Reference gene evaluation for normalization of gene expression studies with lymph tissue and node‑derived stromal cells of patients with oral squamous cell carcinoma

Abstract

Profiling studies using reverse transcription quantitative PCR (RT-qPCR) require reliable normalization to reference genes to accurately interpret the results. A stable reference gene panel was established to profile metastatic and non-metastatic lymph nodes in patients with oral squamous cell carcinoma. The stability of 18S ribosomal RNA (18SrRNA), ribosomal Protein Lateral Stalk Subunit P0 (RPLP0), ribosomal Protein L27 (RPL27), TATA-box binding protein (TBP), hypoxanthine phosphoribosyl-transferase 1 (HPRT1), beta-actin (ACTB), glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) and vimentin (VIM) was evaluated, as reference genes for profiling patient-derived lymph node stromal cells (LNSCs; N=8; N0:6, N+:2) and lymph node tissues (Patients:14, Nodes=20; N0:7; N+:13). The genes were initially assessed based on their expression levels, specificity, and stability rankings to identify the best combination of reference genes. VIM was excluded from the final analysis because of its low expression (high quantification cycle >32) and multiple peaks in the melting curve. The stability analysis was performed using Reffinder, which utilizes four tools; geNorm, NormFinder, BestKeeper and Comparative ∆Ct methods, thereby enabling the computing of a comprehensive ranking. Evaluation of the gene profiles indicated that while RPLP0 and 18SrRNA were stable in both lymph node tissues and LNSCs, HPRT1, RPL27 were uniquely stable in these tissues whereas ACTB and TBP were most stable in LNSCs. The present study identified the most stable reference gene panel for the RT-qPCR profiling of lymph node tissues and patient-derived LNSCs. The observation that the gene panel differed between the two model systems further emphasized the need to evaluate the reference gene subset based on the disease and cellular context.

Introduction

Biomarker profiling is a major component of oncological research that enables the integration of molecular patterns with the clinicopathological status of patients. Reverse transcription-quantitative PCR (RT-qPCR), a valuable technique to profile differential expression, helps understand the molecular patterns in a disease condition, enabling biomarker development. An accurate and economical technique, it is commonly used in the field of molecular oncology. Based on the technology that provides real-time quantifiable levels of biomarkers, it is extremely important to use appropriate controls as the baseline for inferring the results in RT-qPCR. Reference genes served as internal controls to normalize the quantification cycle (Cq) in RT-qPCR, enabling the accurate assessment of biomarker levels in the analyte. Hence, it is extremely important to identify appropriate and reliable reference genes (1,2). In addition to demonstrating minimum variability under various physiological and disease conditions, a reliable reference gene must be unaffected by experimental conditions. The genes commonly used as reference genes are housekeeping genes that are required for the normal functioning of cells and are hence expected to be the least variable across tissues/conditions. Given the possible heterogeneity in the expression profiles of housekeeping genes, their utilization in a particular cell/tissue type, disease condition and organism must be evaluated. The Minimum Information for publication of Quantitative Real-Time PCR Experiments guidelines, a set of guidelines necessary for evaluating RT-qPCR experiments, mandates the evaluation of reference genes suitable for a particular study type (3).

Oral squamous cell carcinoma (OSCC), with a worldwide incidence of 389,846 individuals, is the second most common cancer in the Indian sub-continent (4,5). Given the challenges in staging the disease at diagnosis and improving survival rates, biomarkers are a significant adjunct for early diagnosis, cancer progression, relapse and prognosis, with RT-qPCR being the most commonly used method for biomarker profiling (6–9). Lymph node metastasis (LNM) is a critical prognostic factor in OSCC and reduces survival by 50% (10). Studies on the diagnosis, intraoperative detection and inhibition of LNM routinely employ RT-qPCR to document expression patterns, with housekeeping genes serving as reference genes (11,12). Previous studies on lymph nodes using RT-qPCR have mostly been conducted in mouse models of non-cancerous conditions. In most studies, normalization was performed using a single reference gene. Although the utilization of multiple reference genes can improve the resolution, interpretation and accuracy of the results, studies investigating the comparative accuracy of these reference genes for the accurate assessment of the expression profile in lymph nodes and/or stromal cells derived from patients are lacking.

