Cellular and Molecular Biology
Open Access

A549 cells; Anticancer; LncRNA; Non-small cell lung cancer (NSCLC); Phycocyanin

Introduction

Lung cancer remained the leading cause of cancer-related death in 2020, with an estimated 1.8 million deaths [1]. Non-small cell lung cancer (NSCLC), a subtype of lung cancer, accounts for approximately 85% of lung cancer cases and is characterized by high mortality and a low cure rate [2,3]. Currently, most NSCLCs are not effectively diagnosed at the early stage. Although treatments such as radiation therapy, chemotherapy or surgery have improved the survival rates of patients to a certain extent [4], the therapeutic efficacy of treatments for NSCLC is still very poor due to the lack of known biomarkers of NSCLC [4,5]. Therefore, in-depth research to identify specific biomarkers of NSCLC is urgently needed for the development of new drugs that effectively treat NSCLC.

Long noncoding RNAs (LncRNA s) are a class of regulatory RNA molecules that are larger than 200 nucleotides and have no protein-coding ability [6]. Unlike microRNAs and small nucleolar/ nuclear RNAs, LncRNA s have complex higher order structures [7]. It has been shown that the ectopic expression of LncRNA s is relevant to the occurrence and metabolism of lung cancer. Luo et al. reported that LncRNA TRERNA1 could promote the malignant progression of NSCLC by targeting FOXL1 [8]. Zhao et al. found that LncRNA SNHG14 also contributed to the growth of NSCLC via the miR-206/G6PD pathway [9]. In addition, some other LncRNA s, such as FENDRR, could exert antineoplastic effects on NSCLC cells by regulating the miR-761/TIMP2 axis [10]. Therefore, in-depth exploration of the regulatory role of LncRNA s in NSCLC and their mechanism is of great importance for the future treatment of NSCLC.

Phycocyanin, a pigment-protein complex derived from cyanobacteria, is a well-known natural food additive [11]. It has been revealed that phycocyanin, which is a potential natural antineoplastic factor, exerts antitumor effects on NSCLC [12,13]. Previously, we performed a miRNA-seq analysis and discovered that miR-6883- 3p, miR-3150a-3p, miR-642a-5p, and miR-627-5p were involved in the growth inhibitory effect of phycocyanin on NSCLC cells [14,15]. However, few studies have investigated the LncRNA expression profiles in phycocyanin-treated NSCLC cell lines. As LncRNA s are the upstream regulators of miRNAs in cells [16], further exploration of the expression profiles of LncRNA s participating in the phycocyaninmediated antineoplastic process is vital to elucidating the mechanism of phycocyanin-mediated inhibitory effects on NSCLC.

In the present study, to further investigate the precise antineoplastic regulatory mechanism of phycocyanin in NSCLC, a LncRNA-seq was performed to establish the LncRNA profile of NSCLC A549 cells after phycocyanin exposure. The results of this work are expected to explore the anti-tumor mechanism of phycocyanin at the molecular level, which undoubtedly laid a theoretical basis for the potential treatment of NSCLC with phycocyanin and provided insights into the regulatory mechanism of phycocyanin.

Materials and Methods

Study information

This work included experimental techniques such as cell migration and cell viability assays, RNA extraction, RNA-seq assay, qRT‒PCR, and bioinformatics analysis. The present work was performed in the labs of Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University in 2023.

Cell culture and sample treatment

A549 and H460 cells were cultured in DMEM supplemented with 0.1 mg/mL streptomycin, 100 units/mL penicillin and 10% fetal bovine serum (FBS) in a humidified atmosphere with 5% CO2 at 37 °C (American Type Cell Collection, ATCC, Manassas, VA, USA). Phycocyanin (95% purity) was purchased from Enviroloix (Portland, ME, USA). A549 cells were treated with phycocyanin or PBS as a control, followed by cell lysis and RNA extraction. The control groups were named Con-1, 2 and 3, and the phycocyanin-treated groups were named PC-1, 2 and 3.

