Methyladenosine and Immune Cell Infiltration in Cervical Carcinoma: A Comprehensive Analysis

Long Non-Coding RNAs Related to N6-Methyladenosine and Immune Cell Infiltration in Cervical Carcinoma: A Comprehensive Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long Non-Coding RNAs Related to N6-Methyladenosine and Immune Cell Infiltration in Cervical Carcinoma: A Comprehensive Analysis Lidan Lu, Neng Bao, Qingxue Wei, Ximei Cai, Hongjian Ji, Haiyan Ni, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3319964/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : The aim of this study was to explore the effect of N 6 -methyladenoxin (m6A-lncRNAs) on the prognosis of cervical cancer (CC). Method : The Cancer Genome Atlas dataset was used to comprehensively analyze the prognostic value of m6A-lncRNAs in cervical carcinoma (CC) and their relationship to tumor microenvironment. These data were then used to generate a prognostic model by LASSO regression. Finally, all prognosis-related m6A-lncRNAs were validated in HeLa cells. Results : A total of 87 m6A-lncRNAs were found to be significantly associated with the overall survival of patients with CC. Two subtypes were isolated by clustering the 87 prognostic m6A-lncRNAs. Cluster 1 performed better in terms of patient survival than cluster 2. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of CC using gene set enrichment analysis revealed that the ADHESION_JUNCTION pathway was the most active in Cluster 1, while the OXIDATIVE_PHOSPHORYLATION pathway showed higher activity in Cluster 2. The clinical correlation heatmap and boxplot showed differences in age, tumor grade, immune characteristics, and clustering. Thirteen prognosis-related m 6 A-lncRNAs were identified by LASSO regression, and of these, LINC006 and NNT-AS1 were finally validated through in vitro studies. Conclusion : Prognostic m 6 A-lncRNA markers could be an important mediator of the immune microenvironment of CC and a potential target for immunotherapy. Bioinformatics analysis N6-methyladenosine lncRNAs Immune cell infiltration Cervical cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cervical cancer (CC) is a prevalent gynecological cancer that poses a significant risk of morbidity and mortality among women. CC ranks fourth in the worldwide cancer incidence rate among all female malignant tumors[ 1 ]. The primarycause of CC is human papillomavirus (HPV) infection[ 2 ]. In the last few years, the overall mortality rate of CC has decreased because of the readily available vaccine for HPV[ 3 ]. However, the mechanisms by which HPV causes CC and the 5-year survival rate of patients with high-risk CC need further clarification[ 4 ]. Therefore, it is worthy to conduct a thorough investigation of the pathophysiology of CC to determine its clinical management. N 6 -methyladenosine (m 6 A) is the most prevalent reversible mRNA modification found in mammals[ 5 , 6 ], and which regulates mRNA metabolism[ 7 , 8 ], guides stem cell differentiation, and promotes embryonic development[ 9 , 10 ]. Thus, the modification of the m 6 A segment is one of the most common internal regulation modes in mRNAs[ 11 ]. Most m 6 A modifications are categorized as writers, erasers, and readers. Writers methylate m 6 A through methyltransferases such as METTL3 and METTL14[ 12 ]. Erasers demethylate m 6 A through demethylases such as FTO and ALKBH5[ 13 ]. Readers are primarily responsible for recognizing m 6 A-binding proteins. The common binding proteins include YTHDF1 and IGF2BP1[ 8 , 14 ]. There is a significant correlation between the level of m6A and the activity of tumor associated markers[ 15 ]; This indicates that m6A has the potential to function as a novel therapeutic target for precise tumor treatment[ 16 ]. Long noncoding RNAs (lncRNAs) are segments of mRNA that do not code for proteins and have a length exceeding 200 nucleotides[ 17 ]. lncRNAs are primarily localized in the cell cytoplasm and competitively bind to microRNAs to prevent their function. The main functions of lncRNAs are construction of the nuclear structure, regulation of cis or trans transcription, encoding of post-transcriptional modifications, etc.[ 18 ]. Thus, lncRNAs can play a role in tumor occurrence, migration, and invasion[ 19 ]. For example, elevated expression of the long non-coding RNA HOX transcript antisense RNA (HOTAIR) can worsen the prognosis of hepatocellular carcinoma[ 20 ], and the lncRNA H19 is an important gene for malignant transformation and metastasis of gastric cancer[ 21 ]. Not surprisingly, lncRNAs also play a significant role in the pathogenesis of CC. For example, the degradation of the lncRNA FENDRR drives the proliferation of endometrial cancers[ 22 ]. The exact role of CC-related lncRNAs, however, remains unclear. In this study, we conducted an analysis of cervical cancer data from the Cancer Genome Atlas (TCGA), with a particular emphasis on the expression of long noncoding RNAs (lncRNAs) and their correlation with the prognosis of CC. The obtained findings were summarized to describe the most important factors for the prognosis and treatment of CC in clinical practice. In addition, we performed the spot hybridization and the RNA immunoprecipitation (MeRIP) followed by the quantitative real time PCR (qRT-PCR) to determine the levels of m6A-lncRNAs that were identified in HeLa cells. 2. Materials and methods 2.1 Data sources Genome data was retrieved from Genomic Data, and TCGA was used to obtain clinical cancer gene profiles and clinical data. As of November 2021, the TCGA has compiled the profiles of 556 CC and 23 normal tissue samples. The Perl software was used for data organization and gene ID conversion. As per the guidelines published by the National Cancer Institute, ethics committee approval was not required. 2.2 Determination of m 6 A methylation related to lncRNAs Based on the literature, 23 m6A RNA methylation regulator was obtained[ 8 , 23 ]. The definition of a lncRNA is that any non-coding long chain RNA on the GENCODE Web site. To quantitate lncRNA expression and m 6 A modification, A co-expression system was constructed using the “iGraph” package in R to identify lncRNAs that play a role in m6A methylation. The “survival” package was employed to assess the prognostic value of m6A-lncRNAs through univariate Cox analysis and construct a forest plot. The relationship between lncRNA expression and CC development was visualized using a heatmap. 2.3 Bioinformatics analysis We utilized the “consensus cluster plus” software package and the “limma” software package to classify surgical cancer cases into two cluster subtypes (cluster Alg = km and cluster Num = 2). The lncRNA cluster subtypes were analyzed using the “survminer” package. Differences in m6A-lncRNAs expression and their association with prognosis in each cluster subtype were assessed with the heatmap package. Gene set enrichment analysis (GSEA) was performed to investigate functional and pathway differences among various clinical cancer subtype. The simulation value was set as 1000, and the FDR value was set as 0.05. In order to estimate the penetration of different immune cells in tissue samples, we used PreprocessCore”, “limma” and “e1071” packages to detect infiltration of certain immune cells. Differences in the extent of immune cell infiltration among subgroup clusters were analyzed with the “limma” package, and the results were visualized using vioplot. Moreover, the “limma” package was used to evaluate the infiltration of various immune cell types in each subgroup cluster of CC, with the results presented as a boxplot. Next, the “limma” package was used for the tumor microenvironment (TME) and assess differences in purity among different subtype. Perl software was used to import data, and GSEA was performed to determine differences in the relevant functions and pathways among the different samples. Each sample was classified as “H” or “L” based on whether it belonged to the high-risk or low-risk category of lncRNAs associated. 2.4 Establishment of the m 6 A-lncRNA-related prognostic model We used LASSO regression to build the prognosis model. Then, the samples were classified into high risk and low risk according to the m6A-lncRNA risk score. The training (50%) and test (50%) groups were defined and the precision of the model in predicting CC survival was evaluated using receiver operating characteristic (ROC) curves. The hazard curve, survival status and risk for m6A-lncRNAs were assessed on the curve. Independently prognostic analysis was performed to determine if the model could function independently of other possible prognostic factors. The risk ratio was calculated by multivariate and univariate analysis. By conducting model validation for clinical groups, we tested whether our model was appropriate for application to various groups. We conducted risk analysis and clinical association analysis to identify different risk m6A-lncRNAs using the “heatmap” and “limma” packages. Furthermore, a boxplot of risk and clinical relevance was generated based on the clinical data. Finally, we examined the correlation between immune cells and the risk score. 