The present study aimed to address this gap and identify appropriate reference genes that can be applied for biomarker profiling in lymph node stromal cells and tissues derived from patients with oral cancer. Based on literature review, the known reference genes, 18S ribosomal RNA (18SrRNA), ribosomal Protein Lateral Stalk Subunit P0 (RPLP0), ribosomal Protein L27 (RPL27), TATA-box binding protein (TBP), hypoxanthine phosphoribosyl-transferase 1 (HPRT1), beta-actin (ACTB), glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) and vimentin (VIM) were evaluated for their variability and applicability as reference genes in lymph node cells/tissues from patients with OSCC.

Materials and methods

Patient selection and cell lines for evaluation

The present study was approved [approval no. NHH/MEC-CL-EL-6-2016-403(A-1)] by the Narayana Health Medical Ethics Committee (Bangalore, India). A total of 14 patients with treatment-naive OSCC who underwent neck dissection and gave written consent to participate in the present study, were included (August. 2017-September, 2023). The mean age of the patients was 50 years (SD, 14.75) and 35% (5/14) were females. Patients <18 years of age and diagnosed with HIV/HBV/HCV were excluded from the present study. Lymph node tissue samples were collected under the supervision of surgeons and pathologists to obtain accurate specimens without affecting the patient's diagnosis. The surgical lymph node specimens identified by the surgeon were then evaluated by a pathologist to ensure the metastatic status of the specimen and then split into portions for histopathological evaluation. Primary cultures of lymph node stromal cells (LNSCs) from lymph node tissues previously established [approved by the Narayana Health Medical Ethics Committee; approval no. NHH/MEC-CL-EL-6-2016-403(A-1)] in the laboratory were also used in the present study. LNSCs (passage <15) were cultured in high-glucose Dulbecco's Minimum Essential Medium (DMEM-HG; cat. no. AL007A; HiMedia) to 80–90% confluency for extraction, whereas lymph node tissues were stored in RNAlater solution (cat. no. AM7021; Ambion; Thermo Fisher Scientific, Inc.) at −80°C until extraction.

RNA isolation, cDNA conversion and RT-qPCR

The cells and tissues were lysed and RNA was eluted from the column according to the manufacturer's instructions (cat. no. 740933; Machery-Nagel GmbH). RNA was assessed using a Nanodrop to measure yield and purity (A₂₆₀/A₂₈₀ ratio >1.8; RNA integrity by electrophoresis). For cDNA conversion, 1,000 ng of total RNA was converted using high-capacity cDNA reverse transcription Kit (Applied Biosystems^™; cat. no. 4374966; Thermo Fisher Scientific, Inc.) in a 40 µl reaction mixture. All reagents were thawed and the reaction mixture was prepared on ice. The reaction was setup as per the manufacturer's protocol (Step 1: 25°C for 10 min, Step 2: 37°C for 120 min, Step 3: 85°C for 5 min and Step 4: hold at 4°C). RT-qPCR was performed using Kapa SYBR Fast (cat. no. KK4601; Kapa Biosystems; Roche Diagnostics) on a Roche Light Cycler 480 II Real-Time PCR machine. Reactions were performed in triplicate for 45 cycles using primers specific to each gene (Table I).

Table I.

Primer sequence and efficiency.

