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Abstract

Introduction

Esophageal cancer presents significant challenges due to limited treatment options and poor prognosis, particularly in advanced stages. Dysregulated long non-coding RNAs (lncRNAs) are implicated in cancer progression and treatment resistance. This study investigated the roles of dysregulated lncRNA NONHSAT227443.1, identified through lncRNA-seq, and its downstream target gene MRTFB in esophageal squamous cell carcinoma (ESCC).

Methods

Dysregulated lncRNAs were identified through lncRNA-seq in esophageal cancer tissues with varying chemotherapy response. The regulatory interaction of overexpressed NONHSAT227443.1 was assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Functional assays, including cell viability, cell proliferation, and flow cytometry analyses, were performed to comprehensively investigate the influence of NONHSAT227443.1 and its downstream molecules on ESCC.

Results

NONHSAT227443.1 was significantly overexpressed in paclitaxel plus platinum chemotherapy non-responders and esophageal cancer cell lines. Chemotherapy exposure led to diminished NONHSAT227443.1 expression. NONHSAT227443.1 negatively regulated MRTFB expression, and their combined dysregulation correlated with increased cancer activity, proliferation, and suppressed apoptosis. Diminished MRTFB expression was associated with PI3K/AKT pathway activation.

Conclusion

Our study provides insights into NONHSAT227443.1 and MRTFB roles in esophageal cancer, contributing to aggressive traits and treatment resistance. NONHSAT227443.1 and MRTFB may serve as potential therapeutic targets to enhance the response to paclitaxel plus platinum chemotherapy in non-responsive cases.

Keywords

NONHSAT227443.1 esophageal cancer chemotherapy resistance MRTFB

Introduction

Esophageal cancer is a formidable malignancy characterized by its aggressive nature and alarming rates of both incidence and mortality.¹ According to estimates from GLOBOCAN 2022, the year 2020 witnessed 544,000 deaths globally attributed to esophageal cancer, representing 5.5% of total malignant tumor-related fatalities.² Esophageal cancer is a complex and heterogeneous disease, posing significant challenges in early detection, prognosis, and effective treatment options.^2,3 Despite some progress in treatment strategies involving chemotherapy, targeted therapy, and immunotherapy, therapeutic resistance remains a major obstacle.^4–7

Long non-coding RNAs (lncRNAs) have recently gained significant attention as a fascinating class of non-coding RNA molecules that play crucial roles in gene regulation. Unlike protein-coding genes, lncRNAs do not encode proteins but participate in various cellular processes, including chromatin remodeling, transcriptional regulation, and post-transcriptional modifications.^8–10 The irregular expression of lncRNAs has been entwined with various facets of the journey of esophageal cancer advancement, spanning tumorigenesis, metastatic progression, and the emergence of tenacious resistance against treatments. For instance, the downregulation of the lncRNA RP11–766N7.4 has been demonstrated to be closely correlated with the initiation and metastasis of esophageal squamous cell carcinoma (ESCC).¹¹ Likewise, the lncRNA MALAT1 has been implicated in possessing the ability to impede apoptosis induced by radiation, consequently bolstering cellular resistance against radiotherapy.¹² Furthermore, studies have illuminated the association of lncRNA CCAT1 with the drug sensitivity profile of esophageal cancer, revealing an intricate interplay between non-coding RNA molecules and therapeutic outcomes.¹³In addition，lncRNAs have also been confirmed to play an important role in the targeted therapy of malignant tumors such as pancreatic cancer, prostate cancer and ovarian cancer.

In this study, we focus on a specific dysregulated lncRNA, NONHSAT227443.1, in patients exhibiting diverse responses to paclitaxel plus platinum chemotherapy. We aim to investigate the functional significance of this lncRNA in esophageal cancer to acquire invaluable insights into the underlying molecular mechanisms，which holds promise to significantly elevate patient responses to chemotherapy, ultimately redefining both their immediate outcomes and long-term prognoses.

Methods and Materials

Patients and Specimens

Our study adhered to the principles outlined in the World Medical Association's Declaration of Helsinki, ensuring the informed consent, privacy, and data security of all participants.

All patients underwent thorough evaluation according to the stringent criteria established by the AJCC (American Joint Committee on Cancer) and UICC (Union for International Cancer Control). The eighth edition of UICC's TNM staging for esophageal cancer was meticulously applied. Specifically: 1. R0 resection of esophageal cancer was skillfully performed. 2. Only patients with the thoracic ESCC subtype were included. 3. An expertly administered preoperative neoadjuvant chemotherapy regimen consisting of paclitaxel + platinum for 2 cycles was utilized. 4. Patients with abnormal heart, lung, liver, or kidney function, as well as those with unremarkable blood routine test results rendering them unsuitable for neoadjuvant chemotherapy and surgical intervention, were excluded from the study. In total, forty specimens of esophageal cancer and corresponding adjacent normal tissues were collected from eight patients who had undergone radical resection. Immediately after collection, all tissue samples were gently washed with physiological saline at 4°C, rapidly snap-frozen in liquid nitrogen, and stored at −80°C for subsequent processing. Clinical data from the patients were meticulously collected and organized. Subsequently, 40 patients were stratified into discovery and replication cohorts: In the discovery cohort, four patients (aged 53.25 [range 44-66] years) with a CAP (College of American Pathologists) score of 0–1 were designated as the experimental group (Responders), while another four patients (aged 53.30 [range 43-62] years) with a CAP score of 3, matched to the experimental group in terms of clinical staging (cTNM), were categorized into the control group (Non-responders). In the replication cohort, specimens of esophageal cancer and corresponding adjacent normal tissues were collected from 36 patients for the validation of expression of identified key dysregulated lncRNAs.

