Screening of the siGPCR library in combination with cisplatin against lung cancer

Cell culture conditions

Non-small cell lung cancer cell lines used in the experiments included A549, EKVX (National Institutes of Health, National Cancer Institute, Frederick, MD, USA), H1793 and H1437 (Korean Cell Line Bank, Korean Cell Line Research Foundation, Seoul, Korea). Cell lines were maintained in RPMI 1640 medium containing 10% fetal bovine serum (HyClone Laboratories, Logan, UT, USA) and 1% penicillin. For H1437, RPMI 1640, HEPES (GIBCO Laboratories, Grand Island, New York) was used. Cells were cultured in a humidified atmosphere at 5% CO2 and 95% air at 37°C, and the culture medium was renewed every two to three days.

High throughput siRNA screening

The siRNA screening was performed using four On-Target Plus siRNAs pooled to target each of the 390 genes in the human GPCR siRNA library. Screening was performed in triplicate for four days in a 384-well plate format (400 cells per well). Each plate was provided with a negative control siRNA (siNC; GE Dharmacon) and a positive control siRNA (PLK1; GE Dharmacon) to distinguish sequence-specific silencing from non-specific efficacy and knockdown effects. Reverse transfection was performed using a MultiFlo microplate dispenser (BioTek Instruments) with siRNAs (10 nM final concentration) and RNAiMAX Lipofectamine (Thermo Fisher Scientific, Waltham, MA, USA; 0.05 µl per well) diluted Opti-MEM (Thermo Fisher Scientific; 15 µl per well) in a black 384-well plate (Corning, Corning, NY, USA). After 24 h, each pool was treated with 0.1% dimethylformamide (DMF) and cisplatin (final concentration 1 µM). Cisplatin was obtained from Abmole Bioscience and dissolved in 100% DMF to a final concentration of 50 µM. Aliquots were stored at -20°C and thawed and diluted (1 µM) immediately before use. After 72 h, cells were assayed using the CellTiter-Blue Cell Viability Assay kit (Promega, Madison, WI, USA) according to the manufacturer’s protocol, and the fluorescence produced was proportional to the number of viable cells. The plates were read on a SYNERGY H1 microplate reader (BioTek Instruments). After cell viability assay, cells were stained with Hoechst 33342 (Sigma–Aldrich, St. Louis, MO, USA) and plates were imaged on a CYTATION3 imaging reader (BioTek Instruments) in 2× mount mode 2.

Analysis of filter data and selection of results

For the quality control metric in siRNA screening, the Z’ factor is used, defined as follows16:

$${text{z}}^{^{prime}} {text{ – factor}} = {1}{-}{3}left( {{text{SD}}_{{{ text{siPLK1}}}} {-}{text{SD}}_{{{text{siNC}}}} } right)/left| {{text{AVG}}_{{{text{siPLK1}}}} {-}{text{AVG}}_{{{text{siNC}}}} } right|,$$

where SD is for standard deviation and AVG is for mean.

The range of the z’ factor is from negative infinity to one, with >0.5 indicating excellent dosing and >0 borderline dosing.

The raw data was converted to log2 scale to calculate the inhibitory effect of each siRNA: cell number and cell viability compared to negative controls per plate. After normalization, negative values ​​indicate that the target gene treatment has lower cell counts than negative controls. All data were on a log2 scale, unless otherwise specified. Statistical significance was calculated by independent 2-sample Student’s t-test. Inhibitory siRNA hits were selected by combination of cisplatin and siGPCR transfection. Cisplatin-sensitive hits were selected with a p value

Hit gene validation experiments

Validation was performed to identify false positives. SiRNAs from On-Target Plus siRNAs (siADRA2A, siEMR3, siF2RL3, siGPR108, siNPSR1, siNPY and siTACR3) were rescreened with A549 using the same 10 nM reverse transfection protocol using Lipofectamine RNAi Max and Opti-MEM with DMF and 1 µM cisplatin concentration according to manufacturer’s protocols. Repeated experiments were carried out twice. Another validation experiment was conducted on five siRNAs (siEMR3, siF2RL3, siGPR108, siNPSR1 and siTACR3) from siTOOLs Biotech with A549, EKVX, H1437, H1793. The siRNAs were transfected (final concentration 4 nM) by reverse transfection using Lipofectamine RNAi Max and Opti-MEM and a concentration of 1 μM cisplatin with DMF. The experiment was repeated four times. For the anticancer effect, the % growth inhibition was defined as follows:

$$% Growth inhibition= frac{cell countleft(treatment, 96hright)-cell count(0h)}{cell count(control, 96h)-cell count(0h)} times 100$$

Self-renewal experience

A self-renewal experiment was conducted to identify how the pretreated chemical may affect the regeneration and growth of cancer cell lines even though it was replaced with fresh medium. We seeded the A549 cell line in a 96-well plate (Thermo Fisher) (2500 cells per well) for pretreatment. Transfection of siNC and siGPCR was performed. After 24 h, cells were treated with 1 μM cisplatin and DMF to identify four conditions (siNC-DMF, siNC-cisplatin, siGPCR-DMF and siGPCR-cisplatin). After a total of 96 h, the cells were harvested and seeded in 384-well plates by seeding number (40, 80, 120, 200, 400, 800, 1200 and 2000 cells per well) in fresh medium. After 96 h, cells were assayed using Hoechst 33342 (Sigma–Aldrich) to count cells.

siRNA transfection of cisplatin-pretreated A549 cells

For transfection of cisplatin-pretreated A549, we performed the same protocol with the A549 cell line prepared in advance. Cisplatin (1 µM) in the culture medium was added to prepare cisplatin-pretreated A549 cells. We normalized the percent cell count value for siNC. The experiment was conducted with three repetitions.

Survival analysis

RNA sequencing data, mutation data and clinical data were obtained from the Cancer Genome Atlas (TCGA) GDC data portal23. RNA sequencing data in FPKM (Fragments Per Kilobase Million) were converted to log2 (TPM+1) (Transcripts Per Kilobase Million). Both FPKM and TPM indicate the expression levels of the RNA transcript. TPM was introduced as a measure to correct for inconsistencies between independent samples24. For the calculation of the survival analysis, the following patient details were obtained: vital status, sex, days to death, days to last follow-up, and days to new tumor event after initial treatment at from clinical data.

Genes were analyzed using the Kaplan-Meier method25 and the log-rank test26 via R. Gender (female, male, and both) and stage (I, II, III, and IV) were divided by TCGA clinical data. Patients were divided into high-expression and low-expression groups based on the median and quartile (high group > 25% upper, low group 26 was played.

Prognostic expression of RNA

Survival analysis was calculated for 48 different measurements using the TCGA dataset. Patients were divided according to clinical data, including gender (female, male, and both) and stage (I, II, III, and IV). Gene expression groups were divided into medians or quartiles and calculated based on OS and DFS. Among the 48 survival p values ​​thus calculated, the best p value was retained. We analyzed the expression of favorable and unfavorable prognostic genes27. Favorable prognostic gene expression was associated with lower survival rate in the low gene expression group, and unfavorable prognostic gene expression was associated with lower survival rate in the high gene expression group.

Gene Ontology Database

We used the term GO (Gene Ontology) to know which function each group of genes is associated with. Among them, we focused on the term biological process. The GO database was obtained from msigdb (


Oncoprint was used to display mutation information for LUAD (Lung Adenocarcinoma) patients whose mutation information was obtained from TCGA. Data processing was done using R.

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