S. No.	Primer (Accession No.)	Forward/Reverse	Sequence	Product length	Amplification factor	Efficiency (%)
1	ACTB (NM_001101.5)	Forward	TCAAGATCATTGCTCCTCCTG	101	2.01	101
		Reverse	CTGCTTGCTGATCCACATCTG
2	HPRT1 (NM_000194.3)	Forward	ATGAACCAGGTTATGACCTTGAT	298	2.05	104.55
		Reverse	CCTGTTGACTGGTCATTACAATA
3	RPLP0 (NM_001002.4)	Forward	CCATTCTATCATCAACGGGTACAA	75	2.01	100.53
		Reverse	TCAGCAAGTGGGAAGGTGTAATC
4	18SrRNA (NM_022551.3)	Forward	GAGGATGAGGTGGAACGTGT	199	2.02	101.55
		Reverse	AGAAGTGACGCAGCCCTCTA
5	GAPDH (NM_002046.7)	Forward	TCGACAGTCAGCCGCATCTTCTTT	104	1.93	93.07
		Reverse	GCCCAATACGACCAAATCCGTTGA
6	RPL27 (NM_000988.5)	Forward	ACAATCACCTAATGCCCACA	146	1.95	95.27
		Reverse	GCCTGTCTTGTATCTCTCTTCAA
7	VIM (NM_003380.5)	Forward	AGGCAAAGCAGGAGTCCACTGA	100	2.11	110.68
		Reverse	ATCTGGCGTTCCAGGGACTCAT
8	TBP (NM_003194.5)	Forward	CCACTCACAGACTCTCACAAC	127	1.96	96
		Reverse	CTGCGGTACAATCCCAGAACT

ACTB, beta-actin; HPRT1, hypoxanthine phosphoribosyl-transferase 1; RPLP0, ribosomal protein lateral stalk subunit P0; 18SrRNA, 18S ribosomal RNA; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; RPL27, ribosomal protein L27; VIM, vimentin; TBP, TATA-box binding protein.

Primer efficiency

The efficiency of the primers specific for ACTB, RPL27 and HPRT1 was evaluated. cDNA samples were serially diluted (1:5 dilution) and RT-qPCR was performed using seven dilutions. Average quantification cycle (Cq) values from triplicate experiments were plotted against log₁₀ (concentration). The slope of the regression line was used to determine the amplification factor and efficiency using an online tool from Thermo Fisher Scientific (qPCR Efficiency Calculator | Thermo Fisher Scientific; https://www.thermofisher.com/uk/en/home/brands/thermo-scientific/molecular-biology/molecular-biology-learning-center/molecular-biology-resource-library/thermo-scientific-web-tools/qpcr-efficiency-calculator.html). The primer efficiencies for VIM, TBP, GAPDH, RPLP0 and 18SrRNA were previously established in the laboratory.

Statistical analysis

Reference genes were assessed for amplicon nature, melting temperature (Tm), expression range (Cq values) and stability. The mean Cq was determined from triplicate measurements for each sample. The mean Cq values across LNSCs/lymph node tissues are presented as mean ± standard deviation (SD). The graphs were plotted using Tableau Professional Edition (2022.2.0; Salesforce, Inc.) and Microsoft Excel (Microsoft Corporation).

Stability analysis of the reference genes

For stability analysis, the Cq values for these reference genes were evaluated using the Reffinder tool (http://blooge.cn/RefFinder/), which analyzed the data using multiple normalization methods including geNorm, NormFinder, BestKeeper and the comparative ∆Ct methods. A comprehensive ranking of different reference gene candidates was obtained based on these four methods (13,14).

Results

Details of the patients and the LNSCs

Lymph node tissues (N=20) were collected from 14 patients with treatment-naïve OSCC after obtaining written informed consent. The mean age of the patients was 50 years (SD: 14.75) and 35% (5/14) were females (Table II). Most patients (64.28%) chewed tobacco, smoked and consumed alcohol. The patients were mostly diagnosed with tongue (57.14%) and buccal mucosa (28.57%) tumors, with 35.71% having T1-T2 stage tumors and 64.28% having T3-T4 stage tumors. The patients were further distributed based on the status of nodal metastasis; 57.14% of patients were diagnosed with nodal metastasis (N+ stage). Primary LNSCs (n=8) from five patients with OSCC were assessed in the present study. The average age of the patients was 57 years (SD=7.92), with four out of five patients having smoking/tobacco chewing risk habits.

