Multifaceted investigation underlies diverse mechanisms contributing to the downregulation of Hedgehog pathway-associated genes INTU and IFT88 in lung adenocarcinoma and uterine corpus endometrial carcinoma

Investigations on the prognostic values of INTU in multiple cancer types

The Kaplan-Meier plotter was initially used to assess the prognostic significance of INTU expression in 21 types of cancer. We found that INTU expression was significantly associated with the overall survival (OS) probabilities in patients with nine different cancer types. In liver hepatocellular carcinoma (LIHC) and lung squamous cell carcinoma (LUSC), patients with an increased level of INTU had poor OS probabilities. However, in the remaining cancer types, including esophageal adenocarcinoma (EAC), esophageal squamous cell carcinoma (ESCC), kidney renal papillary cell carcinoma (KIRP), LUAD, pancreatic ductal adenocarcinoma (PDAC), sarcoma (SARC) and UCEC, reduced levels of INTU were correlated with decreased OS probabilities in patients (Figure 1).

Figure 1. Evaluation of the prognostic significance of INTU mRNA level in different cancer types. Pan-cancer survival analysis was carried out to determine the relationship between INTU mRNA level and OS probabilities in 21 different types of cancer. Decreased INTU expression was found associated with poor prognosis in EAC, ESCC, KIRP, LUAD, PDAC, SARC and UCEC patients, whilst high level of INTU correlated with poor prognosis in LIHC and LUSC patients.

Hh pathway-related genes were enriched in LUAD and UCEC tumor samples

We next sought to evaluate the expression of INTU in tumor samples from different cancer types. The expression of INTU in tumor samples from The Cancer Genome Atlas (TCGA) were compared with that from normal samples from TCGA and The Genotype-Tissue Expression project (GTEx). The results showed that the INTU transcription level was significantly downregulated in LUAD, LUSC and UCEC samples, whereas in esophageal carcinoma, KIRP, LIHC and SARC, no significant change of INTU expression between tumor and normal samples was detected (Figure 2A and Supplementary Figure 1A). No significant changes in the expression of housekeeping genes, including beta-actin (ACTB), beta-2-microglobulin (B2M) and ubiquitin C (UBC), were detected in LUAD and UCEC tumor samples when compared to their respective normal samples (Supplementary Figure 1B). In line with our prognostic analysis, the lower levels of INTU in LUAD and UCEC tumor samples (Figure 2A) coincided with poor OS probabilities in cancer patients (Figure 1). We thus decided to focus on LUAD and UCEC in our subsequent studies.

Figure 2. The Hh pathway-associated genes were enriched in LUAD and UCEC tumor samples. (A) The expression of INTU was significantly downregulated in LUAD and UCEC tumor samples. ^* denotes p < 0.05. (B) The GO and Reactome enrichment analysis on the top 100 genes that showed similar expression pattern as INTU from LUAD tumor samples. BP indicates biological process, and CC indicates cellular compartment. (C) The GO and Reactome enrichment analysis on the top 100 genes that showed similar expression pattern as INTU from UCEC tumor samples. (D) Construction of the PPI network using genes that showed similar expression pattern as INTU in LUAD tumor samples. Two enriched Reactome pathways “Anchoring of the basal body to the plasma membrane” and “Hedgehog ‘off’ state” were highlighted. (E) Construction of the PPI network using genes that showed similar expression pattern as INTU in UCEC tumor samples. Three enriched Reactome pathways “Anchoring of the basal body to the plasma membrane”, “Hedgehog ‘off’ state” and “Intraflagellar transport” were highlighted.