lncRNA-seq

Total RNA was extracted from samples with TriQuick reagent (R1100, Solarbio) according to the manufacturer's protocol. RNA concentration and quality were determined by the Qubit^®3.0 Fluorometer (Life Technologies, USA) and the Nanodrop One spectrophotometer (Thermo Fisher Scientific Inc., USA). RNA sequencing libraries were generated from the RNA samples using the ABclonal Whole RNA-seq Lib Prep kit for illumina (ABclonal, China), and subsequent sequencing was conducted on Illumina Novaseq machines.

lncRNA-seq Data Analysis

Fastp was employed to trim and filter the raw sequencing reads. The high-quality reads were aligned to human reference genome using HISAT2. Stringtie software was used to compare the fragments in each gene segment, and then TMM (trimmed mean of M values) algorithm was used for normalization. After obtaining the FPKM expression values of genes, the edgeR software package was used to analyze the differences in gene expression between samples. In this project, the screening criteria for differential genes were P value < 0.05 and |log2fold change| >1 under the condition of FPKM>1 in any group in the comparison group.

qRT-PCR

Total RNA was extracted from samples with TriQuick reagent (R1100, Solarbio) according to the manufacturer's instructions. RNA was reverse transcribed into cDNA using a SureScript^TMFirst-Strand cDNA Synthesis Kit (QP056T, GeneCopeia). qRT–PCR reactions were performed on the iQ5 Real-Time PCR instrument using SYBR Green qPCR Master Mix (G3320-05, Servicebio) for detecting each target gene. GAPDH and the 2 ^−ΔΔCt method were respectively used to normalize the data and calculate the relative expression level. The primers used in the qPCR analysis were provided in Table 1.

Table 1.

Primers Used in qPCR.

Gene	Primer (5′-3′)
β-actin forward	CATGTACGTTGCTATCCAGGC
β-actin reverse	CTCCTTAATGTCACGCACGAT
NONHSAT100798.2 forward	TCGTGGATGGGTTTGGCTAC
NONHSAT100798.2 reverse	CCAACATGTAGGAGCCTGGG
NONHSAT054462.2 forward	TGGTGCCTTTTCTTGGGGTT
NONHSAT054462.2 reverse	CTGGTTAGGTGCCCTTCTGG
NONHSAT149530.1 forward	CTAGTTTGGGACTTCTGGGGG
NONHSAT149530.1 reverse	AGGGACAAAGGAAAGCGAGG
NONHSAT123314.2 forward	GTCCTGTCTGGGCACTAACC
NONHSAT123314.2 reverse	GATTCACTGCCAGGCCTTCT
NONHSAT227443.1 forward	TCCCGTGGAGCATTAGACCA
NONHSAT227443.1 reverse	TCTCCCTTCCCACAAATGAGT
MRTFB forward	TTATAGGCGTTGGGAAGGAGG
MRTFB reverse	CCGGAGACAAAGCGTCACT

MRTFB: myocardin-related transcription factor-B.

Cell Culture, Cell Treatment and Plasmid Constructs

The human Esophageal Carcinoma cell lines KYSE-150 (Catalog number: CL-0638, Pricella, Wuhan, China), TE-1 (Catalog number: CL-0231, Pricella, Wuhan, China) and TE-12 (Catalog number: CL-0751, Pricella, Wuhan, China), and human esophageal normal cells (Het-1A) (Catalog number: tings-1618306, Delf, Hefei, China) were maintained in RPMI Medium 1640 (C11875500BT, Gibco) supplemented with 10% fetal bovine serum (FBS) (10270-106, Gibco) at 37 °C in a humidified atmosphere containing 5% CO_2% and 95% air. Additionally, STRPCR profiling for KYSE-150, TE-1, TE-12 was conducted at Pricella, while STRPCR profiling for Het-1A was conducted at Delf. For maintenance, 800,000 cells were seeded into 10ml of complete media every 3–4 d in 10-cm plates. Inhibition treatment involved the LY294002, a phosphoinositide 3-kinase (PI3K) inhibitor (HY-10108, MedChemExpress, 20 μmol/L), over a 24-h period. Following this treatment, cells were subsequently harvested for protein extraction.

The backbones pcDNA3.1 vector were purchased from TsingkeBiotechnologyCo., Ltd (Beijing, China). Plasmid constructs were generated via Gibson Assembly^® Master Mix—Assembly (E2611, NEB) according to Gibson Assembly^® Protocol (E5510, NEB).

Chemotherapeutic Agents Exposure

Drug concentrations utilized in subsequent experiments were tailored to the specific sensitivities of the cell lines, as determined by their respective median effective doses (IC50) values. Carboplatin and paclitaxel were used as chemotherapeutic agents. Experiments involved treating cells (TE-1, TE-12, KYSE-150) with varying drug concentrations and assessing their responses. Data were collected and analyzed to determine effective drug concentrations (EC50) and IC50 values. To ensure the reliability of our findings, multiple experimental runs were conducted. For the carboplatin with paclitaxel treatment, the following drug concentrations were administered: KYSE150 cells were treated with 350 μM carboplatin and 35 μM paclitaxel, TE-12 cells were exposed to 250 μM carboplatin and 25 μM paclitaxel, and TE-1 cells received 200 μM carboplatin and 20 μM paclitaxel.

