Identification of significant molecules and biological processes in fluoxetine treated lung cancer cells

doi:10.21203/rs.3.rs-1620096/v1

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Identification of significant molecules and biological processes in fluoxetine treated lung cancer cells

https://doi.org/10.21203/rs.3.rs-1620096/v1

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Fluoxetine has shown anti-cancer responses in non-small cell lung cancer in vitro. However, the anticancer effect and mechanism of fluoxetine still need to be elucidated. In this study, we aim to figure out the significant molecules and biological processes by analyzing the RNA-seq data from the GEO database. The GSE200209 was created by the Illumina HiSeq 4000 (Homo sapiens). By analyzing the KEGG and GO enrichments, we found that the Lysosome and Autophagy are the major signaling pathways in the fluoxetine treated lung cancer cells. We further identified the top ten interactive molecules including CDK1, CCNB1, MAD2L1, AURKB, MCM3, MCM, CDC45, CDC20, RFC4, and CCNB2. This study may guide the treatment of lung cancer by using fluoxetine.

Lung cancer is the most common type of cancer in the world¹. Non-small-cell lung cancer (NSCLC) contains a large number of lung cancers and most patients with newly diagnosed NSCLC have advanced disease². Despite the novel therapies, the median survival of patients in advanced stages of lung malignancies is roughly 1 year². Only less than 5% of advanced NSCLC patients and less than 10% of advanced mesothelioma patients survive 5 years after diagnosis³. New treatment options are clearly warranted for these lung malignancies.

Fluoxetine was identified as a lipophilic weak base that can make direct contact with epithelial cells in the intestines⁴. In these epithelial cells, it causes an increase in serotonin (5-HT) levels by inhibiting L-monoamine oxidase and serotonin reuptake transporters⁵. In patients, fluoxetine therapy was reported to reduce the risk of colon cancer to almost 50%⁶. Several studies support the idea of fluoxetine reducing cancer incidence in different animal models, such as carcinogen-induced preneoplastic lesions and tumors in rats and mice, and xenograft tumors in immunosuppressed rats⁴. Thus, fluoxetine may become an important therapy for lung cancer.

Here, we analyzed the lung cancer cells with the treatment of Fluoxetine by using the RNA-seq data. We determined a number of DEGs and significant biological processes. We then performed the gene enrichment and protein-protein interaction (PPI) network analysis to obtain the interacting map. These biological processes may provide novel knowledge of lung cancer therapies.

Data resources

Gene dataset GSE200209 was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The data was created by the Illumina HiSeq 4000 (Homo sapiens) (Jinshan hospital of Fudan university, Longhang road, Shanghai 201508, China). The analyzed dataset includes three groups of non-small cell lung cancer H460 cells and three groups of H460 cancer cells with the treatment of fluoxetine.

Data acquisition and processing

The data were analyzed and conducted by the R package as previously described^7–14. We applied a classical t-test to identify DEGs with P < 0.05 and fold change ≥ 1.5 as being statistically significant.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO)

KEGG and GO analyses were executed by the R package (ClusterProfiler) and Reactome as described^15–19. P < 0.05 was considered statistically significant.

Protein-protein interaction (PPI) networks

The Molecular Complex Detection (MCODE) was used to construct the PPI networks. The significant modules were created from PPI networks and String networks (https://string-db.org/). The biological processes analyses were performed by applying Reactome, and P < 0.05 was considered significant.

Identification of DEGs in lung cancer cells with the treatment of fluoxetine

To determine the effects of fluoxetine on lung cancer cells, we analyzed the RNA-seq data of H460 lung cancer cells with the treatment of fluoxetine. A total of 1,028 genes were identified with a threshold of P < 0.05. The top up- and down-regulated genes were shown by the heatmap and volcano plot (Fig. 1). The top ten DEGs were listed in Table 1.

Enrichment analysis of DEGs in lung cancer cells with the treatment of fluoxetine

To further understand the molecular mechanisms of fluoxetine treated lung cancer cells, we performed the KEGG and GO analyses (Figure 2). We identified the top ten KEGG items, including “Lysosome”, “Autophagy – animal”, “Phagosome”, “Steroid biosynthesis”, “Fatty acid metabolism”, “Epithelial cell signaling in Helicobacter pylori infection”, “Terpenoid backbone biosynthesis”, “Collecting duct acid secretion”, “Biosynthesis of unsaturated fatty acids”, and “Circadian rhythm”. We also identified the top ten biological processes of GO enrichment, including “steroid metabolic process”, “macroautophagy”, “sterol metabolic process”, “organic hydroxy compound biosynthetic process”, “secondary alcohol metabolic process”, “steroid biosynthetic process”, “cholesterol metabolic process”, “sterol biosynthetic process”, “cholesterol biosynthetic process”, and “secondary alcohol biosynthetic process”. We identified the top ten cellular components of GO enrichment, including “vacuolar membrane”, “lysosomal membrane”, “lytic vacuole membrane”, “late endosome”, “vacuolar lumen”, “primary lysosome”, “azurophil granule”, “lysosomal lumen”, “proton−transporting V−type ATPase complex”, and “proton−transporting V−type ATPase, V1 domain”. We identified the top ten molecular functions of GO, including “active transmembrane transporter activity”, “secondary active transmembrane transporter activity”, “active ion transmembrane transporter activity”, “proton transmembrane transporter activity”, “ATPase−coupled transmembrane transporter activity”, “amino acid transmembrane transporter activity”, “ATPase−coupled ion transmembrane transporter activity”, “ATPase activity, coupled to transmembrane movement of ions, rotational mechanism”, “proton−transporting ATPase activity, rotational mechanism”, and “pyrophosphate hydrolysis−driven proton transmembrane transporter activity”.