The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database was used to select the top 100 genes with similar expression patterns as INTU from LUAD and UCEC tumor samples (Supplementary Table 1). We then performed gene enrichment analysis to investigate whether certain enriched Gene Ontology (GO) terms and Reactome pathways could be identified from the LUAD and UCEC gene sets. We found that in LUAD, genes were enriched in cilium-associated biological processes, including cilium morphogenesis, assembly and movement. The most enriched subcellular localization pattern was found in association with ciliary compartments. Meanwhile, two Reactome pathways, “Anchoring of the basal body to the plasma membrane” and “Hedgehog ‘off’ state,” were highlighted (Figure 2B). Similar enriched biological processes and cellular compartment terms were identified from the UCEC gene set, where genes were found enriched in three Reactome pathways, including the “Hedgehog ‘off’ state”, “Intraflagellar transport” and “Anchoring of the basal body to the plasma membrane” pathways (Figure 2C).

Protein–protein interaction networks were subsequently constructed. In the LUAD gene set, the centrosomal genes centrosomal protein 83 (CEP83), centrosomal protein 162 (CEP162), centrosomal protein 290 (CEP290), centriolin (CNTRL), nephrocystin 4 (NPHP4) and MKS transition zone complex subunit 1 (MKS1) were associated with the “Anchoring of the basal body to the plasma membrane” pathway, while INTU, IFT88, MKS1 and WD repeat domain 90 (WDR90) were involved in the “Hedgehog ‘off’ state” pathway (Figure 2D). In the UCEC gene set, coiled-coil and C2 domain containing 2A (CC2D2A), CEP83 and nephrocystin 1 (NPHP1) were found in the “Anchoring of the basal body to the plasma membrane” pathway. The protein transport-associated genes dynein cytoplasmic 2 heavy chain 1 (DYNC2H1), IFT88, intraflagellar transport 140 (IFT140) and WD repeat domain 19 (WDR19) were involved in the “Intraflagellar transport” pathway, while DYNC2H1, INTU, IFT88, IFT140 and WDR19 were involved in the “Hedgehog ‘off’ state” pathway (Figure 2E).

IFT88 downregulation was associated with the poor prognosis of LUAD and UCEC patients

Similar to INTU, IFT88 was implicated in the “Hedgehog ‘off’ state” pathway in both the LUAD and UCEC gene sets (Figure 2D, 2E). In addition, INTU expression significantly correlated with the expression of IFT88 from LUAD and UCEC tumor samples (Figure 3A, 3B). Similar to INTU (Figure 1), we found that LUAD and UCEC patients with lowered IFT88 levels also showed decreased OS probabilities (Figure 3C, 3D). Taken together, these results highlight the significance of the downregulation of the Hh pathway-associated genes INTU and IFT88 in the prognosis of LUAD and UCEC patients. We therefore sought to delineate the underlying mechanisms of INTU and IFT88 downregulation in LUAD and UCEC tumor samples.

Figure 3. The expression of IFT88 correlated with INTU expression in LUAD and UCEC samples, and hypermethylation of INTU^CpG was detected in LUAD and UCEC samples. (A, B) The expression of IFT88 positively correlated with the expression of INTU in LUAD (A) and UCEC (B) tumor samples. (C, D) Decreased expression of IFT88 was found associated with reduced OS probabilities in LUAD (C) and UCEC (D) patients. (E) Two putative CpG islands were identified in INTU gene promoter. (F) Two CpG islands were predicted in IFT88 gene promoter. (G, H) Treatment of 5-azacytidine induced INTU expression in NCI-H1975 (G) and AN3 CA (H) cells. n = 3 biological replicates. Each n represents an independent preparation of cell RNA samples. Error bars represent S.E.M. Statistical analysis was performed using two-tailed unpaired Student’s t-test. ^* denotes p < 0.05. (I, J) Treatment of 5-azacytidine induced IFT88 expression in NCI-H1975 (I) and AN3 CA (J) cells. n = 3 biological replicates. Each n represents an independent preparation of cell RNA samples. Error bars represent S.E.M. Statistical analysis was performed using two-tailed unpaired Student’s t-test. ^* denotes p < 0.05. (K) Hypermethylation of the INTU^CpG was detected in LUAD and UCEC tumor samples. (L) No difference in IFT88^CpG methylation levels were detected in LUAD and UCEC samples when compared to their respective normal control samples.