2.5 m 6 A-lncRNA validation 2.5.1 Cell culture Cells of HeLa were cultured in DMEM medium (11995, Solarbio) containing 10% fetal bovine serum (10099141C, Gibco) and 1% penicillin-streptomycin (C0222, Beyotime). Mycoplasma contamination tests were routinely performed. The Lipofectamine 3000 reagent (L3000150, Invitrogen) was used to transfer the plasmids to the cells. 2.5.2 Quantitative real-time PCR Total RNA was extracted with TRIzol reagent (Merck KGaA, Darmstadt, Germany). The QuantiTect Reverse Transcription kit was used to reverse transcription. PCR was performed on a StepOne Plus cycler with an AceQ qPCR SYBR Green Master Mix (Vazyme, Nanjing, China). The GAPDH was used to standardize gene expression, and the 2- △△ CT was calculated. The PCR primer sequences used in this study are listed below: Inc00662 (F:CATCCCAAGCAAAGGTCCCT;R:TCAGGGTCTGCAGGACAAAC); HM13-IT1 (F: ACACCATCAGCCCCTTCATG;R:ATGTAGGCTGCAAAGCTGGT) ; LINC01936 (F:GAGGTCCAACCCCATTCCTG;R:CATGCCTGTAATCGCAGCAC) ; ELOA-AS1 (F:AGCCTCCCAAGCAGATAGGA;R: AAAGATCCCTGCCTTGGAGC) ; NNT-AS1 (F:AACCCAGCCATTCATGAGGG;R:CCGGGTTCAAGCCATTCTCT; SLC16A1 (F:CTGCAGTCCAACGACAGAGT;R:TCCATTTAGCGGTGCAGTGT) ; AP003096 (F:GCAGTGAAACCCATCTGATTAAGA;R:GGGGAGCTCAGCTAAGTAGG) ; AL645568 (F:CCCGTACCAATGTCAGTGCT;R: ATCAAGCAGCTTGGAACGGA) ; AC244517 (F:CACCTTGGTCACCAGGTAGC;R:CACCATCACTGTCACCGACA) ; AC011466 (F:GGAAGGAATGGAGCCTTGCT;R:GACAAGTGCACGGTTTGACC) ; AC010761 (F:CACTGGTTGGAGCTCCATGT;R: TCTTCTCTCCATCAGGCCGA) ; AC010260 (F:CCCTGGGTCCAAAATCGACA;R:AAGGAGCCAAATGGAGTCCG) ; AC002094 (F:AACCCAGAACTAGTTGCCCG;R:CTTCTCTACCATGCGTCGCT) ; GAPDH (F:GAAAGCCTGCCGGTGACTAA;R:FGCGCCCAATACGACCAAATC). 2.5.3 Spot hybridization test Total RNA was separated by TRIzol, and the RNA was denatured at 95 ℃ for 5 minutes. Next, 2 µL RNA sample was loaded onto a nylon membrane, UV crosslinked, and then incubated at 4℃ with m6A antibodies overnight. After incubation with secondary antibodies, the chemiluminescence system was used to detect signals. 2.5.4 Transwell assay A Transwell insert (0.8 µm, TCS003024, BIOFIL) was used to determine the invasion of HeLa cells. Following transfection for 24 h, 5 × 10 4 cells were starved in 200 mL serum-free medium and then seeded onto plates. After transfection and incubation, cells were allowed to migrate to the bottom of the filter membrane. Subsequently, the cells were stained using a 0.1% crystal violet solution (G1063, Solarbio) and images were captured with an IX73 microscope (OLYMPUS). Finally, the absorbance was measured at 570 nm with a micro-plate reader (SYNERGY H11, BIOTEK, USA). The whole experiment was repeated at least three times. 2.5.5 Prediction of the m 6 A modification site The putative m6A modification sites of NEAT1 were predicted using SRAMP. 2.5.6 MeRIP‑qPCR. The Magna MeRIPTM m6A kit was employed to perform MeRIP for evaluating the m6A modification of genes. Briefly, 10 µL aliquot of anti-m 6 A antibodies was incubated at 4℃ overnight with the chromatin immunoprecipitation (ChIP) grade protein A/G. Next, 300 µL of total RNA fragments were incubated with antibodies in an IP buffer containing protease inhibitors and RNase inhibitors. The RNA modified with m6 A was eluted and extracted for quantitative RT-PCR analysis. 2.5.7 Statistical analysis The Statistical Product and Service Solutions (SPSS) version 20.0 (SPSS Inc. Armonk, New York, USA) were used for the production of graphs. The data were given as mean ± SD. Each test was performed in a minimum of 3 separate trials or replicates. Multiple comparisons between groups were performed using appropriate methods. Student's t-test, one-way ANOVA, and the S-N-K method were used for statistical analysis. A p-value of < 0.05 was found to be statistically significant. 3. Results 3.1 Acquisition of m 6 A-lncRNAs We extracted m 6 A-related genes from the sorted transcriptome data and differentiated mRNAs from lncRNAs. Based on these data, the influence of the expression of m6A gene on the lncRNAs was highlighted by a network diagram (Fig. 1 A). Univariate Cox regression analysis results were presented as forest plots ( Sup. 1A ). Significance levels of p < 0.05 were used to identify those associated with m6A Prognosis. A heatmap and a boxplot (Fig. 1 B) were then used to highlight differences in m6A-lncRNAs were found in normal and malignant specimens. Ultimately, we identified 87 m6A-related prognostic genes exhibiting distinct expression patterns between tumor tissues and normal samples. 3.2 Role of m 6 A-lncRNAs We searched prognosis-related lncRNAs from the clinical data and generated a forest map ( Sup. 1B ) and heatmap (Fig. 1 C). lncRNAs with a p -value of < 0.05 were considered to be related to CC prognosis. In the analysis of the expression level of lncRNAs, a level of K = 2 was assumed as the lowest level of overlap. As a result, the lncRNAs were separated into two groups (Fig. 2 A). However no significant difference in survival rates or expression of prognosis-related lncRNAs was observed between the two clusters. ( p > 0.05, Fig. 2 B). The heatmap generated according to the clusters also showed the same result (Fig. 2 C). 3.3 Immune cell infiltration and TME role A vioplot (Fig. 2 D) shows the characteristics of immune modulation in the clusters. Except for M1 macrophages ( Sup. 2A ) that were preferentially found in cluster 1 ( p 0.05). Based on the block diagram ( Sup. 2B-D ), the TME was analyzed among different sub-types of samples, and the purity of tumor cells was determined. The GSEA revealed variations in the associated functional pathways between the two clusters. The first six enriched functions of each cluster are given. Both the FDR Q value and FWER p value were significant. The pathways most related to the two clusters were “ADHESION_JUNCTION” (Fig. 2 E) and “OXIDATIVE_PHOSPHORYLATION” (Fig. 2 F), respectively. 3.4 Establishment of an m 6 A-lncRNA-related prognostic model The LASSO regression was used to construct the prognosis model, and samples were divided into the training group (50%) and test group (50%). Figure 3 A-B show the coefficient and partial likelihood deviance of the prognostic signatures. Figure 3 C-D show a comparison of the survival curves of the different risk groups. Low-risk patients were more likely to survive than high-risk group ( p 0.5, which validated the accuracy of our model. A risk curve was then generated to determine the relationship between m6A-lncRNAs risk score and patient survival (Fig. 3 G-I). An increased risk score was associated with higher patient mortality. We evaluated whether our model was an independent factor affecting the prognosis of patients by conducting an independent prognostic analysis ( Sup. 3A-B ). We found that the grade of tumor was an independent risk factor for prognosis of CC ( p < 0.05). Clinical group assessment was performed to determine whether the established model was clinically relevant, and 13 prognosis-related m6A-lncRNAs were detected (Fig. 4 A). This evaluation confirmed that the model could be applied to different Clinical groups of patients by age, sex, and Grade (Fig. 4 B-D). 3.5 Validation of prognosis-related m 6 A-lncRNAs HeLa cells were used to further confirm the abovementioned results. QRT-PCR was used to evaluate the level of mRNA expression for 13 prognosis-related m6A-lncRNAs. The levels of mRNA expression of the NNT-AS1, LINC006 Groups exhibited a significant difference in comparison with control group ( p < 0.05, Fig. 5 A). Spot hybridization was conducted to detect the total expression of m6A in LINC006 and NNT-AS1 in HeLa cells. The total expression of m6A in the expression levels of LINC006 and NNT-AS1 in HeLa cells were higher than those of normal cells ( p < 0.05, Fig. 5 B). We also analyzed the LINC006- and Migration Regulation by NNT-AS1-mediatedAnd invasion of HeLa cells. The results revealed a significant delay in the migration and invasion of HeLa cells after LINC006 and NNT-AS1 knock-out (Fig. 5 C-D), thus indicating that these two properties of HeLa cells were regulated by the two abovementioned genes. SRAMP prediction analysis showed abundant m6A modification sites in LINC006 and NNT-AS1, thus indicating that both genes were highly likely to be modified by m6A methylation (Fig. 6 A-B). MeRIP results also confirmed that because the levels of LINC006 and NNT-AS1 in the m6A group were significantly higher, LINC006 and NNT-AS1 were modified by m6A (Fig. 6 C-D) 4. Discussion m 6 A plays a key role in CC. Wang et al. analyzed the RNA transcriptome sequences from the TCGA database and found that m 6 A is closely related to malignant tumor formation; thus, they suggested that m 6 A could be used as a biomarker for diagnosing various diseases[ 24 ]. Zhang et al analyzed the amount of protein and gene expression in tissues and cells of patients with endometrial cancer and found that m6A plays a Central role in malignant transformation. Insulin-like