RNA extraction and quality evaluation

The High Pure RNA isolation kit was employed for total RNA extraction according to the manufacturer’s specification (Roche, Penzberg, Germany). A Qubit fluorometer (Life Technologies, Eugene, OR, USA) with the Qubit RNA Assay Kit and NanoDrop-1000 instrument were used to evaluate the quality of RNA (Thermo Fisher Scientific Inc., Waltham, USA). The Agilent Bioanalyzer microcapillary electrophoresis system was used to assess RNA quality (Agilent, Santa Clara, CA, USA).

RNA sequencing

Two milligrams of polyA-minus RNA were used for cDNA library establishment. After total RNAs were extracted, mRNA and noncoding RNAs were enriched by removing rRNA, followed by fragmenting into short fragments (200-500 nt) using EpicentreRibo- Zero™ rRNA RemovalKit (Epicentre, WI, USA). RNA-sequencing libraries were generated under the instructions provided by NEB Next Ultra™ Directional RNA Library Prep kit forIllumina (New England BioLabs, Inc.). The Illumina HiSeqTM 2000 Sequencing System by Berry Genomics Inc. (Beijing, China) was employed for sequencing. Differential expression analysis for LncRNA from the database has proceeded. The sequencing reads were mapped to the reference genome using the TopHat program. A coding potential calculator assay was employed for evaluation. The quality control of raw data was performed using SeqPrep (https: //github.com/jstjohn/SeqPrep) and Sickle (https: //github.com/najoshi/sickle). TopHat2 (http: //ccb.jhu.edu/software/ tophat/index.shtml) was used for sequence comparative analysis after quality control. The Cufflinks software (http: //cole-trapnell-lab.github. io/cufflinks/) was employed for transcript assembly.

Analysis of differentially expressed (DE) LncRNA s

The number of mapped tags that was given by the raw expression levels of that particular LncRNA. To correct for differences in tag count between samples, the tag counts were scaled to CPM (count per millions) based on the total number of aligned tags. The analysis of differentially expressed LncRNA between control and phycocyanintreated samples was performed by edgeR. The setting was |logFC| > 1 & FDR < 0.05.

Target genes of LncRNA s and enrichment analysis

KEGG pathway analysis was employed for signal transduction pathway analysis of key genes. The p-value and gene count thresholds of KEGG analysis was p-adjust < 0.05 and 7, respectively. Gene Ontology (GO) analysis was used to identify important biological functions or processes of target genes. The p-value and gene count thresholds of GO analysis was p-adjust < 0.05 and 35, respectively.

qRT‒PCR

qRT‒PCR was performed by the Q6 qPCR system. GAPDH was used as the endogenous control gene. SYBR Green Master Mix was used for signal detection. The 2-ΔΔCT method was used to calculate the expression of each gene. The primer sequences of LncRNA and GAPDH for qRT‒PCR were as follows: forward: 5'-CGTTGCCTACAGTCAAGTCAGT- 3'; reverse: 5'-GCCATGGTTCCCTTACTGATAC-3'. Forward: 5'-TGTTCGTCATGGGTGTGAAC-3'; Reverse: 5'-ATGGCATGGACTGTGGTCAT- 3'.

Cell viability assay

Cells were cultured in 96-well plates, followed by LncRNA inhibitor incubation for 24 h. The MTT method was employed to determine the viability of A549 cells at 24 and 48 h. Briefly, MTT was added to each well for 4 h, followed by HCl-SDS lysis buffer and the cells were incubated for 24 h. The absorbance at 570 nm was recorded using a micro plate reader.

Cell migration assay

Cells were incubated with LncRNA inhibitor and cultured in 6-well plates. A pipette tip was used to scratch the cells and create a "wound". Each wound was observed at three different positions and its width was recorded. The three width values were averaged. The widths of the wounds were observed at 0 and 24 h. The cell migration rate was calculated from the difference in scratch width at 24 h over the original width (0 h).

Statistical Analysis

The data were analyzed by Microsoft Excel and GraphPad Prism. A two-tailed Student’s t test (paired t-test) was used for data analysis, and p < 0.05 indicated a significant difference between the control and test groups.