PPI network and Reactome analyses

To explore the potential relationship among the DEGs, we constructed the PPI network by using 869 nodes and 4,381 edges. The combined score > 0.2 was set as a cutoff by using the Cytoscape software. Table 2 indicated the top ten genes with the highest scores. The top two significant modules were presented in Figure 3. We further analyzed the PPI and DEGs with a Reactome map (Figure 4) and identified the top ten biological processes including “Activation of gene expression by SREBF (SREBP)”, “Regulation of cholesterol biosynthesis by SREBP (SREBF)”, “Cholesterol biosynthesis”, “Metabolism of steroids”, “DNA strand elongation”, “Insulin receptor recycling”, “Amino acids regulate mTORC1”, “G1/S-Specific Transcription”, “PPARA activates gene expression”, and “Regulation of lipid metabolism by PPARalpha” (Supplemental Table S1).

Fluoxetine has been reported to elicit the anti-cancer response in liver cancer and non-small cell lung cancer in vitro²⁰. However, the anticancer effect and mechanism of fluoxetine still need to be elucidated. Thus, our study explored the biological processes of Fluoxetine treated lung cancer cells by using RNA-seq analysis.

"Lysosome" and "Autophagy" are the major signaling pathways in the Fluoxetine treated lung cancer cells by analyzing the KEGG enrichment in this study. Ziv Radisavljevic et al found that lysosome leads to cancer and promotes metastasis or cancer relapse²¹. Autophagy is a mechanism by which cellular material is passed to lysosomes for degradation, resulting in the basal turnover of cell components and gaining energy and macromolecular precursors. Autophagy can inhibit cancer, and both stimulate and inhibit autophagy have been proposed as cancer therapies²². Circadian clocks regulate a variety of cell functions including metabolism, proliferation, differentiation, migration, and aging^23–33. Current studies have shown that autophagy is activated rhythmically in a clock-dependent manner whether the organism is healthy or has certain diseases such as cancer³⁴.

Our study also discovered ten key molecules during the treatment of fluoxetine in lung cancer cells. Jin Huang et al found the inhibition of CDK1 can regulate the immune resistance in pancreatic cancer³⁵. Er-Bao Chen et al found CCNB1 leads to gastric cancer proliferation and metastasis³⁶. Yu Wang et al found that miR-30a-3p controls MAD2L1 and mediates the proliferation of gastric cancer cells³⁷. Min Nie et al found that AURKB enhances gastric cancer progression via activation of CCND1³⁸. Hui Ma et al found that MCM3 acts as an important prognostic marker in cervical cancer by bioinformatic analysis³⁹. Lin-Lin Zhang et al discovered that MCM5 promotes the HDAC1-controlled malignant development of lung cancer⁴⁰. GPCR-related signaling pathways are key for a number of physiological and pathophysiological processes including metabolism, inflammation, cancer, and aging^41–52. By analyzing the GPCR signaling, Saber Yari Bostanabad found that β-Arrestins are closely related to CDC45 in the progression of breast cancer⁵³. Zikang He et al also found that CDC45 is related to cervical cancer by analyzing the GEO database⁵⁴. Shujie Cheng et al found that CDC20 is associated with cancer metastasis and CDC20 inhibitors have anti-cancer activity⁵⁵. Jian Zhang et al found RFC4 drives the progression and growth of oral cancer in vivo and vitro⁵⁶. Jintao Cao et al discovered that CCNB2 acts as a prognostic factor in breast cancer⁵⁷.

In conclusion, our study found the significant biological processes and signaling pathways in the fluoxetine treated lung cancer cells. The "Lysosome" and "Autophagy" are the top two affected signaling pathways. Our study may help to guide the treatment of lung cancer by using fluoxetine.

Author Contributions

Tingting Zhang, Guofang Zhao, Tingxiang Chang: Methodology and Writing. Mengshan Wang,Donghong Zhang, Andrew Chung: Conceptualization and writing.

Funding

This work was not supported by any funding.

Declarations of interest

There is no conflict of interest to declare.

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Tables 1-2 are available in the Supplementary Files section.

SupplementalTableS1.xlsx
Supplemental Table S1
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Table 1
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Table 2

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Identification of significant molecules and biological processes in fluoxetine treated lung cancer cells

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Abstract

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Introduction

Methods

Results

Identification of DEGs in lung cancer cells with the treatment of fluoxetine

Discussion

Declarations

References

Tables 1-2

Supplementary Files

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