The methylation level of INTU gene promoter CpG islands was upregulated in LUAD and UCEC tumor samples

DNA methylation is a typical epigenetic modification through which gene expression is modulated [13]. In gene promoter region, CpG islands are regions with densely-accumulated CG dinucleotides, and methylation of CpG islands leads to gene silencing [14]. In each of the INTU and IFT88 gene promoter regions, two putative CpG islands were identified (Figure 3E, 3F). To investigate the association between DNA methylation and INTU and IFT88 gene expression, the LUAD (NCI-H1975) and UCEC (AN3 CA) cell lines were treated with 5-azacytidine, a DNA methyltransferase inhibitor [15], the INTU and IFT88 levels were subsequently detected. We found that upon treatment of 5-azacytidine, both INTU and IFT88 levels were upregulated in NCI-H1975 and AN3 CA cells, suggesting a negative correlation between DNA methylation and INTU and IFT88 gene expression (Figure 3G–3J). The methylation levels of INTU^CpG and IFT88^CpG were further examined in LUAD and UCEC tumor samples. We found that the methylation level of INTU^CpG was significantly upregulated in both LUAD and UCEC tumor samples (Figure 3K). When compared with normal tissues, no significant change in IFT88^CpG methylation levels was detected in LUAD and UCEC tumor samples (Figure 3L). This suggests that hypermethylation of INTU^CpG potentially contributes to the reduction of INTU levels in LUAD and UCEC samples.

Involvement of the transcriptional factor TCF4 in the modulation of INTU and IFT88 levels

Transcription factors are a set of regulatory proteins that bind to gene promoter DNA sequences and modulate gene transcription [16]. A total of nine communal transcription factors, including core-binding factor subunit beta (CBFB), histone deacetylase 2 (HDAC2), transcription factor jun-D (JUND), serum response factor (SRF), small ubiquitin-like modifier 2 (SUMO2), TATA-box binding protein associated factor 1 (TAF1), TATA box binding protein (TBP), transcription factor 4 (TCF4) and yin yang 1 (YY1), were identified in the INTU and IFT88 gene promoters (Figure 4A). Next, we evaluated the expression of these transcription factors in LUAD and UCEC samples. Among these nine transcription factors, we found that only TCF4 (Figure 4B) expression was significantly downregulated in both LUAD and UCEC tumor samples (Figure 4C and Supplementary Figure 2). The TCF4 protein level was further found to be significantly reduced in both LUAD (Figure 4D) and UCEC (Figure 4E) samples. More importantly, the expression of TCF4 was positively correlated with the expression of INTU and IFT88 in LUAD and UCEC samples (Figure 4F, 4G). Similar to INTU (Figure 1) and IFT88 (Figure 3C, 3D), the reduced levels of TCF4 were associated with decreased OS probabilities in LUAD and UCEC patients (Figure 4H, 4I). Altogether, TCF4 was identified as a putative upstream regulator in controlling the expression of INTU and IFT88 in LUAD and UCEC tumor samples.

Figure 4. TCF4 was identified as a potential transcription factor that mediates INTU and IFT88 downregulation in LUAD and UCEC tumor samples. (A) Nine common transcription factors, including CBFB, HDAC2, JUND, SRF, SUMO2, TAF1, TBP, TCF4 and YY1 were predicted in INTU and IFT88 gene promoters. (B) Illustration of TCF4 binding consensus sequence and the putative TCF4 binding sites in INTU and IFT88 promoter sequence. (C) The TCF4 transcript level was downregulated in LUAD and UCEC tumor samples. ^* denotes p < 0.05. (D, E) The protein level of TCF4 was downregulated in LUAD (D) and UCEC (E) tumor samples. (F, G) The expression of TCF4 positively correlated with the expression of INTU and IFT88 in LUAD (F) and UCEC (G) tumor samples. (H, I) The LUAD (H) and UCEC (I) patients with lowered level of TCF4 showed reduced OS probabilities.