growth factor 2 (IGF2BP1) regulates cancer formation by competing with m 6 A sites[ 25 ]. Ni et al. examined the pathological specimens of 177 patients with CC and found that the expression of METTL3, one of the m 6 A members, was significantly increased in patients with advanced CC; this finding confirmed that m 6 A is an independent diagnostic factor for CC [ 26 ]. Zou et al. analyzed the TCGA database and genotype tissue expression (GTEX) data and showed that m 6 A is an important diagnostic marker of uterine cancer, wherein METTL16 and IGF2BP1 are two important regulatory factors[ 27 ]. In the last 2 years, some authors have proposed that m 6 A-related lncRNAs (m 6 A-lncRNAs) could be used as a marker for tumor diagnosis and treatment. For example, Zhong et al. analyzed the genomic and clinical data from the TCGA database and revealed 22 m 6 A-lncRNAs of high significance for diagnosing hepatocellular carcinoma. The transmembrane domain-containing member 3 (CMTM3) was also found to be overexpressed in tumor tissues[ 28 ]. A recently published study of Li et al. showed an association between the diagnosis and prognosis of bladder cancer and various m6A-lncRNAs. In addition, it has been demonstrated that certain genes (e.g., PD-L1) or pathways (JAK-STAT) play a significant role in the disease [ 29 ]. In the present study, CC-related transcriptome data were extracted from the TCGA database and sorted. Subsequently, the expression matrix of lncRNAs and m6A was created. A network diagram of m 6 A and lncRNAs was constructed using the results of univariate Cox regression analysis. We then combined the abovementioned m 6 A-lncRNAs with the survival time of clinical patients to obtain 87 prognosis-related m 6 A-lncRNAs. These results were represented by a forest plot and a heatmap. Previous studies have suggested that m 6 A-lncRNAs are a diagnostic marker of tumors[ 30 , 31 ]. The results of our present study confirmed this assumption. We then clustered and typed these prognostic m 6 A-lncRNAs (K = 2) and used them as the basis for classifying the clinical samples. Significant differences were observed in the clinical samples, and a violin diagram of immune cells indicated differences in the distribution of M1 macrophages between the groups. KEGG pathway enrichment analysis of CC by GSEA showed that the “ADHESION_JUNCTION” pathway was the most active in cluster 1, while the “OXIDATIVE_PHOSPHORYLATION” pathway was the most active in cluster 2. The adhesion junction is an important mode of physical connection between cells. It connects adjacent plasma membranes via cadherin receptors and plays an important role in tissue morphogenesis and remodeling. Li et al. reported tumor tissues from cancer patients and found a positive correlation between the high mobility group box 1 protein (HMGB1) and tumor malignancy. Further analysis showed that “ADHESION_JUNCTION” may be an important molecular pathway for HMGB1 to regulate diseases[ 32 ]. Oxidative phosphorylation is a biochemical reaction that occurs in mitochondria, and it phosphorylates ADP to synthesize ATP through electron transfer. Tango et al conducted a retrospective analysis of clinical swab samples from 92 CC patients and observed a higher abundance of oxidative phosphorylation in tumor cells [ 33 ]. Jonsson et al reported that oxidative phosphorylation promoted the replication of mitochondria in CC cells and HeLa cells to promote radiation resistance[ 34 ]. Therefore, an investigation of the role of oxidative phosphorylation pathways in cancer pathogenesis is worthy of future study. In this study, we employed LASSO Cox analysis to study the relationship between prognosis and m6A-lncRNAs, utilizing the risk score. Based on a median risk score for all m6A-lncRNAs, we separated the patients into high-risk and low-risk groups and conducted follow-up. The survival rate showed a significant difference between the two groups, with the high-risk group exhibiting a lower survival rate. The ROC value of > 0.65 further confirmed the accuracy of this prediction method. The clinical correlation heatmap and the boxplot showed differences in the age, tumor grade, immune signature, and clustering; this finding indicated that grouping according to the m 6 A-lncRNA risk score has clinical significance. These results also confirmed that m 6 A-lncRNAs affect the prognosis of CC. The TME plays a key role in cancer development and treatment resistance[ 35 , 36 ]. In the risk correlation heat map, 13 m 6 A-lncRNAs with differential expression between the high-risk and low-risk groups, including AP003096.1, HM13-IT1, LINC00662, and NNT-AS1. The gene expression levels increased with higher risk scores. To confirm the involvement of the abovementioned 13 prognosis-related m 6 A-lncRNAs in CC, we performed qRT-PCR using HeLa cells. The results revealed differential expression levels of LINC00662 and NNT-AS1. LINC00662 accelerates the proliferation and infiltration of tumor cells, and it has been shown that there is a close relationship between the progression of various cancers. For example, LINC00662 worsens the condition of gastric cancer by regulating the Hippo-YAP1 pathway[ 37 ]. Other studies showed that LINC00662 enables prostate cancer cells to easily enter the advanced stage by binding to miR-34 to increase its activity during prostate cancer lesion development[ 38 ]. Cheng et al reported that LINC00662 can accelerate the proliferation and invasion of tumor cells by activating ERK signaling pathway via CLDN8/IL22 complex[ 39 ]. In the present study, we performed the Transwell assay and confirmed that HeLa cells showed a significant decrease in their migration and infiltration ability following LINC00662 knockout. Dot blot hybridization and MeRIP-qPCR results demonstrated that LINC00662 was methylated in HeLa cells; this finding confirmed that LINC00622 regulated CC as an m 6 A-lncRNA. NNT-AS1 is a lncRNA located in the chromosome 5p12 region, and it plays a key regulatory role in the proliferation, metastasis, invasion, and cancer cell apoptosis[ 40 ]. Recent studies by He et al. revealed that NNT-AS1 accelerates epithelial-mesenchymal transition and tumor cell proliferation, migration, and invasion by regulating YAP1 activity [ 41 ]. Similarly, Wang et al. found that NNT-AS1 expression in colorectal cancer is associated with patient survival and may enhance colorectal cancer cell proliferation and metastasis by regulating the MAPK/ERK signaling pathway[ 42 ]. In our cellular experiments, we observed significantly higher NNT-AS1 mRNA expression in HeLa cells compared to normal cells. The Transwell assay also demonstrated a notable reduction in HeLa cell migration and invasion after NNT-AS1 deletion. Dot blot hybridization and MeRIP-qPCR further confirmed that NNT-AS1 underwent m 6 A methylation. 5. Conclusion Our study provides confirmation that m6A-lncRNAs are significantly associated with the prognosis of CC. The ADHESION_JUNCTION and OXIDATIVE_PHOSPHORYLATION pathways were identified as important factors in the development of CC. Among the thirteen prognosis-related m6A-lncRNAs, LINC006 and NNT-AS1 were further validated through in vitro experiments. Declarations 6. Ethics approval and consent to participate This study did not involve humans or animals, no ethical approval was required. 7. Consent for publication Not applicable 8. Data Availability All data generated or analyzed during this study are included in this published article. 9. Conflicts of Interest All authors declare that they have no conflicts of interest. 10. Authors’ Contributions Lidan Lu and Neng Bao performed data analysis and assisted in manuscript writing. Peijuan Wang designed the study. Qingxue Wei, Hongjian Ji, Haiyan Ni, and Ximei Cai assisted in manuscript writing. All authors have read and approved the final manuscript. 11. Funding This study was jointly supported by the Natural Science Foundation Project of Nanjing University of Traditional Chinese Medicine (No. XZR2021094); the Suzhou Science and Technology Development Project (No. SKY2022073); the Suzhou Integrated Traditional Chinese and Western Medicine Research Fund (No. SYSD2020224); the Suzhou Science and Technology Development Plan Guiding Project (No. SKJYD2021177), and the Changshu Science and Technology Development Project (No. CS202215). 12. 