Table II.

Patient's demographics for lymph node tissues (N=14 patients).

S. No.	Patient code	Sample code	Age	Sex	Tumor site	Pathologic T stage	Risk habits
1	P1	S1	67	Male	Alveolus	T4bN2b	Tobacco chewing, alcohol
2	P2	S2	39	Male	Tongue	T3N1	Tobacco chewing
3		S3	39	Male	Tongue	T3N1	Tobacco chewing
4	P3	S4	47	Female	Tongue	T1N3b	No habits
5		S5	47	Female	Tongue	T1N3b	No habits
6	P4	S6	75	Male	Tongue	T3N3b	Smoking, tobacco chewing
7		S7	75	Male	Tongue	T3N3b	Smoking, tobacco chewing
8	P5	S8	30	Male	Retro molar trigone	T4bN3b	Smoking
9		S9	30	Male	Retro molar trigone	T4bN3b	Smoking
10	P6	S10	51	Male	Tongue	T1N0	Smoking, tobacco chewing
11	P7	S11	39	Male	Tongue	T2N0	Tobacco chewing
12	P8	S12	54	Male	Tongue	T1N0	No habits
13	P9	S13	61	Male	Buccal Mucosa	T3N0	Smoking, tobacco chewing
14	P10	S14	48	Female	Buccal Mucosa	T3N3b	Tobacco chewing
15		S15	48	Female	Buccal Mucosa	T3N3b	Tobacco chewing
16	P11	S16	50	Female	Buccal Mucosa	T4aN3b	Tobacco chewing
17	P12	S17	21	Female	Tongue	T3N0	No habits
18	P13	S18	72	Female	Tongue	T3N3b	No habits
19	P14	S19	46	Male	Buccal Mucosa	T1N0	No habits
20		S20	46	Male	Buccal Mucosa	T1N0	No habits

Evaluation of expression levels (quantification cycle) and specificity (melting curve)

Assessment of primer efficiency (Table I) indicated that the primers had efficiencies that ranged from 93.07 to 110.68% with an amplification factor of 1.93–2.11.

Reference genes were assessed based on their expression levels (Cq values) and amplicon nature (melting curves) across different cohorts. Comparison of the Cq values indicated that lymph node tissues had lower expression of GAPDH, TBP and HPRT1 (Cq range: ~28-33, Fig. 1A) and higher expression of 18SrRNA, RPLP0, ACTB, RPL27 and VIM (Cq range: ~18.5–26, Fig. 1B). Similarly, LNSCs revealed lower expression of GAPDH, TBP, RPL27 and HPRT1 (Cq range: ~24.5–32, Fig. 1C) and higher expression of 18SrRNA, RPLP0, ACTB and VIM (Cq range: ~17.4–24.5, Fig. 1D). The Cq values, when plotted between the metastatic (N+) and non-metastatic (N0) patient groups for each primer, demonstrated no significant differences between the cohorts (Fig. 1) for lymph node tissues and LNSCs. Furthermore, evaluation of the melting curves of the amplicons across all the samples indicated that the amplified products of VIM demonstrated multiple peaks, indicating more than one product (Fig. S1). VIM was excluded from further analysis.

Figure 1.

Distribution of Cq values in lymph node tissues and LNSCs. (A) A box and whisker plot depicting the Cq distribution in lymph node tissues demonstrated comparatively lower expression for GAPDH, TBP and HPRT1. (B) Higher expression for RPL27, ACTB, 18SrRNA, RPLP0 and VIM. (C) The scatter plot for Cq values for the reference gene with LNSCs revealed varied distribution. (D) 18SrRNA, ACTB, RPLP0 & VIM demonstrated Cq ranging between 17–24. The box represents the interquartile range and the whiskers represent the minimum and maximum Cq values. LNSCs, lymph node stromal cells; TBP, TATA-box binding protein; HPRT1, hypoxanthine phosphoribosyl-transferase 1; RPL27, ribosomal protein L27; ACTB, beta-actin; 18SrRNA, 18S ribosomal RNA; RPLP0, ribosomal protein lateral stalk subunit P0; VIM, vimentin; N+, metastatic; N0, non-metastatic.