Cell Proliferation and Viability Detection

Cell proliferation in various experimental groups was accessed using plate cloning. A total of 300 cells were seeded in 12-well plates with 2 mL of complete medium per well. Cultured at 37 °C with 5% CO2 for two weeks, clones with more than 50 cells were subjected to PBS washing, fixed with 4% paraformaldehyde at 4 °C for 30 min, stained with 0.1% crystal violet, and photographed after drying. Cell viability was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Seed 3000 cells per well in a 96-well microplate, followed by the addition of 10 μL of 10 mg/mL MTT solution (0793, Ameresco). Subsequently, the medium was removed, and formazan crystals were dissolved in 100 µL of DMSO. Absorbance was measured at 490 nm using a spectrophotometer (FLX800, Bio-tek).

Apoptosis was Detected by Flow Cytometry

We routinely harvested 5 × 10^5 cells and gently resuspended them in 500 µL of 1× Binding Buffer. The Annexin V-FITC/PI apoptosis kit was employed following the manufacturer's guidelines, and measurements were conducted using a FACSCalibur flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA). Data analysis was carried out using BD FACSDiva software (BD Biosciences, Franklin Lakes, NJ, USA).

Target Gene Prediction and Enrichment Analysis

Based on the principle of sequence complementarity pairing, mRNA complementary with lncRNA was obtained by blast comparison, and the thermodynamic parameter values of lncRNA after complementarity pairing with mRNA were calculated using RNAplex software, and the results above the software threshold were selected as the target genes of lncRNA. To gain insights into the function of identified target geness, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using the R package -“clusterProfiler”.

Luciferase Assay

To investigate the effect of NONHSAT227443.1 on MRTFB (ref to NCBI gene:57496) gene expression, we performed luciferase assay. We created wild-type (wt) and mutant (mut) luciferase reporter vectors. In the wt group, MRTFB promoter (Start codon upstream 2K sequence) were inserted into pGL3-basic vectors (Promega, Madison). In the mut group, MRTFB promoter were mutated within the pGL3-basic vectors. For the Luciferase assay, 293T cells (Hunan Fenghui Biotechnology Co., Ltd, Hunan) were transfected with the following constructs: wt-MRTFB/NC, wt-MRTFB/NHSAT227443.1, mut-MRTFB/NC, and mut-MRTFB/NHSAT227443.1. Following transfection, cells were lysed, and both Firefly luciferase (representing the target gene) and Renilla luciferase (serving as an internal control) activities were measured using a luminometer. The relative luciferase activity was calculated as the ratio of Firefly to Renilla luciferase activity, providing insights into the regulatory effects of the constructs on the target genes. This assay was performed following the guidelines of the Dual Luciferase Reporter Gene Assay Kit (Yesheng Biotechnology (Shanghai) Co., Ltd, Shanghai).

Western Blot

Tissues or cells were harvested and homogenized in RIPA buffer (R002, Solarbio). The protein was quantified using the BSA Bradford protein concentration assay kit (AR0145, BOSTER). Proteins were separated by SDS–PAGE (5% stacking gel and 10% separated gel) (AR0138, BOSTER) and transferred to PVDF membrane (IPVH0010, Millipore). The membranes were then incubated overnight at 4 °C with the primary antibody against MRTFB (14613, Cell Signaling), p-AKT (ab81283, abcam), p-PI3K (ab182651,abcam), or β-actin (ab8227, abcam). After washing with TBST buffer, the membranes were incubated with the secondary antibody (ab7090, abcam). The protein bands were visualized using Western Lightning^TM Chemiluminescence Reagent (NEL10300EA, PerkinElmer) and quantified using Epson Perfection V39 system (EPSON).

Statistics and Reproducibility

All experiments were reproducible and analyzed in GraphPad Prism 9. Significance was assessed using unpaired two-tailed t-tests for two categories, one-way ANOVA for multiple categories, and linear regression for growth curve comparisons. Significance levels: NS (not significant); *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

Results

Diminished NONHSAT227443.1 Expression in Esophageal Cancer Tissues and Cells: A Chemotherapy Responsiveness Indicator

To unveil dysregulated lncRNAs associated with the response to chemotherapy, we conducted a comprehensive lncRNA-seq analysis on esophageal cancer tissues obtained from the discovery cohort of 4 chemotherapy responders and 4 non-responders. Employing the threshold of P value <0.05 and |log2fold change| >1, we identified 1015 upregulated lncRNAs and 1184 downregulated lncRNAs in responders compared to non-responders (shown in Figure 1A and Table SI). Among these dysregulated lncRNAs, we concentrated on five downregulated candidates (NONHSAT100798.2, NONHSAT054462.2, NONHSAT149530.1, NONHSAT123314.2, and NONHSAT227443.1) in responders, due to their notable fold change differences and consistent expression across individual samples. The choice of five downregulated candidates is driven by the fact that their similar change trend is beneficial for identifying the most prominent one for further investigation.Their expression was visualized with a heatmap in Figure 1B. qRT-PCR on the same clinical samples consistently validated altered expression patterns. Notably, NONHSAT227443.1 exhibited the most significant reduction in responders compared to non-responders (shown in Figure 1C). To further validate our findings in a larger cohort, we utilized qRT-PCR to assess the expression of NONHSAT227443.1 in cancer tissues and corresponding adjacent normal tissues obtained from 36 patients with esophageal cancer. Our analysis revealed a significant increase in the expression of NONHSAT227443.1 in the cancer tissues(Figure 1D). Patient demographics of both discovery and replication cohort are detailed in Table 2.

Figure 1.