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Cell 2015, 162 (6):1299-1308. Huarte M: The emerging role of lncRNAs in cancer . Nature medicine 2015, 21 (11):1253-1261. Li P, Xu M, Cai H, Thapa N, He C, Song Z: The effect of HMGB1 on the clinicopathological and prognostic features of cervical cancer . Bioscience reports 2019, 39 (5). Tango CN, Seo SS, Kwon M, Lee DO, Chang HK, Kim MK: Taxonomic and Functional Differences in Cervical Microbiome Associated with Cervical Cancer Development . Sci Rep 2020, 10 (1):9720. Jonsson M, Fjeldbo CS, Holm R, Stokke T, Kristensen GB, Lyng H: Mitochondrial Function of CKS2 Oncoprotein Links Oxidative Phosphorylation with Cell Division in Chemoradioresistant Cervical Cancer . Neoplasia (New York, NY) 2019, 21 (4):353-362. Fridman WH, Pagès F, Sautès-Fridman C, Galon J: The immune contexture in human tumours: impact on clinical outcome . Nature reviews Cancer 2012, 12 (4):298-306. Hui L, Chen Y: Tumor microenvironment: Sanctuary of the devil . Cancer letters 2015, 368 (1):7-13. Liu Z, Yao Y, Huang S, Li L, Jiang B, Guo H, Lei W, Xiong J, Deng J: LINC00662 promotes gastric cancer cell growth by modulating the Hippo-YAP1 pathway . Biochem Biophys Res Commun 2018, 505 (3):843-849. Li N, Zhang LY, Qiao YH, Song RJ: Long noncoding RNA LINC00662 functions as miRNA sponge to promote the prostate cancer tumorigenesis through targeting miR-34a . European review for medical and pharmacological sciences 2019, 23 (9):3688-3698. Cheng B, Rong A, Zhou Q, Li W: LncRNA LINC00662 promotes colon cancer tumor growth and metastasis by competitively binding with miR-340-5p to regulate CLDN8/IL22 co-expression and activating ERK signaling pathway . Journal of experimental & clinical cancer research : CR 2020, 39 (1):5. Cai Y, Dong ZY, Wang JY: LncRNA NNT-AS1 is a major mediator of cisplatin chemoresistance in non-small cell lung cancer through MAPK/Slug pathway . European review for medical and pharmacological sciences 2018, 22 (15):4879-4887. He W, Zhang Y, Xia S: LncRNA NNT-AS1 promotes non-small cell lung cancer progression through regulating miR-22-3p/YAP1 axis . Thoracic cancer 2020, 11 (3):549-560. Wang Q, Yang L, Hu X, Jiang Y, Hu Y, Liu Z, Liu J, Wen T, Ma Y, An G et al : Upregulated NNT-AS1, a long noncoding RNA, contributes to proliferation and migration of colorectal cancer cells in vitro and in vivo . Oncotarget 2017, 8 (2):3441-3453. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3319964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":230726584,"identity":"33d74e28-7ae5-4dda-b0c4-f4f8ccb0841b","order_by":0,"name":"Lidan Lu","email":"","orcid":"","institution":"Changshu Hospital, Nanjing University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lidan","middleName":"","lastName":"Lu","suffix":""},{"id":230726585,"identity":"849ed44a-1cfa-4e76-ad4d-ecaaffa98f52","order_by":1,"name":"Neng Bao","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Neng","middleName":"","lastName":"Bao","suffix":""},{"id":230726586,"identity":"e578bffa-1489-428d-bb2d-59a45e0056da","order_by":2,"name":"Qingxue Wei","email":"","orcid":"","institution":"Changshu Hospital, Nanjing University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingxue","middleName":"","lastName":"Wei","suffix":""},{"id":230726587,"identity":"78988a7f-fee8-461e-83d8-c38932abc973","order_by":3,"name":"Ximei Cai","email":"","orcid":"","institution":"Changshu Hospital, Nanjing University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ximei","middleName":"","lastName":"Cai","suffix":""},{"id":230726588,"identity":"2b1c4ca4-d47f-4c1a-937f-6286354a92fd","order_by":4,"name":"Hongjian Ji","email":"","orcid":"","institution":"Wuxi Hospital, Nanjing University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongjian","middleName":"","lastName":"Ji","suffix":""},{"id":230726589,"identity":"240cd063-9cff-4662-8a08-c8abac6b0377","order_by":5,"name":"Haiyan Ni","email":"","orcid":"","institution":"Changshu Hospital, Nanjing University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Ni","suffix":""},{"id":230726590,"identity":"7383f334-fc05-4ce3-9783-da138bb45e8c","order_by":6,"name":"Peijuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFCCAyDCRo6fvfnAgQ8/iNeSZizZcyzx4Mwe4q06nLjhRo7xYQ42ItSaM54x/FzwK82Y4UDOh8MMPAzy/GIH8GuxbDhjLD2zz0aOseHshsMFFgyGM2cn4NdicODsBmnenjRjZsbeDYdn8DAkGNwmrGXzb96ew4ltzDwPDvOwEadlmzTPj8OJPWw8DMRpsWw4/82atyHNWIKHzQAYyBKE/WIucSz5Ns8fGzn7+48ff/jww0aeX5qQwyQOMDAwtsH5EviVg7XwNwDJP4QVjoJRMApGwQgGAEmWTfFcuYfrAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Peijuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-09-02 14:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3319964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3319964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42846063,"identity":"d87b12a6-ab27-49f1-80cd-c3fa91bef40b","added_by":"auto","created_at":"2023-09-08 18:09:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003em\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA-lncRNAs related to clinical prognosis\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Co-expression network of m\u003csup\u003e6\u003c/sup\u003eA-related genes and lncRNAs; \u003cstrong\u003eB\u003c/strong\u003e. Forest plot of univariate Cox regression analysis for prognostic m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs. Red represents high risk and blue represents low risk; \u003cstrong\u003eC\u003c/strong\u003e. Forest plot of univariate Cox regression analysis of m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs for clinical diagnosis. Red represents high risk and blue represents low risk.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/7cdf20aae75a2edd94763460.jpg"},{"id":42847729,"identity":"b931480b-a0a5-417b-a67e-b45606434bc5","added_by":"auto","created_at":"2023-09-08 18:17:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":730729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival curve and immune-associated analysis based on clustering.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. The CC cohort of TCGA was divided into two groups according to the consensus clustering matrix; \u003cstrong\u003eB\u003c/strong\u003e. Overall survival analysis showed that the survival of CC patients in cluster 2 was better than that of CC patients in cluster 1; \u003cstrong\u003eC\u003c/strong\u003e. Heatmap of the two clusters with regard to clinicopathological characteristics. \u003cstrong\u003eD\u003c/strong\u003e. Vioplot analysis of various immune cells infiltrating in different clusters; \u003cstrong\u003eE-F\u003c/strong\u003e. Gene set enrichment analysis. To clarify the various pathways in different samples, the most enriched pathways of each cluster were listed.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/2f681dab37017a3719bed6a1.jpg"},{"id":42846067,"identity":"82f647b3-1b8c-4406-acb8-38d4bddeb505","added_by":"auto","created_at":"2023-09-08 18:09:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":571216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003em\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA-lncRNAs-related prognostic model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eCross-validation for tuning the parameter selection in LASSO regression; \u003cstrong\u003eB\u003c/strong\u003e. LASSO regression for the m6A-lncRNAs-related prognostic model; \u003cstrong\u003eC–D\u003c/strong\u003e. Overall survival analysis of patients in low-risk/high-risk among the training and testing groups;\u003cstrong\u003e E–F\u003c/strong\u003e. ROC curve to predict the value in the training and testing cohorts; \u003cstrong\u003eG\u003c/strong\u003e. Distribution of risk score for CC patients in training and testing cohorts; \u003cstrong\u003eH\u003c/strong\u003e. Current survival status of CC patients in training and testing cohorts; \u003cstrong\u003eI\u003c/strong\u003e. Heatmap for prognosis-related m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs in\u003cstrong\u003e \u003c/strong\u003ethe\u003cstrong\u003e \u003c/strong\u003etraining and testing cohorts.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/0042b0d4e42d564c0fee3704.jpg"},{"id":42846068,"identity":"3ef3344d-ec26-4ad7-b991-897986fb0205","added_by":"auto","created_at":"2023-09-08 18:09:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognosis-related m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA-lncRNAs associated with clinical condition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eHeatmap for prognosis-related m6A-lncRNAs in the high-risk and low-risk groups\u003cstrong\u003e. \u003c/strong\u003eAge, tumor grade, and immune score were the risk factors;\u003cstrong\u003e B-D. \u003c/strong\u003eThe risk score for different age, clusters, and immune scores.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/0439ed1cb5e0ff277d339d1a.jpg"},{"id":42846066,"identity":"08eeb134-4cc8-42fb-a9f6-6039ecb2d1b3","added_by":"auto","created_at":"2023-09-08 18:09:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation for the prognosis-related m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA-lncRNAs\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eRelative mRNA expression levels of the 13 prognosis-related m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs in the normal and HeLa groups. Data are expressed as mean ± SD (n = 3); \u003csup\u003e#\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs. the normal group. \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 vs. the normal group. \u003cstrong\u003eB.