Stability analysis of reference genes with lymph node tissues

The expression analysis of the genes with the lymph node tissues revealed that the highest expression was observed in 18SrRNA, RPLP0, ACTB and RPL27 with Cq ranging between 18.5 to 24 (Fig. 1A and B, Table SI). The stability of these genes across the 20 samples was further evaluated using Reffinder (Fig. 2A-E, Table SII). The Comparative ∆Ct, geNorm and NormFinder methods identified RPLP0, HPRT1, RPL27 and 18SrRNA as the most stable genes across the lymph nodes irrespective of their metastatic status. All four methods identified TBP and GAPDH as the least stable genes (Table SII). A comprehensive ranking combining all methods identified RPLP0, HPRT1, RPL27 and 18SrRNA as the most stable genes for RT-qPCR profiling in lymph node tissues (Fig. 2E).

Figure 2.

Ranking for reference genes for lymph node tissues. (A-D) The analysis of reference gene stability with four different methods, namely (A) comparative ∆Ct, (B) NormFinder, (C) geNorm and (D) BestKeeper are demonstrated. The stability analysis with comparative ∆Ct, geNorm and NormFinder identified RPLP0 and HPRT1 as the most stable genes except BestKeeper which identified RPL27 and RPLP0 as the most stable. (E) The comprehensive ranking analysis identifies RPLP0, HPRT1, RPL27 and 18SrRNA as the most stable genes, appropriate to be used as reference genes. RPLP0, ribosomal protein lateral stalk subunit P0; HPRT1, hypoxanthine phosphoribosyl-transferase 1; RPL27, ribosomal protein L27; 18SrRNA, 18S ribosomal RNA; ACTB, beta-actin.

Evaluation of reference genes stability with LNSCs

The RT-qPCR analysis with the lymph node cells revealed that ACTB had the highest expression with Cq ranging between 17 to 20 followed by RPLP0 and 18SrRNA (Cq ranging between ~21.5 to 24), whereas Cq values of GAPDH, TBP, RPL27 and HPRT1 ranged between 24 to 30 (Fig. 1C and D, Table SIII).

The Cq values were further analyzed for expression stability using Reffinder (Fig. 3A-E, Table SIV). The comparative ∆Ct, NormFinder and geNorm methods identified ACTB, 18SrRNA and RPLP0 as the most stable genes and GAPDH and RPL27 as the least stable genes. BestKeeper identified ACTB, TBP and RPLP0 as the most stable genes (Table SIV). Comprehensive ranking using Reffinder identified 18SrRNA, RPLP0, ACTB and TBP as the four most stable genes for LNSC expression (Fig. 3E).

Figure 3.

Ranking for reference genes for LNSCs. The analysis was carried out with eight LNSCs developed in-house. (A-E) The comprehensive analysis with (A) comparative ∆Ct, (B) NormFinder, (C) geNorm, and (D) BestKeeper methods ranked (E) 18SrRNA, RPLP0, ACTB and TBP as the most stable genes that can serve as appropriate reference genes for LNSCs. LNSCs, lymph node stromal cells; 18SrRNA, 18S ribosomal RNA; RPLP0, ribosomal protein lateral stalk subunit P0; ACTB, beta-actin; TBP, TATA-box binding protein; HPRT1, hypoxanthine phosphoribosyl-transferase 1; RPL27, ribosomal protein L27; TBP, TATA-box binding protein.