The volcano plot illustrates identified 1015 upregulated (yellow dots) and 1184 downregulated (purple dots)lncRNAs in esophageal carcinoma tissues between chemotherapy non-responder and responder, meeting significance criteria of adjusted P value <0.05 and |log2fold change|>1. B. Heatmap depicted the expression level of five downregulated long non-coding RNAs (lncRNAs), namely NONHSAT100798.2, NONHSAT054462.2, NONHSAT149530.1, NONHSAT123314.2, and NONHSAT227443.1, across each clinical tissue in eight esophageal cancer patients. C. In concordance with the findings from lncRNA-Seq, qRT-PCR also demonstrated five lncRNAs NONHSAT100798.2, NONHSAT054462.2, NONHSAT149530.1, NONHSAT123314.2, and NONHSAT227443.1 were downregulated in esophageal carcinoma tissues of chemotherapy (CT) responder compared to chemotherapy non-responder. D. The bar graph depicted the qRT-PCR results, indicating the NONHSAT227443.1 was highly expressed in three esophageal carcinoma cell lines, including TE-1, TE-12, and KYSE-150 cells, compared to Het-1A controls, whereas chemotherapy significantly reduced its expression. E. The qRT-PCR analysis further demonstrated that NONHSAT227443.1 exhibited high expression levels in esophageal carcinoma tissue when compared to paracarcinoma tissue.

Table 2.

Patients Information.

Terms	Discovery cohort		Replication cohort
	Non-responder	Responder	Esophageal cancer patients
	n = 4	n = 4	n = 36
Age, range	53.30 [43–62]	53.25 [44–66]	59.86 [43–75]
Gender (Male, Female)	3, 1	3, 1	27, 9
cTNM stage
I	0	0	0
II	2	2	10
III	2	2	26
ypTNM stage
I	0	4	13
II	2	0	5
III	2	0	18
CAP Score
0∼1	0	4	15
3	4	0	21

cTNM stage: Clinical Tumor-Node-Metastasis stage; ypTNM stage: Postneoadjuvant Therapy Tumor-Node-Metastasis stage; CAP Score: Tumor Regression Score designed by College of American Pathologists.

Expanding our study, we investigated various human esophageal cancer cell lines, including TE-1 (well-differentiated), TE-12 (moderately differentiated), and KYSE-150 (poorly differentiated), alongside human esophageal normal cells (Het-1A). Rigorous qRT-PCR analysis (shown in Figure 1E) revealed significant overexpression of NONHSAT227443.1 in cancer cell lines compared to controls. Remarkably, chemotherapy exposure notably reduced this heightened expression, suggesting NONHSAT227443.1's potential as a key chemotherapy response indicator in esophageal cancer cells.

Overexpressed NONHSAT227443.1 Enhanced Invasive Esophageal Cancer Cell Processes

To delve into the underlying mechanisms governed by NONHSAT227443.1 in human esophageal cancer cells, we constructed overexpression and silencing vectors for NONHSAT227443.1, along with empty control vectors, in TE-1 and KYSE-150 cell lines. Transfection efficacy was confirmed via qRT-PCR. As illustrated in Figure 2A, our findings demonstrated a significant increase in NONHSAT227443.1 expression (P < 0.001) following NONHSAT227443.1 vector transfection, while si-NONHSAT227443.1 transfection led to marked downregulation (P < 0.001). MTT assays indicated that NONHSAT227443.1 overexpression significantly promoted cell viability, and vice versa (shown in Figure 2B). The plate cloning assay demonstrated a significant increase in cell proliferation in TE-1 and KYSE-150 cells following the overexpression of NONHSAT227443.1. Conversely, silencing NONHSAT227443.1 in TE-1 and KYSE-150 cells led to a notable decrease in cell proliferation, as illustrated in Figure 2C. Additionally, flow cytometry demonstrated that NONHSAT227443.1 overexpression inhibited cell apoptosis compared to the control group, while NONHSAT227443.1 silencing promoted cell apoptosis (shown in Figure 2D).

Figure 2.

The overexpression and silencing of NONHSAT227443.1 were successfully achieved through transfection by overexpressed NONHSAT227443.1 vector and si-NONHSAT227443.1 vector in TE-1 and KYSE-150 cells. B. MTT assays indicated that NONHSAT227443.1 overexpression significantly promoted cell viability, and vice versa. C. Compared to the human esophageal cancer cells transfected with the empty vector, NONHSAT227443.1 overexpression in TE-1 and KYSE-150 cells significantly enhanced cell proliferation, while the NONHSAT227443.1 silencing in TE-1 and KYSE-150 cells resulted in decreased cell proliferation. D. Flow cytometry demonstrated that NONHSAT227443.1 overexpression inhibited cell apoptosis compared to the control group, while NONHSAT227443.1 silencing promoted cell apoptosis.

NONHSAT227443.1 Negatively Regulated the Expression of Myocardin-Related Transcription Factor-B (MRTFB)

To delve into the function of NONHSAT227443.1 in human esophageal cancer cells, we predicted 49 potential target mRNAs with the RNAplex software and constructed the protein-protein interaction network in Figure 3A. Based on the literature research, we identified MRTFB as a candidate target mRNA, which is involved in various cancer-related cellular processes, including cell cycle progression,¹⁴ tumor growth,¹⁵ and cancer invasion.¹⁶ Based on the complementarity pairing sequence information of MRTFB and NONHSAT227443.1, we speculated the interaction between them. Besides, the luciferase assay revealed NONHSAT227443.1 can negatively regulated the expression of MRTFB (as shown in Figure 3B).

Figure 3.