We further provided experimental evidence in support of our findings. Chromatin immunoprecipitation was performed to investigate the interaction between TCF4 protein and INTU and IFT88 gene promoters. We found that in both the NCI-H1975 and AN3 CA cells, the binding of TCF4 to INTU and IFT88 gene promoters was detected (Figure 5A, 5B). The transcriptional regulatory function of TCF4 on INTU and IFT88 gene expression was subsequently determined. When endogenous TCF4 was knocked down in NCI-H1975 and AN3 CA cells, the transcript levels of INTU and IFT88 were downregulated (Figure 5C, 5D). These data further support the positive correlation between TCF4 and INTU and IFT88 levels in LUAD and UCEC samples (Figure 4F, 4G).

Figure 5. TCF4 interacted with INTU and IFT88 promoters and mediated their gene expression. (A, B) The binding between TCF4 and INTU and IFT88 promoters was detected in NCI-H1975 (A) and AN3 CA (B) cells. n = 3 biological replicates. Each n represents an independent preparation of ChIP samples. Error bars represent S.E.M. Statistical analysis was performed using two-tailed unpaired Student’s t-test. ^* denotes p < 0.05 and ^** denotes p < 0.01. (C, D) Knockdown of TCF4 downregulated the transcript levels of INTU and IFT88 in NCI-H1975 (C) and AN3 CA (D) cells. n = 3 biological replicates. Each n represents an independent preparation of RNA and protein samples. Error bars represent S.E.M. Statistical analysis was performed using two-tailed unpaired Student’s t-test. ^*denotes p < 0.05, ^**denotes p < 0.01 and ^***denotes p < 0.001.

Identification of hsa-miR-212-3p as the upstream microRNA targeting Hh-related genes INTU and IFT88

In addition to gene silencing, post-transcriptional regulation of gene expression mediated by non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), has been reported [17, 18]. The DIANA-TarBase v8 database was used to select potential miRNAs that target INTU or IFT88 based on experimental evidence [19]. We identified 23 miRNAs that target the INTU transcript and four miRNAs that target the IFT88 transcript (Figure 6A). Interestingly, two miRNAs, hsa-miR-210-3p [20] and hsa-miR-212-3p [21], were shown to target both INTU and IFT88 (Figure 6A, 6B). We next evaluated the correlation between the miRNA level and INTU or IFT88 expression in LUAD and UCEC tumor samples. The expression of hsa-miR-212-3p, but not hsa-miR-210-3p, was found to be negatively correlated with the expression of INTU and IFT88 in LUAD and UCEC samples (Figure 6C–6F and Supplementary Figure 3). Moreover, we found that when hsa-miR-212 was overexpressed in NCI-H1975 and AN3 CA cells, downregulation of INTU and IFT88 protein levels were detected (Figure 6G, 6H). This further suggests the negative regulatory function of hsa-miR-212 on the expression of INTU and IFT88.

Figure 6. Identification of hsa-miR-212-3p as a communal miRNA against INTU and IFT88 in LUAD and UCEC samples. (A) Identification of hsa-miR-212-3p and hsa-miR-210-3p miRNAs that target both INTU and IFT88 transcripts. (B) Construction of the miRNA-target gene regulatory network. (C, D) The expression of hsa-miR-212-3p negatively correlated with the expression of INTU (C) and IFT88 (D) in LUAD samples. (E, F) The expression of hsa-miR-212-3p negatively correlated with the expression of INTU (E) and IFT88 (F) in UCEC samples. (G, H) Overexpression of hsa-miR-212 led to the downregulation of INTU and IFT88 protein levels in NCI-H1975 (G) and AN3 CA (H) cells. The MALAT1 levels were not affected. n = 3 biological replicates. Each n represents an independent preparation of protein or RNA samples. Error bars represent S.E.M. Statistical analysis was performed using two-tailed unpaired Student’s t-test. ns indicates no significant difference. ^* denotes p < 0.05.

