Identification of Hub Genes and Small Molecule Drugs Associated with Acquired Resistance to Gefitinib in Non-Small Cell Lung Cancer

S100A8 and S100A9 in Cancer

S100A8 and S100A9 are Ca²⁺ binding proteins that belong to the S100 family. Primarily expressed in neutrophils and monocytes, S100A8 and S100A9 play critical roles in modulating various inflammatory responses and inflammation-associated diseases. Forming a common heterodimer structure S100A8/A9, S100A8 and S100A9 are widely reported to participate in multiple signaling pathways in tumor cells. Meanwhile, S100A8/A9, S100A8, and S100A9, mainly as promoters, contribute to tumor development, growth and metastasis by interfering with tumor metabolism and the microenvironment. In recent years, the potential of S100A8/A9, S100A9, and S100A8 as tumor diagnostic or prognostic biomarkers has also been demonstrated. In addition, an increasing number of potential therapies targeting S100A8/A9 and related signaling pathways have emerged. In this review, we will first expound on the characteristics of S100A8/A9, S100A9, and S100A8 in-depth, focus on their interactions with tumor cells and microenvironments, and then discuss their clinical applications as biomarkers and therapeutic targets. We also highlight current limitations and look into the future of S100A8/A9 targeted anti-cancer therapy.

Identification of crucial lncRNAs and mRNAs in liver regeneration after portal vein ligation through weighted gene correlation network analysis

BACKGROUND

Portal vein ligation (PVL)-induced liver hypertrophy increases future liver remnant (FLR) volume and improves resectability of large hepatic carcinoma. However, the molecular mechanism by which PVL facilitates liver hypertrophy remains poorly understood.

METHODS

To gain mechanistic insight, we established a rat PVL model and carried out a comprehensive transcriptome analyses of hepatic lobes preserving portal blood supply at 0, 1, 7, and 14-day after PVL. The differentially expressed (DE) long-non coding RNAs (lncRNAs) and mRNAs were applied to conduct weighted gene co-expression network analysis (WGCNA). LncRNA-mRNA co-expression network was constructed in the most significant module. The modules and genes associated with PVL-induced liver hypertrophy were assessed through quantitative real-time PCR.

RESULTS

A total of 4213 DElncRNAs and 6809 DEmRNAs probesets, identified by transcriptome analyses, were used to carry out WGCNA, by which 10 modules were generated. The largest and most significant module (marked in black_M6) was selected for further analysis. Gene Ontology (GO) analysis of the module exhibited several key biological processes associated with liver regeneration such as complement activation, IL-6 production, Wnt signaling pathway, autophagy, etc. Sixteen mRNAs (Notch1, Grb2, IL-4, Cops4, Stxbp1, Khdrbs2, Hdac2, Gnb3, Gng10, Tlr2, Sod1, Gosr2, Rbbp5, Map3k3, Golga2, and Rev3l) and ten lncRNAs (BC092620, AB190508, EF076772, BC088302, BC158675, BC100646, BC089934, L20987, BC091187, and M23890) were identified as hub genes in accordance with gene significance value, module membership value, protein-protein interaction (PPI) and lncRNA-mRNA co-expression network. Furthermore, the overexpression of 3 mRNAs (Notch1, Grb2 and IL-4) and 4 lncRNAs (BC089934, EF076772, BC092620, and BC088302) was validated in hypertrophic liver lobe tissues from PVL rats and patients undergoing hepatectomy after portal vein embolization (PVE).

CONCLUSIONS

Microarray and WGCNA analysis revealed that the 3 mRNAs (Notch1, Grb2 and IL-4) and the 4 lncRNAs (BC089934, EF076772, BC092620 and BC088302) may be promising targets for accelerating liver regeneration before extensive hepatectomy.

Identification of Gefitinib Resistance-Related lncRNA-miRNA-mRNA Regulatory Networks and Corresponding Prognostic Signature in Patients with Lung Adenocarcinoma

Purpose

To identify and characterize gefitinib resistance-related (GefR-related) lncRNAs and construct a prediction model for lung adenocarcinoma (LUAD).