The protein-protein interaction network elucidates the target relationship between NONHSAT227443.1 and 49 predicted downstream molecules, one of which is the cancer-related gene, MRTFB. B. The luciferase assay revealed a notable reduction in relative luciferase activity in wt-MRTFB/NHSAT227443.1 compared to wt-MRTFB/NC. However, no significant difference was observed between mut-MRTFB/NHSAT227443.1 and mut-MRTFB/NC. C.Western blot results revealed MRTFB protein level was downregulated in both TE-1 and KYSE-150 cells compare to the Het-1A controls, while chemotherapy can significantly upregulated the MRTFB protein level. D-E. The qRT-PCR results demonstrated MRTFB expression level was downregulated under NONHSAT227443.1 overexpressed cells, and upregulated under NONHSAT227443.1 silenced cells in TE-1 cells (D) and KYSE-150 cells (E). F. Bar graphs unveil the identification of 41 distinct KEGG pathway types that are distributed across six classes. G. In a more extensive analysis, we crafted the bar graph encompassing the 21 signaling transduction pathways linked to environmental information processing.

To validate the role of MRTFB in esophageal cancer cells, we measured its protein levels in different experimental groups. Intriguingly, MRTFB was downregulated in TE-1 and KYSE-150 cells compared to Het-1A controls, but chemotherapy notably upregulated MRTFB protein levels (shown in Figure 3C). Furthermore, we investigated the impact of NONHSAT227443.1 on MRTFB expression in these cells. The result demonstrated NONHSAT227443.1 overexpression led to MRTFB downregulation, while its silencing upregulated MRTFB, indicating the negative regulation of NONHSAT227443.1 on MRTFB in esophageal cancer cells (shown in Figure 3D-E). Furthermore, to gain insights into the potential molecular mechanism underlying chemotherapy resistance, we conducted KEGG pathway analysis based on all lncRNA-seq target genes. This analysis revealed the identification of 41 distinct pathway types distributed grouped into six classes (shown in Figure 3F). We focused on 21 signaling pathways related to environmental information processing (shown in Figure 3G), with the PI3K-AKT (hsa04151) pathway showing the most significant involvement of targeted mRNAs.

Downregulated MRTFB Promoted Invasive Esophageal Cancer Cell Processes via PI3K/AKT Pathway Regulation

To investigate the function of MRTFB in human esophageal cancer cell, we generated MRTFB-overexpressed and silenced cell lines in TE-1 and KYSE-150, along with empty vector control cell lines. qRT-PCR confirmed substantial upregulation and downregulation of MRTFB expression in MRTFB-overexpressed and silenced cell lines, respectively, compared to empty vector-transfected cells (shown in Figure 4A).

Figure 4.

The qRT-PCR results unveiled a significant upregulation of MRTFB expression in both TE-1 and KYSE-150 cells upon transfection with MRTFB-overexpressed cell lines, while MRTFB-silenced cell lines exhibited a marked downregulation, both compared to cells transfected with empty vectors. B. MTT assays indicated that MRTFB overexpression significantly inhibited TE-1 and KYSE-150 cell viability, on the other hand, silencing MRTFB promoted TE-1 and KYSE-150 cell viability. C. The overexpression of MRTFB significantly impeded the proliferation of TE-1 and KYSE-150 cells in contrast to human esophageal cancer cells transfected with the empty vector, whereas MRTFB silencing resulted in an elevation of cell proliferation. D. Flow cytometry demonstrated that MRTFB overexpression promoted TE-1 and KYSE-150 cell apoptosis compared to the control group, while MRTFB silencing inhibited TE-1 and KYSE-150 cell apoptosis. E. Western blot analysis unveiled the silencing of MRTFB in cell lines led to a significant accumulation of p-PI3K and p-AKT proteins compared to the control groups, while MRTFB overexpression significantly reduced the expression of p-PI3K and p-AKT proteins. F. Western blot analysis revealed comparable outcomes to the MRTFB overexpression in cell lines, wherein treatment with the PI3K/AKT inhibitor LY294002 resulted in a significant reduction of p-PI3K and p-AKT proteins compared to the control groups.

Subsequently, we assessed the effects of MRTFB on various cell behavior, including cell viability, proliferation, and apoptosis. Intriguingly, MTT assays indicated that MRTFB overexpression significantly inhibited TE-1 and KYSE-150 cell viability, while silencing MRTFB promoted TE-1 and KYSE-150 cell viability (shown in Figure 4C). Consistent with these findings, MRTFB overexpression significantly inhibited proliferation in TE-1 and KYSE-150 cells compared to empty vector-transfected cells (shown in Figure 4B). Conversely, MRTFB silencing led to an increase in cell proliferation. Furthermore, flow cytometry clearly demonstrated that MRTFB overexpression promoted TE-1 and KYSE-150 cell apoptosis compared to controls, and MRTFB silencing inhibited TE-1 and KYSE-150 cell apoptosis (shown in Figure 4D).

Based on KEGG pathway analysis (shown in Figure 3F), we've spotlighted the PI3K-AKT (hsa04151) pathway for in-depth exploration. This choice stems from its evident influence on a substantial fraction of dysregulated lncRNA's target mRNAs across diverse chemotherapy responses, and its established role in cancer. Moreover, MRTFs, including MRTFA and MRTFB, were also found to activate PI3K/AKT signaling via diverse mechanisms.^17,18 As portrayed in Figure 4E, the suppression of MRTFB in the cell lines notably resulted in a substantial accumulation of p-PI3K and p-AKT proteins in comparison to the controls. Conversely, the overexpression of MRTFB distinctly curtailed the expression levels of p-PI3K and p-AKT proteins. Remarkably, akin outcomes were observed when the cell lines were treated with the PI3K/AKT inhibitor LY294002, as depicted in Figure 4F. These compelling findings robustly indicate the potential role of MRTFB in modulating the PI3K/AKT signaling pathway.