Identification of MALAT1 as an upstream lncRNA

We next examined upstream lncRNAs using the DIANA-LncBase v3 database. A total of 63 lncRNAs were obtained, and their expression levels in LUAD and UCEC samples were evaluated (Figure 7A). The levels of four of the 63 lncRNAs, including HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1), KMT2E antisense RNA 1 (KMT2E-AS1), metastasis associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1), were significantly downregulated in LUAD and UCEC tumor samples when compared with their respective normal tissues (Figure 7B and Supplementary Figure 4A). The prognostic significance of these four lncRNAs were further evaluated. We found that MALAT1, but not HOTAIRM1 and NEAT1, showed prognostic significance in both LUAD and UCEC patients (Figure 7C, 7D and Supplementary Figure 4B). Similar to what was detected regarding INTU (Figure 1) and IFT88 (Figure 3C, 3D), the reduced level of MALAT1 contributed to poor OS probabilities in LUAD and UCEC patients (Figure 7C, 7D). Overexpression of hsa-miR-212 did not modulate MALAT1 levels in NCI-H1975 and AN3 CA cells (Figure 6G, 6H). In contrast, MALAT1 has been shown to target hsa-miR-212-3p in two independent studies (Figure 7E) [22, 23]. Moreover, the expression of MALAT1 was found to be positively associated with the mRNA levels of INTU and IFT88 in LUAD and UCEC tumor samples (Figure 7F, 7G). To further validate the regulatory function of MALAT1 on the expression of INTU and IFT88, MALAT1 was overexpressed in NCI-H1975 or AN3 CA cells (Figure 7H, 7I). We found that in MALAT1-overexpressing cells, the protein levels of INTU and IFT88 were simultaneously increased (Figure 7H, 7I). These results therefore confirm the role of MALAT1 in regulating the expression of INTU and IFT88 in lung and endometrial cancer cells.

Figure 7. Identification of MALAT1 as a communal lncRNA mediating INTU and IFT88 expression in LUAD and UCEC samples. (A) Identification of the lncRNAs against hsa-miR-212-3p and construction of the lncRNA-miRNA regulatory network. (B) The expression of MALAT1 was significantly downregulated in LUAD and UCEC tumor samples. (C, D) Decreased level of MALAT1 was found associated with poor prognosis in LUAD (C) and UCEC (D) patients. (E) The MALAT1 was identified as targeted lncRNA against hsa-miR-212-3p from two independent studies. (F, G) The expression of MALAT1 positively associated with the expression of INTU and IFT88 in LUAD (F) and UCEC (G) tumor samples. (H, I) Overexpression of MALAT1 caused the upregulation of INTU and IFT88 protein levels in NCI-H1975 (H) and AN3 CA (I) cells. n = 3 biological replicates. Each n represents an independent preparation of RNA and protein samples. Error bars represent S.E.M. Statistical analysis was performed using two-tailed unpaired Student’s t-test. ^* denotes p < 0.05.

Kaplan-Meier plotter analysis

The pan-cancer analysis function (https://kmplot.com/analysis/index.php?p=service&cancer=pancancer_rnaseq) from the Kaplan-Meier plotter database was used to evaluate the prognostic significance of INTU mRNA expression in 21 different types of cancer [65]. The prognostic significance of Hh pathway-associated genes IFT88, MKS1, WDR90, IFT140, DYNC2H1 and WDR19 was also evaluated using LUAD and UCEC patient survival data from Kaplan-Meier plotter database. Similar approach was used to determine the prognostic value of TCF4 and lncRNAs. The OS probabilities of cancer patients were assessed using the Kaplan-Meier survival plots, and logrank p < 0.05 indicates that the association between gene/lncRNA expression and patient survival probability is statistically significant.