Discussion

Carcinogenesis is a complex process involving various pathways composed of distinct molecular markers. Delineating these complex pathways, identifying markers that specify these processes and are clinically relevant is challenging. Oral cancer, with high incidence (3,89,846) and mortality (1,88,438) worldwide (4), has added heterogeneity owing to the site, mode of metastasis and differential response to treatment. The incidence and mortality of cancers of lip and oral cavity rank 16 and 15th respectively among the top 32 cancers worldwide. LNM, the most common pattern of metastasis, notably affects the prognosis of oral OSCC, reducing five-year survival rates to 30–59% (15–17). LNSCs, which form the major component of lymph nodes, play a crucial role in tumor-stromal interactions (18–24). Furthermore, metastasis, a process central to the prognostic outcomes in patients, is extremely complex with multiple cellular (tumor cells, stromal cells, extra-cellular vesicles) and molecular players including ncRNAs/miRNAs (25–28). A comprehensive mechanistic and an accurate understanding of the underlying molecular patterns and representative biomarkers is crucial. Molecular profiling employing lymph node tissues and cells has been the focus of numerous studies. Expression profiling at the transcript level is a major strategy, and RT-qPCR is an easy, accurate and quantitative tool for targeted expression profiling. Adequate normalization using reference genes is the key to obtaining reliable RT-qPCR results. In the present study, eight commonly used reference genes were evaluated and selected from the literature for their relevance as reference genes in the RT-qPCR-based profiling of lymph node tissues and cultured LNSCs.

Housekeeping genes are usually the chosen subset of reference genes; however, multiple studies have identified inaccurate usage of some of these reference genes, further emphasizing the need for the validation of reference genes in specific tissues, cells, diseases and experimental conditions (2,29–31). Given the cellular and tissue-level heterogeneity in samples, established guidelines recommend the validation of genes within the context of the tissue type and experimental design. In the present study, it was revealed that in lymph node tissues RPLP0, HPRT1, RPL27 and 18SrRNA were the stable genes. These genes remained stable in lymph node tissues, regardless of metastatic or non-metastatic nodes, and their expression was not affected by presence or absence of tumor cells. Multiple studies have utilized RT-qPCR profiling to study various aspects of lymph node organogenesis in mouse tissues; however, normalization was performed using one housekeeping gene (GAPDH, ACTB, TBP) (32–36). Studies on human lymph node tissues have been comparatively fewer. HPRT1 and TBP have been identified as reliable reference genes for molecular profiling in metastatic and non-metastatic pelvic lymph node tissues from patients with prostate cancer (37).

In the present study, reference genes were assessed in two cohorts of patient-derived LNSCs and lymph node tissues. LNSCs comprised multiple cell types, including fibroblastic reticular cells and double-negative cells (20,38). Cells derived from both metastatic/non-metastatic nodes were evaluated; 18SrRNA, RPLP0, ACTB and TBP were the most stable genes for expression profiling in nodal stromal cells. A study on LNSCs of patients with rheumatoid arthritis revealed that the metabolic landscape, including genes involved in glucose, fatty acid and glutamine metabolism, was altered in patients with established disease, as well as in high-risk individuals. Normalization of the target genes was performed using the geometric mean of RPLP0 and POLR2G as reference genes (39). There are few RT-qPCR studies of LNSCs from patients with cancer (40); however, an evaluation of the best combination of reference genes has not been performed. Given the cellular heterogeneity and the effect of tumor cell cues, identification of appropriate reference genes is crucial, and to the best of the authors' knowledge, the present study is the first study reporting the same in patient-derived LNSCs.