Discussion

In the present study, we unveiled that overexpressed NONHSAT227443.1 in both clinical esophageal cancer tissue samples and cell lines, which can be reduced by chemotherapy. This overexpression led to a significant decrease in MRTFB expression. Interestingly, both the high NONHSAT227443.1 levels and low MRTFB levels correlated with increased cancer cell activity, enhanced proliferation, and reduced apoptosis. Additionally, we observed that low MRTFB expression is linked to the activation of the PI3K/AKT pathway. These findings provide important insights into the functional role of NONHSAT227443.1 and MRTFB in esophageal cancer, highlighting their potential as therapeutic targets for improving the response to paclitaxel + platinum chemotherapy in non-responsive cases.

In recent studies, emerging evidence has highlighted the involvement of specific lncRNAs (such as CCAT2, LOC285194, and GAS5) in conferring resistance to cytotoxic agents.^19–21 Zhou et al²² examined 162 randomly selected ESCC patients and unveiled dysregulated lncRNA, AFAP1-AS1, displayed significant upregulation in tumor tissues and exhibited correlations with definitive chemoradiotherapy (dCRT) response. In alignment with these observations, we identified the substantial upregulation of NONHSAT227443.1 in both the paclitaxel + platinum resistance clinical ESCC tissue samples and cell lines (TE-1, TE-12, KYSE-150). In addition, overexpression of NONHSAT227443.1 was demonstrated to be associated with heightened cancer cell activity, increased proliferation, and suppressed apoptosis in our findings. This supports the notion that dysregulated NONHSAT227443.1 could hold crucial implications for the identification of patients likely to respond favorably or unfavorably to chemotherapy.

To gain a deeper understanding of the function of NONHSAT227443.1 in human esophageal cancer cells, we identified MRTFB as a candidate target mRNA due to its critical roles in cancer-related cellular processes. In the current study, we employed a combination of bioinformatics analysis and experimental validation to predict and confirm the regulatory relationship between NONHSAT227443.1 and MRTFB. Specifically, we utilized computational algorithms to identify potential target genes of NONHSAT227443.1, followed by functional assays such as luciferase assays to validate the direct binding and subsequent downregulation of MRTFB by NONHSAT227443.1. In addition, we also observed their associations with elevated cancer cell activity, enhanced proliferation, and inhibited apoptosis. In line with this, MRTFB has surfaced as a potential cancer driver gene in gastrointestinal (GI) tract transposon mutagenesis studies, including colorectal cancer (CRC), hepatocellular carcinoma (HCC), and pancreatic ductal adenocarcinoma (PDAC). Moreover, reduced MRTFB expression is often linked to tumor development, aggressive cancer behavior, and decreased patient survival in these GI tract cancers.^{15,16,23–26} As a crucial member of the myocardin family, MRTFB functions as a well-established co-activator for the transcription factor serum response factor (SRF), which plays a pivotal role in regulating cytoskeletal and muscle-specific gene expression.^27,28 Despite being recognized as an effective promoter of growth arrest and differentiation in specific tumors, MRTFB is often suppressed during human malignant transformation.^29,30 Therefore, diminished MRTFB expression contributes to the enhancement of tumor cell invasion and metastasis.

In addition, in order to elucidate the chemoresistance mechanisms in esophageal cancer patients from a pathway perspective, we performed KEGG pathway enrichment analysis using target mRNAs predicted from dysregulated lncRNAs identified through lncRNA-seq. While we did not identify any significantly enriched signaling pathway related to MRTFB, our focus shifted to the PI3K-AKT (hsa04151) pathway for more comprehensive investigation, which involved a substantial subset of target mRNAs, as well as had established significance in cancer biology. Intriguingly, our findings suggest that MRTFB may act as a potential negative regulator of the PI3K/AKT pathway, and its downregulation may contribute to the heightened cancer cell activity observed in esophageal cancer. The PI3K/AKT pathway is well-known for its critical role in promoting cell survival, growth, and proliferation.³¹ Activation of PI3K/AKT signaling pathway has also been implicated in the chemoresistance of various human malignant tumor, such as prostate cancer,^32,33 multiple myeloma,³⁴ ovarian cancer,³⁵ and breast cancer.^36,37 Therefore, in the context of chemotherapy non-responders, the observed upregulation of NONHSAT227443.1 may play a pivotal role in modulating the expression of MRTFB. This modulation, along with their associations with elevated cancer cell activity, enhanced proliferation, and inhibited apoptosis, may collectively contribute to the chemoresistance phenotype in esophageal cancer. While a direct link between MRTFB and the PI3K/AKT pathway is not supported by existing literature, our findings suggest a potential indirect association.Several downstream or co-activated molecules of MRTFB, including MCAM,²⁵ SRF and extracellular signal-regulated protein kinase (ERK)³⁸ have been previously reported to play roles in metastasis and chemotherapy resistance in various cancer types through the PI3K/AKT pathway.^39–41 This implies that MRTFB, although not directly associated with the PI3K/AKT pathway, may function within the context of this pathway through its downstream effectors and interact with the intricate network of dysregulated lncRNAs. Consequently, the observed upregulation of NONHSAT227443.1 in chemotherapy non-responders may reflect a complex interplay between MRTFB, the PI3K/AKT pathway, and other dysregulated lncRNAs, contributing to the chemoresistance observed in esophageal cancer.

Our study sheds light on the potential interplay between MRTFB, the PI3K/AKT pathway, and the intricate network of dysregulated lncRNAs in the context of esophageal cancer chemoresistance. Further functional studies are warranted to delineate the precise mechanisms underlying MRTFB's role in regulating the PI3K/AKT pathway and its contribution to the observed therapeutic responses.