Results and Discussion

Classification and subgroup analysis of LncRNA s

Phycocyanin has been reported to exert effective antineoplastic effects on NSCLC cells [12,13]. Further investigation of the underlying anticancer mechanism of phycocyanin can help clarify the pathogenesis of NSCLC, laying the theoretical basis for lung cancer treatment. LncRNA s are well-known important molecules that regulate gene expressi

Table S1: Statistics of lncRNAs.

Table S2: Classification and subgroup of lncRNAs.

on at the transcriptional, epigenetic, and posttranscriptional levels [17]. Numerous LncRNA s have been identified by highthroughput technology and are related to growth and development in cancer cells.

Over 26,000 LncRNA s (including known and novel LncRNA s) was identified in the Con and PC groups (Table S1). The chromosomal distribution and transcript types of LncRNA s were determined. In Table S2, of the antisense LncRNA s, the known LncRNA s accounted for 93.5%. Of the sense intron overlap, the known LncRNA s accounted for 96.1%. Of the intergenic LncRNA s, the known LncRNA s accounted for 99.4%. Of the sense exon overlap, the known LncRNA s accounted for 95.0%. Of the bidirectional LncRNA s, the known LncRNA s accounted for 99.9%.

Table S1: Statistics of lncRNAs.

Analysis of LncRNA expression and differentially expressed (DE) LncRNA s

As shown in Figure 1A, the LncRNA expression patterns of the Con and PC samples were similar, indicating the suitable replication of each experiment. Finally, 5,084 and 6,080 LncRNA transcripts were obtained from the Con and PC groups, respectively, after eliminating the single-exon, low-expression, and unreliable fragments. Of these, 4886 LncRNA s were found in both Con and PC samples (Figure 1B). Table S3 presents the top 15 LncRNA s that are expressed in the Con and PC groups based on TPM (transcripts per million reads). Strikingly, some LncRNA s was highly expressed in both the Con and PC groups, including ENST00000602361, ENST00000554988, ENST00000618589, and ENST00000659240. Some LncRNA s exhibited sample-specific expression. For instance, NONHSAT135828.2 and ENST00000508832 were specifically highly expressed in phycocyanin-treated A549 cells, while NONHSAT190639.1 and NONHSAT176665.1 were specifically highly expressed in control cells.

Figure 1: The expression analysis of lncRNAs. (A) The violin figure of expression pattern of lncRNAs in Con and PC groups. (B) Venn figure of the lncRNA numbers in Con and PC groups. (C) Heatmap of DE lncRNAs in each sample. Blue indicates low expression levels, red indicates high expression levels. (D) Number of DE lncRNAs between Con and PC groups. (E) Scatter diagram of DE lncRNAs. Red indicates upregulated lncRNAs, and green represents downregulated lncRNAs.

Table S3: The top expressed 15 lncRNA transcripts in Con and PC groups.

The heat map of differentially expressed LncRNA s showed that the expression patterns of LncRNA s within Con or PC samples were consistent, while the expression profiles between Con and PC groups exerted extremely significant differences (Figure 1C), indicating the effects of phycocyanin on the expression of LncRNA s in A549 cells. In total, 309 LncRNA s exhibited significant expression differences between the Con and PC groups, including 193 significantly upregulated and 116 significantly downregulated LncRNA s after phycocyanin treatment in A549 cells (Figure 1D). The scatter diagram of DE LncRNA s is shown in Figure 1E. The detailed information of DE LncRNA s was shown in Table S4. This LncRNA s is likely to be key LncRNA s that may act as downstream targets of phycocyanin in NSCLC cells. Table S5 shows the top 10 downregulated and top 10 upregulated LncRNA transcripts.

Table S4: The detailed information of DE lncRNAs.

Table S5: Top 10 upregulated and 10 downregulated lncRNA transcripts.

GO and KEGG enrichment analyses of DE LncRNA s and predicted targets

The DE LncRNA s was annotated by KEGG and GO enrichment analyses. Figure 2A shows the top 20 GO entries. Moreover, several of the GO items were related to cell activities, including GO: 0006955 (immune response), GO: 0070613 (regulation of protein processing), GO: 2000257 (regulation of protein activation cascade), GO: 1903034 (regulation of response to wounding), and GO: 0006952 (defense response). Figure 2B shows the KEGG pathway enrichment results.