GEPIA2 database analysis

GEPIA2 (http://gepia2.cancer-pku.cn/#index) is an online database that provides gene expression profiling and interactive analyses in primary tumor and normal tissue samples on the basis of data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects [66]. The expression of INTU, housekeeping genes, different transcription factors and different lncRNAs were determined in LUAD and UCEC primary tumor samples and their respective normal tissues. The p < 0.05 was considered as statistically significant. The top 100 genes with similar expression pattern as INTU in LUAD or UCEC tumor samples were also selected using the “Similar Gene Detection” function from GEPIA2. The detailed gene lists are included in Supplementary Table 1.

Gene ontology and Reactome pathway analysis

The INTU and top 100 genes with similar expression pattern obtained from GEPIA2 database were input to Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 (https://david.ncifcrf.gov/home.jsp) to analyze their enriched Gene Ontology (GO) terms and Reactome pathways [67]. The biological processes (BP) and cellular components (CC) were included in the GO enrichment analysis. The false discovery rate (FDR) q-value < 0.05 was used as selection criteria for significantly enriched GO terms and Reactome pathways.

Protein-protein interaction (PPI) network analysis

The construction of PPI network was performed using STRING v11.5 database (https://string-db.org/) [68]. The genes with similar expression pattern obtained from GEPIA2 database were input to STRING database, and the PPI network was constructed based on the sources of “Co-expression”, “Databases”, “Experiments”, “Gene Fusion”, “Neighborhood” and “Textmining” with minimum required interaction score of medium confidence. The Cytoscape v3.8.0 was used to visualize the constructed PPI network [69].

Prediction of CpG islands and transcription factor binding sites within gene promoter sequences

INTU and IFT88 promoter sequences were withdrawn from GenBank under the accession numbers NC_000004.12 and NC_000013.11, respectively. The CpG islands were predicted using MethPrimer 2.0 (http://www.urogene.org/cgi-bin/methprimer2/MethPrimer.cgi) software [70]. The transcription factor binding sites were predicted using Animal Transcription Factor Database 3.0 (AnimalTFDB3.0; http://bioinfo.life.hust.edu.cn/AnimalTFDB/#!/) [71]. The “q-value < 0.05” and “Score > 20” were used as filtering parameters to select potential transcription factors for INTU and IFT88. The TCF4 binding site was further validated using JASPAR (https://jaspar.genereg.net/) database [72]. The relative profile score threshold equals to 90%, and “Score > 12.5” and “Relative score > 0.92” were used as filtering parameters. The detailed INTU and IFT88 promoter sequences are listed in Supplementary Table 2, the putative CpG islands are shown in blue and the TCF4 binding sites are highlighted.

MethHC2.0 database analysis

MethHC2.0 is a web-based resource that provides analysis on the methylation levels of gene regions, including CpG islands, from different types of cancer [73]. The methylation levels of INTU and IFT88 CpG islands were scrutinized using methylome data from LUAD and UCEC tumor samples and their respective normal samples.

UALCAN database analysis

UALCAN (http://ualcan.path.uab.edu/analysis-prot.html) is an interactive online resource that enables the analysis of protein expression based on the Clinical Proteomic Tumor Analysis Consortium Confirmatory/Discovery datasets [74]. In this study, the TCF4 protein expression from LUAD and UCEC tumor samples and their respective normal tissues was analyzed.

Candidate miRNA and lncRNA prediction

The list of miRNAs that target INTU and IFT88 was obtained using DIANA-Tarbase v8 database (https://dianalab.e-ce.uth.gr/html/diana/web/index.php?r=tarbasev8%2Findex) [19]. The “Species = Homo Sapiens” and “Validated as Positive” were used as filtering parameters. The miRNA-target genes regulatory network was constructed using Cytoscape v3.8.0.

The list of lncRNAs that target different miRNAs was obtained using DIANA-LncBase v3 database (https://diana.e-ce.uth.gr/lncbasev3) [75]. The “Species = Homo Sapiens”, “miRNA Conf. Level = High” and “Validated as Positive” were used as filtering parameters. The lncRNA-miRNA regulatory network was constructed using Cytoscape v3.8.0.