In the present study, gene stability revealed a differential pattern in the two cohorts, re-emphasizing the inherent heterogeneity and need for context-dependent reference gene evaluation. The two common genes that were stable in cultured LNSCs as well as in lymph node tissues were 18SrRNA and the ribosomal protein RPLP0, which corroborates with other studies wherein the ribosomal family of proteins has been efficient as reference genes (41–45). In tissues, RPLP0, HPRT1, RPL27 and 18SrRNA were the most stable genes; however, the results further indicated differences in expression levels. While HPRT1 demonstrated a low expression (31±0.95), RPLP0 (22.5±0.8), 18SrRNA (21.6±0.97) and RPL27 (20.2±0.75), had a higher expression level indicating that they are more suitable as reference genes. The cultured LNSCs further revealed that in addition to 18SrRNA (22.5±0.83) and RPLP0 (22.7±0.73), ACTB (18.6±0.62) and TBP (30.0±0.67) were the stable genes; TBP being unsuitable considering the low level of expression. Thus, the present study indicated that, in addition to stability, the expression levels of the chosen reference should also be considered during selection.

Among these genes, HPRT1 and TBP demonstrated an inverse pattern of stability between stromal cells and nodal tissue. This may be due to inherent differences in cell types, considering that lymph node tissues are composed of multiple types of LNSCs, lymphocytes, macrophages, dendritic cells and endothelial cells. The use of HPRT1 as a candidate reference gene has provided contradictory evidence. Studies on meningioma and melanoma have determined HPRT1 as the most stable housekeeping gene under experimental conditions (46,47). However, another study identified HPRT1 unsuitable as a reference gene for cancer-related studies and proposed its discontinuation, especially in the context of tumor-normal tissue comparisons (30). A review of HPRT1 further described its changing role from being a regulator of nucleotide synthesis in normal cells to becoming an accessory in cancer cells by helping them bypass nucleotide synthesis (48). In the present study, although HPRT1 was stably expressed in the lymph node tissues of patients with OSCC, its stability was poor in LNSCs. Furthermore, its low expression in tissues precludes its candidature as a reference gene. Similar to HPRT1, the evidence for TBP, another commonly used reference gene, contradicts studies on its stability and utility as a reference gene in various cancer cell lines (49–51). In the present study, although TBP expression was stable in LNSCs, it revealed lower expression range that differed from that of other stable genes. Hence, the decision to use HPRT1 or TBP as reference genes should be carefully evaluated, depending on the experimental design and specific cancer type under study. Furthermore, the present study provides strong evidence towards the need to evaluate the reference gene while comparing parent tissue and derived primary cells.

GAPDH was the least stable gene in both the lymph node tissues and cultured LNSCs. GAPDH is the most popular and accepted reference standard for RT-qPCR assays (52–55). However, multiple studies have questioned the use of GAPDH as a reference gene because of its involvement in cell proliferation, migration and glycolysis, and studies have also reported that the gene is oncogenic (31,56–59). The present study corroborated the finding that GAPDH is unsuitable for the normalization of LNSC and lymph node tissues from patients with OSCC.

Biomarkers for the detection and prediction of nodal metastasis as well as toward delineating the underlying mechanisms of metastatic progression continue to be challenging (25,28,60–62). The present study identified and validated a panel of reference genes that could be used in lymph node stromal cells and tissues. The need for tissue/cell/context-dependent selection of reference genes for RT-qPCR-based expression profiling was further emphasized by the identification of a different panel of genes for the lymph node tissue and node tissue-derived cells. The panel of genes recommended in the present study will be invaluable towards acquiring accurate expression data from lymph nodes and LNSCs of patients with oral cancer.

Funding Statement

Funding: No funding was received.

Availability of data and materials

The data generated in the present study are included in the figures and tables of this article.

Authors' contributions

BLJ, AS and MAK conceptualized the present study. BLJ, SNZ, NBS, VBR, YD, VS, VP, AS and MAK developed methodology. BLJ performed formal analysis. BLJ and AS interpreted the data. BLJ and AS wrote the original draft. BLJ, AS and MAK wrote, reviewed and edited the manuscript. BLJ and AS confirm the authenticity of all the raw data in the study. All the authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

The present study was approved [approval no. NHH/ MEC-CL-EL-6-2016-403(A-1)] by the Narayana Health Medical Ethics Committee (Bangalore, India). Samples were collected after obtaining written informed consent from the patients.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.