Figure 2: The GO (A) and KEGG (B) enrichment analysis of DE lncRNAs. (C) The GO enrichment analysis of targets of DE lncRNAs.

The DE LncRNA s were shown to be enriched in signaling pathways related to cell growth and metabolism, such as the NOD-like receptor signaling pathway (map04621) and apoptosis (map04215). Strikingly, the DE LncRNA s were enriched in pathways associated with cancer (map05200) and small cell lung cancer (map05222) according to the KEGG analysis, suggesting that the DE LncRNA s affected the growth, apoptosis, and viability of A549 lung cancer cells, which were possibly regulated by phycocyanin.

The potential target genes of DE LncRNA s were predicted by a trans-regulation analysis method to determine the functions of this core LncRNA s. A total of 207 Trans targets (p-adjust < 0.01) for DE LncRNA s were screened and obtained. Detailed information on the potential targets of LncRNA s is shown in Table S6. Next, the target genes were annotated by GO enrichment analysis. In total, 780 GO entries were obtained, and Figure 2C presents the top 20 GO entries. It is worth noting that the DE LncRNA s were enriched in several GO items related to cell movement and proliferation were enriched, including GO: 0007166 (cell surface receptor signaling pathway), GO: 0006928 (movement of cell or subcellular component), GO: 0006955 (immune response), and GO: 0070613 (regulation of protein processing).Moreover, the Notch signaling pathway (GO: 0007219) was predicted to be activated in A549 cells after phycocyanin treatment.

Table S6: Detailed information for potential targets of DE lncRNAs.

In this work, 193 upregulated and 116 downregulated LncRNA s were identified between phycocyanin-treated and control A549 cells. In addition, the target genes (mRNAs) of DE LncRNA s were identified in PC samples, indicating that the expression of various genes was altered by LncRNA s after phycocyanin treatment. Currently, it has been reported that LncRNA s could be potential biomarkers for multiple cancers, including NSCLC [18,19]; however, the involved regulatory mechanism is not yet clearly understood. In this work, according to the GO enrichment analysis, the biological factors affected most by phycocyanin are extracellular region, extracellular space, and epithelium development, which suggested that NSCLC cell behavior is likely regulated by phycocyanin through LncRNA s and target genes. In addition, KEGG analysis of LncRNA s showed that the NOD-like receptor pathway, Notch pathway, and apoptosis were also enriched after phycocyanin exposure in A549 cells. Alzokaky et al. reported that phycocyanin could protect against ethanol-induced gastric ulcers in rats through multiple signaling pathways, including the HMGB1/ NLRP3/NF-κB and NOD-like receptor pathways [20]. It's worth mentioning that we used mRNA-seq to study the potential mechanism of phycocyanin, which also showed that "apoptosis pathway" and "NOD-like receptor pathway" were significantly enriched after phycocyanin explosure [21]. The similar conclusions of the two studies have reflected the potential regulatory mechanism of phycocyanin on NSCLC cells.

Establishment of the LncRNA ‒mRNA and target gene coexpression networks

As shown in Supplementary Figure S1, a comprehensive LncRNA ‒mRNA coexpression network was constructed. A total of 2,238 mRNA‒mRNA, LncRNA ‒LncRNA, and LncRNA ‒mRNA pairs were identified, showing the potential mutual effects of DE LncRNA s and mRNAs. Detailed information on LncRNA s and mRNAs is presented in Table S7. It is worth noting that ENSG00000130600 (LncRNA) and ENSG00000172638 (mRNA) showed the highest negative correlation coefficient (p-adjust < 0.01), while ENSG00000258479 (LncRNA) and ENSG00000188747 (mRNA) showed the highest positive correlation coefficient (p-adjust < 0.01). In addition, one mRNA with high correlation coefficient (such as ENSG00000188747) might have a regulatory relationship with multiple mRNAs and LncRNA s, including ENSG00000008517 (mRNA), ENSG00000078098 (mRNA), and ENSG00000254815 (LncRNA). The LncRNA ‒mRNA network revealed the underlying complex regulatory mechanisms of phycocyanin in A549 cells, providing a potential theoretical basis for NSCLC treatment.

Figure S1: lncRNA-mRNA co-expression networks.