ENCORI database analysis

ENCORI (http://starbase.sysu.edu.cn/) is an online database that determines the correlation between miRNA level and target gene expression [76]. The expression correlation between hsa-miR-210-3p/hsa-miR-212-3p and different enriched Hh-related genes was analyzed using “miRNA-Target CoExpression” module from the “Pan-Cancer” function. The p-value < 0.05 was considered as statistically significant.

TIMER2.0 database analysis

The TIMER2.0 (http://timer.cistrome.org/) is an online web server that enables the detection of gene expression correlation [77]. The correlation among different Hh-related genes, and correlation between Hh-related genes and MALAT1 in LUAD and UCEC tumor samples were evaluated using the “Gene_Correlation” module. No adjustment was made, and p < 0.05 was considered as statistically significant.

cBioPortal database analysis

cBioPortal v3.7.3 is a comprehensive web resource that enables the visualization and analysis of cancer genomic mutation data (https://www.cbioportal.org/) [78, 79]. The missense and nonsense mutation profiles in INTU, IFT88 and TCF4 genes were obtained from Lung Adenocarcinoma (TCGA, PanCancer Atlas, 566 samples) and Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas, 529 samples) datasets.

D-lnc database

The D-lnc database (http://www.jianglab.cn/D-lnc/index.jsp) is a comprehensive platform that summarizes the lncRNA-targeting drugs based on the experimental evidence and computational predictions [80]. The “Species = Homo Sapiens” and “lncRNA = MALAT1” were used to select potential small molecules that target MALAT1.

Mammalian cell culture

The human endometrial adenocarcinoma cell line AN3 CA was a kind gift from Prof. Chi Chiu Wang (Department of Obstetrics and Gynecology, The Chinese University of Hong Kong, China). The human lung adenocarcinoma cell line NCI-H1975 (CRL-5908™) was obtained from American Type Culture Collection. Both cell lines were cultured using Dulbecco’s Modified Eagle’s Medium (11995065, Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (F7524, Sigma-Aldrich) and 1% penicillin-streptomycin solution (15140122, Thermo Fisher Scientific). The cells were maintained in a 37°C humidified cell culture incubator supplemented with 5% CO₂.

Plasmid, microRNA and siRNA transfection

The pcDNA-MALAT1 plasmid was a kind gift from Prof. Huating Wang (Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, China). Cells were transfected with 0.5 μg pcDNA-MALAT1 plasmid with 0.5 μl lipofectamine 2000 (11668019, Thermo Fisher Scientific). The RNA or protein samples were harvested 24 h post transfection. The hsa-miR-NC (4464058) and hsa-miR-212 (4464066) were synthesized by Thermo Fisher Scientific. Cells were transfected with 20 pmol microRNAs with 2 μl lipoRNAiMAX (13778150, Thermo Fisher Scientific). Cell culture medium and transfection mixtures were refreshed every 24 h, and protein samples were harvested 72 h post transfection. The Control-siRNA, 5′-UUCUCCGAACGUGUCACGUTT-3′ and TCF4-siRNA, 5′-CUAUCAGUAUUCUAGCAAUAATT-3′ were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Cells were transfected with 20 pmol siRNAs with 2 μl lipoRNAiMAX. Cell culture medium and transfection mixtures were refreshed every 24 h, and RNA or protein samples were harvested 72 h post transfection.

Drug treatment

The NCI-H1975 and AN3 CA cells were treated with 2 μM 5-azacytidine (A2385, Sigma-Aldrich). The treatment lasted 72 h, with medium and drug refreshed every 24 h.