However, several questions and challenges warrant further investigation. Firstly, while our study hints at an intricate association between MRTFB and the PI3K/AKT pathway, further exploration of the downstream effectors of MRTFB in this context is essential for a comprehensive perspective. Identifying the target genes that MRTFB modulates within the PI3K/AKT pathway could offer additional insights into its role in cancer progression. Furthermore, our study, primarily conducted with the available data, highlights the potential clinical relevance of NONHSAT227443.1 and MRTFB. However, we recognize the need to validate these findings in larger patient cohorts to establish their robustness as potential prognostic markers or therapeutic targets. Future research efforts should encompass a more extensive patient population to further elucidate the clinical implications of our findings.

Conclusion

In conclusion, the present study significantly advances our understanding of the functional roles of NONHSAT227443.1 and MRTFB in esophageal cancer. Their dysregulation appears to contribute to heightened cancer cell activity, increased proliferation, and suppressed apoptosis, with MRTFB potentially influencing the PI3K/AKT pathway. These findings highlight the importance of further investigating the underlying mechanisms and clinical implications of these molecules in esophageal cancer. The identification of NONHSAT227443.1 and MRTFB as potential therapeutic targets offers promising opportunities for the development of novel intervention strategies aimed at improving the prognosis and treatment of esophageal cancer patients.

Supplemental Material

sj-docx-1-tct-10.1177_15330338241274369 - Supplemental material for LncRNA NONHSAT227443.1 Confers Esophageal Squamous Cell Carcinoma Chemotherapy Resistance by Activating PI3K/AKT Signaling via Targeting MRTFB

Supplemental material, sj-docx-1-tct-10.1177_15330338241274369 for LncRNA NONHSAT227443.1 Confers Esophageal Squamous Cell Carcinoma Chemotherapy Resistance by Activating PI3K/AKT Signaling via Targeting MRTFB by Yuchen Wang, PhD, Yingying Wang, PhD, Jinze Zhang, PhD, Zhihua Shi, PhD, and Junfeng Liu, PhD in Technology in Cancer Research & Treatment

Footnotes

Abbreviations

Author Contributions

Yuchen Wang led this research endeavor, overseeing experimental design, conducting experiments, and taking the lead in manuscript writing. Yingying Wang, Jinze Zhang, and Zhihua Shi significantly contributed by assisting in experiments and participating in manuscript revision. Junfeng Liu provided valuable input in project design and offered critical feedback during manuscript revision.

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval and Consent to Participate

All procedures performed involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval (No. 2022MECD58) was granted by the Human Research Ethics Committee at the Fourth Hospital of Hebei Medical University. Informed consent was obtained from all volunteers.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study received financial support solely from The Fourth Hospital of Hebei Medical University, without any additional external funding sources.

ORCID iD

Junfeng Liu

Supplemental Material

Supplemental material for this article is available online.

References

Ferlay

Colombet

Soerjomataram

, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941-1953.

Siegel

Miller

Fuchs

Jemal

. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33.

Siegel

Miller

Jemal

. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7-30.

Yang

Niu

Sun

. Current neoadjuvant therapy for operable locally advanced esophageal cancer. Med Oncol. 2023;40(9):252.

Liu

Bian

Jing

. T-cell exhaustion prediction algorithm in tumor microenvironment for evaluating prognostic stratification and immunotherapy effect of esophageal cancer. Environ Toxicol. 2024;39(2):592-611.

Lopez

Harada

Chen

, et al. Taxane-based or platinum-based combination chemotherapy given concurrently with radiation followed by surgery resulting in high cure rates in esophageal cancer patients. Medicine (Baltimore). 2020;99(9):e19295.

Adenis

Kulkarni

Girotto

, et al. Impact of pembrolizumab versus chemotherapy as second-line therapy for advanced esophageal cancer on health-related quality of life in KEYNOTE-181. J Clin Oncol. 2022;40(4):382-391.

Zhu

Chen

Shen

Tang

Yang

Zheng

. Long noncoding RNA LINC-PINT suppresses cell proliferation, invasion, and EMT by blocking wnt/beta-catenin signaling in glioblastoma. Front Pharmacol. 2021;11:586653.

Zhao

Lan

Chen

. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J. 2022;20:4381-4389.

10.

Altschuler

Stockert

Kyprianou

. Non-coding RNAs set a new phenotypic frontier in prostate cancer metastasis and resistance. Int J Mol Sci. 2021;22(4):2100.

11.

Yao

Pan

, et al. Long noncoding RNA RP11-766N7.4 functions as a tumor suppressor by regulating epithelial-mesenchymal transition in esophageal squamous cell carcinoma. Biomed Pharmacother. 2017;88:778-785.

12.

Farooqi

Legaki

Gazouli

Rinaldi

Berardi

. MALAT1 as a versatile regulator of cancer: overview of the updates from predatory role as competitive endogenous RNA to mechanistic insights. Curr Cancer Drug Targets. 2021;21(3):192-202.

13.

Zhang

Tian

Wang

Niu

. lncRNA CCAT1 is a biomarker for the proliferation and drug resistance of esophageal cancer via the miR-143/PLK1/BUBR1 axis. Mol Carcinog. 2019;58(12):2207-2217.

14.

Shaposhnikov

Kuffer

Storchova

Posern

. Myocardin related transcription factors are required for coordinated cell cycle progression. Cell Cycle. 2013;12(11):1762-1772.

15.

Hampl

Martin

Aigner

, et al. Depletion of the transcriptional coactivators megakaryoblastic leukaemia 1 and 2 abolishes hepatocellular carcinoma xenograft growth by inducing oncogene-induced senescence. EMBO Mol Med. 2013;5(9):1367-1382.