Methods

Differential expression analysis between PC9 and gefitinib-resistant PC9 (PC9GR) cell samples was performed to screen GefR-related lncRNAs and mRNAs based on the GSE34228 dataset. These lncRNAs, mRNAs, and their corresponding microRNAs (miRNAs) were used to construct the GefR-related network and PPI networks. Functional enrichment analyses were conducted using the STRING database. A prognostic signature was developed using the TCGA dataset. The reliability of the signature was tested using the Kaplan–Meier method and ROC curve. Lastly, the FZD4-associated ceRNA subnetwork was selected to confirm the in vitro expressions of the GefR-related lncRNAs using RT-qPCR assay.

Results

A GefR-related ceRNA network that consists of 35 miRNAs, 26 lncRNAs, and 179 mRNAs was constructed. Then, 20 hub genes were screened from the targeted mRNAs of the constructed PPI network, and enrichment analysis identified relevant enriched pathways. We also constructed a prognostic signature for LUAD based on nine mRNAs in the GefR-related ceRNA network. The 9-mRNA signature was an independent predictor of LUAD, the AUC produced by ROC analysis showed a good predictive power of the model, and Kaplan–Meier analysis showed poorer outcomes in the high-risk group, relative to the low-risk group. Lastly, MIR137HG and ZNF295-AS1 levels were found to be associated with gefitinib resistance and exerted their functions through the ceRNA mechanism.

Conclusion

We established a prognostic signature and identified two lncRNAs (MIR137HG and ZNF295-AS1) with potential significant roles in gefitinib resistance.

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

FNDC3B and BPGM Are Involved in Human Papillomavirus-Mediated Carcinogenesis of Cervical Cancer

Human papillomavirus (HPV)-mediated cervical carcinogenesis is a multistep progressing from persistent infection, precancerous lesion to cervical cancer (CCa). Although molecular alterations driven by viral oncoproteins are necessary in cervical carcinogenesis, the key regulators behind the multistep process remain not well understood. It is pivotal to identify the key genes involved in the process for early diagnosis and treatment of this disease. Here we analyzed the mRNA expression profiles in cervical samples including normal, cervical intraepithelial neoplasia (CIN), and CCa. A co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to reveal the crucial modules in the dynamic process from HPV infection to CCa development. Furthermore, the differentially expressed genes (DEGs) that could distinguish all stages of progression of CCa were screened. The key genes involved in HPV-CCa were identified. It was found that the genes involved in DNA replication/repair and cell cycle were upregulated in CIN compared with normal control, and sustained in CCa, accompanied by substantial metabolic shifts. We found that upregulated fibronectin type III domain-containing 3B (FNDC3B) and downregulated bisphosphoglycerate mutase (BPGM) could differentiate all stages of CCa progression. In patients with CCa, a higher expression of FNDC3B or lower expression of BPGM was closely correlated with a shorter overall survival (OS) and disease-free survival (DFS). A receiver operating characteristic (ROC) analysis of CIN and CCa showed that FNDC3B had the highest sensitivity and specificity for predicting CCa development. Taken together, the current data showed that FNDC3B and BPGM were key genes involved in HPV-mediated transformation from normal epithelium to precancerous lesions and CCa.

Targeting MERTK and AXL in EGFR Mutant Non-Small Cell Lung Cancer

MERTK and AXL are members of the TAM family of receptor tyrosine kinases and are abnormally expressed in 69% and 93% of non-small cell lung cancers (NSCLCs), respectively. Expression of MERTK and/or AXL provides a survival advantage for NSCLC cells and correlates with lymph node metastasis, drug resistance, and disease progression in patients with NSCLC. The TAM receptors on host tumor infiltrating cells also play important roles in the immunosuppressive tumor microenvironment. Thus, MERTK and AXL are attractive biologic targets for NSCLC treatment. Here, we will review physiologic and oncologic roles for MERTK and AXL with an emphasis on the potential to target these kinases in NSCLCs with activating EGFR mutations.

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Purpose

Lung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy.

Methods

The drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms.

Results

In this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference.

Conclusion

In this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.