Chromatin immunoprecipitation

The chromatin immunoprecipitation (ChIP) assay was performed using Pierce™ Magnetic ChIP Kit (26157, Thermo Fisher Scientific). The experimental procedures were carried out following the manufacturer’s instructions. Two micrograms of anti-TCF4 antibody (ab217668, abcam) were used for the immunoprecipitation, while the same amount of normal rabbit IgG (I-1000, Vector Laboratories, lnc.) was used as negative control. Twenty nanograms of recovered genomic DNAs from each of input, normal rabbit IgG and TCF4 immunoprecipitated samples were used in the following real-time PCR to analyze the levels of INTU and IFT88 promoter fragments. The primers used were INTU promoter-F, 5′-CAGCCTGGACTTCGCGAG-3′; INTU promoter-R, 5′-TGAAGGCGGTGGTGTCAG-3′; IFT88 promoter-F, 5′-AAAACGGACACCTTAAGCGC-3′ and IFT88 promoter-R, 5′-CTTGTGAACCTTGGAAGCCC-3′.

RNA extraction, reverse transcription and real-time PCR

The total RNA was isolated from cultured cells using TRIzol™ reagent (15596018, Thermo Fisher Scientific). The reverse transcription was performed using ImProm-II™ Reverse Transcription System (A3803, Promega) and random hexamer (N8080127, Thermo Fisher Scientific) according to the manufacturers’ instructions. Quantitative real-time PCR was performed using SYBR™ Green PCR Master Mix (4309155, Thermo Fisher Scientific) on the Bio-Rad CFX96 system. Relative gene expression was determined via normalizing against β-ACTIN using the 2^−ΔΔCT method. Primers used in this study were INTU-F, 5′-CGCATAGATGAACGGCTAGC-3′; INTU-R, 5′-AGCGTTCTTCTGCATGTTGG-3′; IFT88-F, 5′-CTGCAACCAATCTCTCAGCC-3′; IFT88-R, 5′-GCGGCCTTCTCATAATCACC-3′; MALAT1-F, 5′-ATGCGAGTTGTTCTCCGTCT-3′; MALAT1-R, 5′-TATCTGCGGTTTCCTCAAGC-3′; β-ACTIN-F, 5′-ATGTGCAAGGCCGGTTTCGC-3′ and β-ACTIN-R, 5′-CGACACGCAGCTCATTGTAG-3′.

Immunoblotting

Protein samples were harvested from cells using the SDS sample buffer (100 mM Tris-HCl, pH 6.8, 2% SDS, 40% glycerol, 5% β-mercaptoethanol, and 0.1% bromophenol blue). Samples were heated at 99°C for 10 min prior to being subjected to the immunoblot analysis. The protein samples were then transferred to a PVDF membrane (IPVH00010, pore size 0.45 μm, Merck Millipore). The membrane was blocked using 5% nonfat milk at 25°C for 1 h, followed by the incubation of primary antibodies at 4°C for 16 h. Primary antibodies used were anti-TCF4 (ab217668, 1:1,000, abcam), anti-INTU (ab229243, 1:1,000, abcam), anti-IFT88 (13967-1-AP, 1:1,000, Proteintech) and anti-β-TUBULIN (ab6046, 1:2,000, abcam). The membrane was washed three times with 1× TBST each for 10 min, before being subjected to the incubation of secondary antibodies at 25°C for 1 h. Secondary antibodies used were HRP-conjugated goat anti-rabbit IgG (H + L) (11-035-045, 1:5,000) and HRP-conjugated goat anti-mouse IgG (H + L) (115-035-062, 1:10,000) from Jackson ImmunoResearch. The membrane was washed three times with 1× TBST each for 10 min, prior to the detection of chemiluminescent signal. The signal was developed using Immobilon Forte Western HRP substrate (WBLUF0100, Merck Millipore), and the images were captured and processed using ChemiDoc™ Touch Imaging System (170-8370, Bio-Rad).

Statistical analysis

The two-tailed, unpaired Student’s t test was used for the comparison between two experimental groups. ^*, ^** and ^*** represent p < 0.05, p < 0.01 and p < 0.001, respectively, which are considered statistically significant. ns indicates no significant difference. GraphPad Prism version 9.0.0 was used for statistical analysis.