16.

Takeda

Wei

Koso

, et al. Transposon mutagenesis identifies genes and evolutionary forces driving gastrointestinal tract tumor progression. Nat Genet. 2015;47(2):142-150.

17.

Yoshida

Togi

Matsumae

, et al. CCN1 Protects cardiac myocytes from oxidative stress via beta1 integrin-AKT pathway. Biochem Biophys Res Commun. 2007;355(3):611-618.

18.

Small

O'Rourke

Moresi

, et al. Regulation of PI3-kinase/AKT signaling by muscle-enriched microRNA-486. Proc Natl Acad Sci U S A. 2010;107(9):4218-4223.

19.

Redis

Sieuwerts

Look

, et al. CCAT2, A novel long non-coding RNA in breast cancer: Expression study and clinical correlations. Oncotarget. 2013;4(10):1748-1762.

20.

Tong

Zhou

Wang

, et al. Association of decreased expression of long non-coding RNA LOC285194 with chemoradiotherapy resistance and poor prognosis in esophageal squamous cell carcinoma. J Transl Med. 2014;12:233.

21.

Long

Song

, et al. Long non-coding RNA GAS5 inhibits DDP-resistance and tumor progression of epithelial ovarian cancer via GAS5-E2F4-PARP1-MAPK axis. J Exp Clin Cancer Res. 2019;38(1):345.

22.

Zhou

Wang

Zhu

, et al. High expression of long non-coding RNA AFAP1-AS1 predicts chemoradioresistance and poor prognosis in patients with esophageal squamous cell carcinoma treated with definitive chemoradiotherapy. Mol Carcinog. 2016;55(12):2095-2105.

23.

Mann

Ward

Yew

, et al. Sleeping beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma. Proc Natl Acad Sci U S A. 2012;109(16):5934-5941.

24.

Bard-Chapeau

Nguyen

Rust

, et al. Transposon mutagenesis identifies genes driving hepatocellular carcinoma in a chronic hepatitis B mouse model. Nat Genet. 2014;46(1):24-32.

25.

Kodama

Marian

Lee

, et al. MRTFB Suppresses colorectal cancer development through regulating SPDL1 and MCAM. Proc Natl Acad Sci U S A. 2019;116(47):23625-23635.

26.

Song

Liu

Sun

, et al. The MRTF-A/B function as oncogenes in pancreatic cancer. Oncol Rep. 2016;35(1):127-138.

27.

Posern

Treisman

. Actin' together: Serum response factor, its cofactors and the link to signal transduction. Trends Cell Biol. 2006;16(11):588-596.

28.

Olson

Nordheim

. Linking actin dynamics and gene transcription to drive cellular motile functions. Nat Rev Mol Cell Biol. 2010;11(5):353-365.

29.

Pipes

Creemers

Olson

. The myocardin family of transcriptional coactivators: Versatile regulators of cell growth, migration, and myogenesis. Genes Dev. 2006;20(12):1545-1556.

30.

Shang

Cong

Bian

Yuan

. MicroRNA-135b promotes proliferation, invasion and migration of osteosarcoma cells by degrading myocardin. Int J Oncol. 2014;45(5):2024-2032.

31.

Cully

You

Levine

Mak

. Beyond PTEN mutations: The PI3K pathway as an integrator of multiple inputs during tumorigenesis. Nat Rev Cancer. 2006;6(3):184-192.

32.

Toren

Zoubeidi

. Targeting the PI3K/AKT pathway in prostate cancer: Challenges and opportunities (review). Int J Oncol. 2014;45(5):1793-1801.

33.

Braglia

Zavatti

Vinceti

Martelli

Marmiroli

. Deregulated PTEN/PI3K/AKT/mTOR signaling in prostate cancer: Still a potential druggable target? Biochim Biophys Acta Mol Cell Res. 2020;1867(9):118731.

34.

Wang

Lin

. The PI3K/AKT signaling pathway regulates ABCG2 expression and confers resistance to chemotherapy in human multiple myeloma. Oncol Rep. 2019;41(3):1678-1690.

35.

Ning

Luo

Feng

Wang

. Let-7d increases ovarian cancer cell sensitivity to a genistein analog by targeting c-Myc. Oncotarget. 2017;8(43):74836-74845.

36.

Zhou

Jiang

Yang

. QPRT Acts as an independent prognostic factor in invasive breast cancer. J Oncol. 2022;2022:6548644.

37.

Lyu

Wang

Huang

Wang

Liu

. Survivin-targeting miR-542-3p overcomes HER3 signaling-induced chemoresistance and enhances the antitumor activity of paclitaxel against HER2-overexpressing breast cancer. Cancer Lett. 2018;420:97-108.

38.

Ihara

Miyata

Fukuchi

Tsuda

Tabuchi

. SRF And SRF cofactor mRNA expression is differentially regulated by BDNF stimulation in cortical neurons. Biol Pharm Bull. 2023;46(4):636-639.

39.

Jiang

Dai

Feng

Peng

. Role of PI3K/AKT pathway in cancer: The framework of malignant behavior. Mol Biol Rep. 2020;47(6):4587-4629.

40.

Tripathi

Fahrmann

Celiktas

, et al. MCAM Mediates chemoresistance in small-cell lung cancer via the PI3K/AKT/SOX2 signaling pathway. Cancer Res. 2017;77(16):4414-4425.

41.

De Roock

Claes

Bernasconi

, et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: A retrospective consortium analysis. Lancet Oncol. 2010;11(8):753-762.

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