\u003c/strong\u003e Detection of m6A enrichment by the spot hybridization test. Total content of m6A methylated LINC00662 and NNT-AS1 in the normal and HeLa groups. Data are expressed as mean ± SD (n = 3); ##\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01 vs. the normal group. \u003cstrong\u003eC\u003c/strong\u003e. Number of migrated cells in 24 h in the different groups. ns: compared with the HeLa group, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05; ##: compared with the siRNA-NC group, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. \u003cstrong\u003eD.\u003c/strong\u003e Number of invaded cells in 24 h in the different groups. ns: compared with the HeLa group, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05; ##: compared with the siRNA-NC group, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/22646afbdbd878f76d783edf.jpg"},{"id":42846065,"identity":"54c7b8eb-7eb9-48ac-a76f-a790ada000b1","added_by":"auto","created_at":"2023-09-08 18:09:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of methylated lncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B. \u003c/strong\u003eOnline prediction of m\u003csup\u003e6\u003c/sup\u003eA enrichment peak and specific peak sites of lncRNAs,\u003cstrong\u003e \u003c/strong\u003eresults showed that LINC00662 and NNT-AS1 could produce m6A methylation at multiple sites. \u003cstrong\u003eC-D\u003c/strong\u003e. The MeRIP-qPCR tested the LINC00662 and NNT-AS1, results indicated that the site mutated to G or T dramatically reduced the m6A enrichment in LINC00662 and NNT-AS1. Significance level was denoted by ## \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/38a8ab207180c445e835bd4a.jpg"},{"id":44057123,"identity":"77bda623-c5a7-49e1-85e5-9cd0d5ed0b19","added_by":"auto","created_at":"2023-10-04 05:37:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/a734725b-7926-4653-8687-9ae9f369dd6f.pdf"},{"id":42847730,"identity":"401021b6-f062-4aca-addb-23c1f09066da","added_by":"auto","created_at":"2023-09-08 18:17:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3330186,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3319964/v1/c13090c398cd66127e56c1f4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long Non-Coding RNAs Related to N6-Methyladenosine and Immune Cell Infiltration in Cervical Carcinoma: A Comprehensive Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical cancer (CC) is a prevalent gynecological cancer that poses a significant risk of morbidity and mortality among women. CC ranks fourth in the worldwide cancer incidence rate among all female malignant tumors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The primarycause of CC is human papillomavirus (HPV) infection[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the last few years, the overall mortality rate of CC has decreased because of the readily available vaccine for HPV[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the mechanisms by which HPV causes CC and the 5-year survival rate of patients with high-risk CC need further clarification[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is worthy to conduct a thorough investigation of the pathophysiology of CC to determine its clinical management.\u003c/p\u003e \u003cp\u003e \u003cem\u003eN\u003c/em\u003e \u003csup\u003e6\u003c/sup\u003e-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) is the most prevalent reversible mRNA modification found in mammals[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and which regulates mRNA metabolism[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], guides stem cell differentiation, and promotes embryonic development[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, the modification of the m\u003csup\u003e6\u003c/sup\u003eA segment is one of the most common internal regulation modes in mRNAs[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Most m\u003csup\u003e6\u003c/sup\u003eA modifications are categorized as writers, erasers, and readers. Writers methylate m\u003csup\u003e6\u003c/sup\u003eA through methyltransferases such as METTL3 and METTL14[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Erasers demethylate m\u003csup\u003e6\u003c/sup\u003eA through demethylases such as FTO and ALKBH5[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Readers are primarily responsible for recognizing m\u003csup\u003e6\u003c/sup\u003eA-binding proteins. The common binding proteins include YTHDF1 and IGF2BP1[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There is a significant correlation between the level of m6A and the activity of tumor associated markers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; This indicates that m6A has the potential to function as a novel therapeutic target for precise tumor treatment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) are segments of mRNA that do not code for proteins and have a length exceeding 200 nucleotides[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. lncRNAs are primarily localized in the cell cytoplasm and competitively bind to microRNAs to prevent their function. The main functions of lncRNAs are construction of the nuclear structure, regulation of cis or trans transcription, encoding of post-transcriptional modifications, etc.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, lncRNAs can play a role in tumor occurrence, migration, and invasion[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, elevated expression of the long non-coding RNA HOX transcript antisense RNA (HOTAIR) can worsen the prognosis of hepatocellular carcinoma[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and the lncRNA H19 is an important gene for malignant transformation and metastasis of gastric cancer[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Not surprisingly, lncRNAs also play a significant role in the pathogenesis of CC. For example, the degradation of the lncRNA FENDRR drives the proliferation of endometrial cancers[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The exact role of CC-related lncRNAs, however, remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we conducted an analysis of cervical cancer data from the Cancer Genome Atlas (TCGA), with a particular emphasis on the expression of long noncoding RNAs (lncRNAs) and their correlation with the prognosis of CC. The obtained findings were summarized to describe the most important factors for the prognosis and treatment of CC in clinical practice. In addition, we performed the spot hybridization and the RNA immunoprecipitation (MeRIP) followed by the quantitative real time PCR (qRT-PCR) to determine the levels of m6A-lncRNAs that were identified in HeLa cells.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eGenome data was retrieved from Genomic Data, and TCGA was used to obtain clinical cancer gene profiles and clinical data. As of November 2021, the TCGA has compiled the profiles of 556 CC and 23 normal tissue samples. The Perl software was used for data organization and gene ID conversion. As per the guidelines published by the National Cancer Institute, ethics committee approval was not required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Determination of m\u003csup\u003e6\u003c/sup\u003eA methylation related to lncRNAs\u003c/h2\u003e \u003cp\u003eBased on the literature, 23 m6A RNA methylation regulator was obtained[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The definition of a lncRNA is that any non-coding long chain RNA on the GENCODE Web site. To quantitate lncRNA expression and m\u003csup\u003e6\u003c/sup\u003eA modification, A co-expression system was constructed using the \u0026ldquo;iGraph\u0026rdquo; package in R to identify lncRNAs that play a role in m6A methylation. The \u0026ldquo;survival\u0026rdquo; package was employed to assess the prognostic value of m6A-lncRNAs through univariate Cox analysis and construct a forest plot. The relationship between lncRNA expression and CC development was visualized using a heatmap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Bioinformatics analysis\u003c/h2\u003e \u003cp\u003eWe utilized the \u0026ldquo;consensus cluster plus\u0026rdquo; software package and the \u0026ldquo;limma\u0026rdquo; software package to classify surgical cancer cases into two cluster subtypes (cluster Alg\u0026thinsp;=\u0026thinsp;km and cluster Num\u0026thinsp;=\u0026thinsp;2). The lncRNA cluster subtypes were analyzed using the \u0026ldquo;survminer\u0026rdquo; package. Differences in m6A-lncRNAs expression and their association with prognosis in each cluster subtype were assessed with the heatmap package. Gene set enrichment analysis (GSEA) was performed to investigate functional and pathway differences among various clinical cancer subtype. The simulation value was set as 1000, and the FDR value was set as 0.05. In order to estimate the penetration of different immune cells in tissue samples, we used PreprocessCore\u0026rdquo;, \u0026ldquo;limma\u0026rdquo; and \u0026ldquo;e1071\u0026rdquo; packages to detect infiltration of certain immune cells. Differences in the extent of immune cell infiltration among subgroup clusters were analyzed with the \u0026ldquo;limma\u0026rdquo; package, and the results were visualized using vioplot. Moreover, the \u0026ldquo;limma\u0026rdquo; package was used to evaluate the infiltration of various immune cell types in each subgroup cluster of CC, with the results presented as a boxplot. Next, the \u0026ldquo;limma\u0026rdquo; package was used for the tumor microenvironment (TME) and assess differences in purity among different subtype. Perl software was used to import data, and GSEA was performed to determine differences in the relevant functions and pathways among the different samples. Each sample was classified as \u0026ldquo;H\u0026rdquo; or \u0026ldquo;L\u0026rdquo; based on whether it belonged to the high-risk or low-risk category of lncRNAs associated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Establishment of the m\u003csup\u003e6\u003c/sup\u003eA-lncRNA-related prognostic model\u003c/h2\u003e \u003cp\u003eWe used LASSO regression to build the prognosis model. Then, the samples were classified into high risk and low risk according to the m6A-lncRNA risk score. The training (50%) and test (50%) groups were defined and the precision of the model in predicting CC survival was evaluated using receiver operating characteristic (ROC) curves. The hazard curve, survival status and risk for m6A-lncRNAs were assessed on the curve. Independently prognostic analysis was performed to determine if the model could function independently of other possible prognostic factors. The risk ratio was calculated by multivariate and univariate analysis. By conducting model validation for clinical groups, we tested whether our model was appropriate for application to various groups. We conducted risk analysis and clinical association analysis to identify different risk m6A-lncRNAs using the \u0026ldquo;heatmap\u0026rdquo; and \u0026ldquo;limma\u0026rdquo; packages. Furthermore, a boxplot of risk and clinical relevance was generated based on the clinical data. Finally, we examined the correlation between immune cells and the risk score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 m\u003csup\u003e6\u003c/sup\u003eA-lncRNA validation\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Cell culture\u003c/h2\u003e \u003cp\u003eCells of HeLa were cultured in DMEM medium (11995, Solarbio) containing 10% fetal bovine serum (10099141C, Gibco) and 1% penicillin-streptomycin (C0222, Beyotime). Mycoplasma contamination tests were routinely performed. The Lipofectamine 3000 reagent (L3000150, Invitrogen) was used to transfer the plasmids to the cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Quantitative real-time PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted with TRIzol reagent (Merck KGaA, Darmstadt, Germany). The QuantiTect Reverse Transcription kit was used to reverse transcription. PCR was performed on a StepOne Plus cycler with an AceQ qPCR SYBR Green Master Mix (Vazyme, Nanjing, China). The GAPDH was used to standardize gene expression, and the 2-\u003csup\u003e△△\u003c/sup\u003eCT was calculated. The PCR primer sequences used in this study are listed below:\u003c/p\u003e \u003cp\u003eInc00662 (F:CATCCCAAGCAAAGGTCCCT;R:TCAGGGTCTGCAGGACAAAC); HM13-IT1 (F: ACACCATCAGCCCCTTCATG;R:ATGTAGGCTGCAAAGCTGGT) ; LINC01936 (F:GAGGTCCAACCCCATTCCTG;R:CATGCCTGTAATCGCAGCAC) ; ELOA-AS1 (F:AGCCTCCCAAGCAGATAGGA;R: AAAGATCCCTGCCTTGGAGC) ; NNT-AS1 (F:AACCCAGCCATTCATGAGGG;R:CCGGGTTCAAGCCATTCTCT; SLC16A1 (F:CTGCAGTCCAACGACAGAGT;R:TCCATTTAGCGGTGCAGTGT) ; AP003096 (F:GCAGTGAAACCCATCTGATTAAGA;R:GGGGAGCTCAGCTAAGTAGG) ; AL645568 (F:CCCGTACCAATGTCAGTGCT;R: ATCAAGCAGCTTGGAACGGA) ; AC244517 (F:CACCTTGGTCACCAGGTAGC;R:CACCATCACTGTCACCGACA) ; AC011466 (F:GGAAGGAATGGAGCCTTGCT;R:GACAAGTGCACGGTTTGACC) ; AC010761 (F:CACTGGTTGGAGCTCCATGT;R: TCTTCTCTCCATCAGGCCGA) ; AC010260 (F:CCCTGGGTCCAAAATCGACA;R:AAGGAGCCAAATGGAGTCCG) ; AC002094 (F:AACCCAGAACTAGTTGCCCG;R:CTTCTCTACCATGCGTCGCT) ; GAPDH (F:GAAAGCCTGCCGGTGACTAA;R:FGCGCCCAATACGACCAAATC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Spot hybridization test\u003c/h2\u003e \u003cp\u003eTotal RNA was separated by TRIzol, and the RNA was denatured at 95 ℃ for 5 minutes. Next, 2 \u0026micro;L RNA sample was loaded onto a nylon membrane, UV crosslinked, and then incubated at 4℃ with m6A antibodies overnight. After incubation with secondary antibodies, the chemiluminescence system was used to detect signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Transwell assay\u003c/h2\u003e \u003cp\u003eA Transwell insert (0.8 \u0026micro;m, TCS003024, BIOFIL) was used to determine the invasion of HeLa cells. Following transfection for 24 h, 5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells were starved in 200 mL serum-free medium and then seeded onto plates. After transfection and incubation, cells were allowed to migrate to the bottom of the filter membrane. Subsequently, the cells were stained using a 0.1% crystal violet solution (G1063, Solarbio) and images were captured with an IX73 microscope (OLYMPUS). Finally, the absorbance was measured at 570 nm with a micro-plate reader (SYNERGY H11, BIOTEK, USA). The whole experiment was repeated at least three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.5 Prediction of the m\u003csup\u003e6\u003c/sup\u003eA modification site\u003c/h2\u003e \u003cp\u003eThe putative m6A modification sites of NEAT1 were predicted using SRAMP.\u003c/p\u003e \u003cp\u003e2.5.6 MeRIP‑qPCR. The Magna MeRIPTM m6A kit was employed to perform MeRIP for evaluating the m6A modification of genes. Briefly, 10 \u0026micro;L aliquot of anti-m\u003csup\u003e6\u003c/sup\u003eA antibodies was incubated at 4℃ overnight with the chromatin immunoprecipitation (ChIP) grade protein A/G. Next, 300 \u0026micro;L of total RNA fragments were incubated with antibodies in an IP buffer containing protease inhibitors and RNase inhibitors. The RNA modified with m6 A was eluted and extracted for quantitative RT-PCR analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe Statistical Product and Service Solutions (SPSS) version 20.0 (SPSS Inc. Armonk, New York, USA) were used for the production of graphs. The data were given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Each test was performed in a minimum of 3 separate trials or replicates. Multiple comparisons between groups were performed using appropriate methods. Student's t-test, one-way ANOVA, and the S-N-K method were used for statistical analysis. A p-value of \u0026lt;\u0026thinsp;0.05 was found to be statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Acquisition of m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs\u003c/h2\u003e \u003cp\u003eWe extracted m\u003csup\u003e6\u003c/sup\u003eA-related genes from the sorted transcriptome data and differentiated mRNAs from lncRNAs. Based on these data, the influence of the expression of m6A gene on the lncRNAs was highlighted by a network diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Univariate Cox regression analysis results were presented as forest plots (\u003cb\u003eSup. 1A\u003c/b\u003e). Significance levels of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used to identify those associated with m6A Prognosis. A heatmap and a boxplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) were then used to highlight differences in m6A-lncRNAs were found in normal and malignant specimens. Ultimately, we identified 87 m6A-related prognostic genes exhibiting distinct expression patterns between tumor tissues and normal samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Role of m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs\u003c/h2\u003e \u003cp\u003eWe searched prognosis-related lncRNAs from the clinical data and generated a forest map (\u003cb\u003eSup. 1B\u003c/b\u003e) and heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). lncRNAs with a \u003cem\u003ep\u003c/em\u003e-value of \u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 were considered to be related to CC prognosis. In the analysis of the expression level of lncRNAs, a level of K\u0026thinsp;=\u0026thinsp;2 was assumed as the lowest level of overlap. As a result, the lncRNAs were separated into two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However no significant difference in survival rates or expression of prognosis-related lncRNAs was observed between the two clusters. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The heatmap generated according to the clusters also showed the same result (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Immune cell infiltration and TME role\u003c/h2\u003e \u003cp\u003eA vioplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) shows the characteristics of immune modulation in the clusters. Except for M1 macrophages (\u003cb\u003eSup. 2A\u003c/b\u003e) that were preferentially found in cluster 1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), there was no difference in the other cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Based on the block diagram (\u003cb\u003eSup. 2B-D\u003c/b\u003e), the TME was analyzed among different sub-types of samples, and the purity of tumor cells was determined. The GSEA revealed variations in the associated functional pathways between the two clusters. The first six enriched functions of each cluster are given. Both the FDR Q value and FWER p value were significant. The pathways most related to the two clusters were \u0026ldquo;ADHESION_JUNCTION\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and \u0026ldquo;OXIDATIVE_PHOSPHORYLATION\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Establishment of an m\u003csup\u003e6\u003c/sup\u003eA-lncRNA-related prognostic model\u003c/h2\u003e \u003cp\u003eThe LASSO regression was used to construct the prognosis model, and samples were divided into the training group (50%) and test group (50%). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B show the coefficient and partial likelihood deviance of the prognostic signatures. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D show a comparison of the survival curves of the different risk groups. Low-risk patients were more likely to survive than high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To determine the accuracy in predicting patient mortality, the ROC curves obtained from the \u0026ldquo;timeroc\u0026rdquo; package was used (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). The area under the two curves value was \u0026gt;\u0026thinsp;0.5, which validated the accuracy of our model. A risk curve was then generated to determine the relationship between m6A-lncRNAs risk score and patient survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-I). An increased risk score was associated with higher patient mortality. We evaluated whether our model was an independent factor affecting the prognosis of patients by conducting an independent prognostic analysis (\u003cb\u003eSup. 3A-B\u003c/b\u003e). We found that the grade of tumor was an independent risk factor for prognosis of CC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Clinical group assessment was performed to determine whether the established model was clinically relevant, and 13 prognosis-related m6A-lncRNAs were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This evaluation confirmed that the model could be applied to different Clinical groups of patients by age, sex, and Grade (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of prognosis-related m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs\u003c/h2\u003e \u003cp\u003eHeLa cells were used to further confirm the abovementioned results. QRT-PCR was used to evaluate the level of mRNA expression for 13 prognosis-related m6A-lncRNAs. The levels of mRNA expression of the NNT-AS1, LINC006 Groups exhibited a significant difference in comparison with control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Spot hybridization was conducted to detect the total expression of m6A in LINC006 and NNT-AS1 in HeLa cells. The total expression of m6A in the expression levels of LINC006 and NNT-AS1 in HeLa cells were higher than those of normal cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). We also analyzed the LINC006- and Migration Regulation by NNT-AS1-mediatedAnd invasion of HeLa cells. The results revealed a significant delay in the migration and invasion of HeLa cells after LINC006 and NNT-AS1 knock-out (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D), thus indicating that these two properties of HeLa cells were regulated by the two abovementioned genes. SRAMP prediction analysis showed abundant m6A modification sites in LINC006 and NNT-AS1, thus indicating that both genes were highly likely to be modified by m6A methylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). MeRIP results also confirmed that because the levels of LINC006 and NNT-AS1 in the m6A group were significantly higher, LINC006 and NNT-AS1 were modified by m6A (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003em\u003csup\u003e6\u003c/sup\u003eA plays a key role in CC. Wang et al. analyzed the RNA transcriptome sequences from the TCGA database and found that m\u003csup\u003e6\u003c/sup\u003eA is closely related to malignant tumor formation; thus, they suggested that m\u003csup\u003e6\u003c/sup\u003eA could be used as a biomarker for diagnosing various diseases[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Zhang et al analyzed the amount of protein and gene expression in tissues and cells of patients with endometrial cancer and found that m6A plays a Central role in malignant transformation. Insulin-like growth factor 2 (IGF2BP1) regulates cancer formation by competing with m\u003csup\u003e6\u003c/sup\u003eA sites[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Ni et al. examined the pathological specimens of 177 patients with CC and found that the expression of METTL3, one of the m\u003csup\u003e6\u003c/sup\u003eA members, was significantly increased in patients with advanced CC; this finding confirmed that m\u003csup\u003e6\u003c/sup\u003eA is an independent diagnostic factor for CC [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Zou et al. analyzed the TCGA database and genotype tissue expression (GTEX) data and showed that m\u003csup\u003e6\u003c/sup\u003eA is an important diagnostic marker of uterine cancer, wherein METTL16 and IGF2BP1 are two important regulatory factors[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the last 2 years, some authors have proposed that m\u003csup\u003e6\u003c/sup\u003eA-related lncRNAs (m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs) could be used as a marker for tumor diagnosis and treatment. For example, Zhong et al. analyzed the genomic and clinical data from the TCGA database and revealed 22 m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs of high significance for diagnosing hepatocellular carcinoma. The transmembrane domain-containing member 3 (CMTM3) was also found to be overexpressed in tumor tissues[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A recently published study of Li et al. showed an association between the diagnosis and prognosis of bladder cancer and various m6A-lncRNAs. In addition, it has been demonstrated that certain genes (e.g., PD-L1) or pathways (JAK-STAT) play a significant role in the disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, CC-related transcriptome data were extracted from the TCGA database and sorted. Subsequently, the expression matrix of lncRNAs and m6A was created. A network diagram of m\u003csup\u003e6\u003c/sup\u003eA and lncRNAs was constructed using the results of univariate Cox regression analysis. We then combined the abovementioned m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs with the survival time of clinical patients to obtain 87 prognosis-related m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs. These results were represented by a forest plot and a heatmap. Previous studies have suggested that m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs are a diagnostic marker of tumors[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The results of our present study confirmed this assumption. We then clustered and typed these prognostic m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs (K\u0026thinsp;=\u0026thinsp;2) and used them as the basis for classifying the clinical samples. Significant differences were observed in the clinical samples, and a violin diagram of immune cells indicated differences in the distribution of M1 macrophages between the groups.\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis of CC by GSEA showed that the \u0026ldquo;ADHESION_JUNCTION\u0026rdquo; pathway was the most active in cluster 1, while the \u0026ldquo;OXIDATIVE_PHOSPHORYLATION\u0026rdquo; pathway was the most active in cluster 2. The adhesion junction is an important mode of physical connection between cells. It connects adjacent plasma membranes via cadherin receptors and plays an important role in tissue morphogenesis and remodeling. Li et al. reported tumor tissues from cancer patients and found a positive correlation between the high mobility group box 1 protein (HMGB1) and tumor malignancy. Further analysis showed that \u0026ldquo;ADHESION_JUNCTION\u0026rdquo; may be an important molecular pathway for HMGB1 to regulate diseases[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Oxidative phosphorylation is a biochemical reaction that occurs in mitochondria, and it phosphorylates ADP to synthesize ATP through electron transfer. Tango et al conducted a retrospective analysis of clinical swab samples from 92 CC patients and observed a higher abundance of oxidative phosphorylation in tumor cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Jonsson et al reported that oxidative phosphorylation promoted the replication of mitochondria in CC cells and HeLa cells to promote radiation resistance[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, an investigation of the role of oxidative phosphorylation pathways in cancer pathogenesis is worthy of future study.\u003c/p\u003e \u003cp\u003eIn this study, we employed LASSO Cox analysis to study the relationship between prognosis and m6A-lncRNAs, utilizing the risk score. Based on a median risk score for all m6A-lncRNAs, we separated the patients into high-risk and low-risk groups and conducted follow-up. The survival rate showed a significant difference between the two groups, with the high-risk group exhibiting a lower survival rate.\u003c/p\u003e \u003cp\u003eThe ROC value of \u0026gt;\u0026thinsp;0.65 further confirmed the accuracy of this prediction method. The clinical correlation heatmap and the boxplot showed differences in the age, tumor grade, immune signature, and clustering; this finding indicated that grouping according to the m\u003csup\u003e6\u003c/sup\u003eA-lncRNA risk score has clinical significance. These results also confirmed that m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs affect the prognosis of CC. The TME plays a key role in cancer development and treatment resistance[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the risk correlation heat map, 13 m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs with differential expression between the high-risk and low-risk groups, including AP003096.1, HM13-IT1, LINC00662, and NNT-AS1. The gene expression levels increased with higher risk scores.\u003c/p\u003e \u003cp\u003eTo confirm the involvement of the abovementioned 13 prognosis-related m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs in CC, we performed qRT-PCR using HeLa cells. The results revealed differential expression levels of LINC00662 and NNT-AS1. LINC00662 accelerates the proliferation and infiltration of tumor cells, and it has been shown that there is a close relationship between the progression of various cancers. For example, LINC00662 worsens the condition of gastric cancer by regulating the Hippo-YAP1 pathway[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Other studies showed that LINC00662 enables prostate cancer cells to easily enter the advanced stage by binding to miR-34 to increase its activity during prostate cancer lesion development[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Cheng et al reported that LINC00662 can accelerate the proliferation and invasion of tumor cells by activating ERK signaling pathway via CLDN8/IL22 complex[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In the present study, we performed the Transwell assay and confirmed that HeLa cells showed a significant decrease in their migration and infiltration ability following LINC00662 knockout. Dot blot hybridization and MeRIP-qPCR results demonstrated that LINC00662 was methylated in HeLa cells; this finding confirmed that LINC00622 regulated CC as an m\u003csup\u003e6\u003c/sup\u003eA-lncRNA. NNT-AS1 is a lncRNA located in the chromosome 5p12 region, and it plays a key regulatory role in the proliferation, metastasis, invasion, and cancer cell apoptosis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Recent studies by He et al. revealed that NNT-AS1 accelerates epithelial-mesenchymal transition and tumor cell proliferation, migration, and invasion by regulating YAP1 activity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Similarly, Wang et al. found that NNT-AS1 expression in colorectal cancer is associated with patient survival and may enhance colorectal cancer cell proliferation and metastasis by regulating the MAPK/ERK signaling pathway[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In our cellular experiments, we observed significantly higher NNT-AS1 mRNA expression in HeLa cells compared to normal cells. The Transwell assay also demonstrated a notable reduction in HeLa cell migration and invasion after NNT-AS1 deletion. Dot blot hybridization and MeRIP-qPCR further confirmed that NNT-AS1 underwent m\u003csup\u003e6\u003c/sup\u003eA methylation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study provides confirmation that m6A-lncRNAs are significantly associated with the prognosis of CC. The ADHESION_JUNCTION and OXIDATIVE_PHOSPHORYLATION pathways were identified as important factors in the development of CC. Among the thirteen prognosis-related m6A-lncRNAs, LINC006 and NNT-AS1 were further validated through in vitro experiments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve humans or animals, no ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Conflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. Authors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLidan Lu and Neng Bao performed data analysis and assisted in manuscript writing. Peijuan Wang designed the study. Qingxue Wei, Hongjian Ji, Haiyan Ni, and Ximei Cai assisted in manuscript writing. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e11. \u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was jointly supported by the Natural Science Foundation Project of Nanjing University of Traditional Chinese Medicine (No. XZR2021094); the Suzhou Science and Technology Development Project (No. SKY2022073); the Suzhou Integrated Traditional Chinese and Western Medicine Research Fund (No. SYSD2020224); the Suzhou Science and Technology Development Plan Guiding Project (No. SKJYD2021177), and the Changshu Science and Technology Development Project (No. CS202215).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e12. \u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank International Science Editing ( http://www.internationalscienceediting. com) for editing this manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen PA, Jhingran A, Oaknin A, Denny L: 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\u003cstrong\u003e22\u003c/strong\u003e(15):4879-4887.\u003c/li\u003e\n\u003cli\u003eHe W, Zhang Y, Xia S: \u003cstrong\u003eLncRNA NNT-AS1 promotes non-small cell lung cancer progression through regulating miR-22-3p/YAP1 axis\u003c/strong\u003e. \u003cem\u003eThoracic cancer \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e(3):549-560.\u003c/li\u003e\n\u003cli\u003eWang Q, Yang L, Hu X, Jiang Y, Hu Y, Liu Z, Liu J, Wen T, Ma Y, An G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eUpregulated NNT-AS1, a long noncoding RNA, contributes to proliferation and migration of colorectal cancer cells in vitro and in vivo\u003c/strong\u003e. \u003cem\u003eOncotarget \u003c/em\u003e2017, \u003cstrong\u003e8\u003c/strong\u003e(2):3441-3453.\u003c/li\u003e\n\u003c/ol\u003e 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cancer","lastPublishedDoi":"10.21203/rs.3.rs-3319964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3319964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: The aim of this study was to explore the effect of \u003cem\u003eN\u003c/em\u003e\u003csup\u003e6\u003c/sup\u003e-methyladenoxin (m6A-lncRNAs) on the prognosis of cervical cancer (CC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: The Cancer Genome Atlas dataset was used to comprehensively analyze the prognostic value of m6A-lncRNAs in cervical carcinoma (CC) and their relationship to tumor microenvironment. These data were then used to generate a prognostic model by LASSO regression. Finally, all prognosis-related m6A-lncRNAs were validated in HeLa cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 87 m6A-lncRNAs were found to be significantly associated with the overall survival of patients with CC. Two subtypes were isolated by clustering the 87 prognostic m6A-lncRNAs. Cluster 1 performed better in terms of patient survival than cluster 2. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of CC using gene set enrichment analysis revealed that the ADHESION_JUNCTION pathway was the most active in Cluster 1, while the OXIDATIVE_PHOSPHORYLATION pathway showed higher activity in Cluster 2. The clinical correlation heatmap and boxplot showed differences in age, tumor grade, immune characteristics, and clustering. Thirteen prognosis-related m\u003csup\u003e6\u003c/sup\u003eA-lncRNAs were identified by LASSO regression, and of these, LINC006 and NNT-AS1 were finally validated through \u003cem\u003ein vitro \u003c/em\u003estudies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Prognostic m\u003csup\u003e6\u003c/sup\u003eA-lncRNA markers could be an important mediator of the immune microenvironment of CC and a potential target for immunotherapy.\u003c/p\u003e","manuscriptTitle":"Long Non-Coding RNAs Related to N6-Methyladenosine and Immune Cell Infiltration in Cervical Carcinoma: A Comprehensive Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-08 18:08:57","doi":"10.21203/rs.3.rs-3319964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5122b245-e7d5-4fe8-b79d-4bcb198d0c6a","owner":[],"postedDate":"September 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-04T05:29:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-08 18:08:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3319964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3319964","identity":"rs-3319964","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}