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. 2025 May 3;17(9):1558. doi: 10.3390/cancers17091558

Five Cellular Genes as Candidates for Cervical Adenocarcinoma Molecular Markers

Isui Abril García-Montoya 1, Karla Berenice López-Córdova 1, Daniel Marrero-Rodríguez 2, Mauricio Salcedo-Vargas 3, Claudia Lucía Vargas-Requena 1, Angélica Maria Escárcega-Avila 1, Santos Adriana Martel-Estrada 4, Florinda Jiménez-Vega 1,*
Editor: Jacobus van der Velden
PMCID: PMC12071559  PMID: 40361484

Simple Summary

This study describes the search for and evaluation of cervical adenocarcinoma molecular markers in a population of Ciudad Juárez, Chihuahua, México. Bioinformatic analysis of the NCBI database and 161 transcriptomic libraries was performed. The expression of selected genes was analyzed using semi-quantitative RT-PCR in samples from fresh cervical adenocarcinoma and cervical normal tissues. Five genes presented higher amplification frequency with a statistically significant difference, making them possible molecular markers for cervical cancer.

Keywords: cervical adenocarcinoma, molecular markers, MACC1, RARβ, BCL2, HOXC8, E6/E7, genetic expression

Abstract

Background/Aim: Cervical adenocarcinoma associated with Human Papillomavirus (HPV) infection represents 85–90% of all adenocarcinomas that have poor prognostic factors and is an important health public concern. Currently, cervical adenocarcinoma molecular markers are scarce. This study searched databases and the literature regarding candidate genes to find these molecular markers, which were experimentally evaluated in fresh cervical samples. Materials and Methods: Bioinformatic analysis of 161 transcriptomic libraries of cervical tissues with or without lesions from the NCBI database was performed using the Partek Genomics Suite 6.6v software. The selected genes with a p value of >0.05, and 1.5-fold change were considered. A search of molecular marker candidates of cervical lesions that were already published in the literature was performed. To validate the selected genes, total RNA from fresh cervical adenocarcinoma and cervical normal tissues were subjected to RT-PCR experiments; HPV detection was also performed. Results: Initially, twenty-five genes were identified using bioinformatic analysis, and their expression was evaluated. The results showed that the HOXC6, HOXC8, RARβ, ELAVL2, URG4, CISD2, CA9, BCL2, Survivin, MACC1, CDKN2A, and HPV E6/E7 genes were found to be differentially expressed in CC. Among these, RARβ, MACC1, BCL2, HOXC8, and E6/E7/HPV exhibited higher statistical significance for CC samples. Conclusions: This five-gene panel could serve as a novel molecular tool for HPV-associated cervical adenocarcinoma detection.

1. Introduction

Cervical cancer (CC) is one of the main cancer types for women, ranking fourth in incidence and mortality worldwide [1]. Currently, CC is diagnosed histopathologically using cervical smear and colposcopic procedures, but these approaches have the disadvantage of a long waiting period [2]. Moreover, several barriers are highly involved in early CC detection, such as culture, religion, health public services, etc., that make achieving an efficient program result more difficult [3]. Although the Papanicolaou test detects CC and, thus, helps in reducing its incidence, this test has relatively low accuracy and sensitivity [4]. Recent studies suggest that it is necessary to design novel systems or procedures for the actual CC screening programs.

The Federal Drug Administration (FDA) and the Pan American Health Organization (PAHO) [5] have already accepted the Human Papillomavirus (HPV) molecular test as an important test for CC screening. However, HPV is an essential factor but not sufficient for cervical carcinogenesis; a positive test result only indicates the presence of HPV sequences [6].

Squamous cell carcinomas represent 70% of CC cases [7], while the remainder are cervical adenocarcinomas. The prevalence of adenocarcinomas has been increasing over the years; it is currently the most recurrent in women above 30 years of age [8,9]. In addition, 80% of all adenocarcinoma cases are related to HPV types 16 and 18 [10,11].

Recently, the use of molecular markers associated with the distinctive characteristics of cancer has allowed for a more precise diagnosis, and therefore, it has helped to identify the best therapeutic approach to combat the diagnosed cancer type [12]. A molecular genetic marker is a sequence (gene, transcript, protein, metabolite) associated with a disease [13]. Some diagnostic or predictive panels have been developed for detecting different types of cancer [14,15,16]; however, panels for CC are scarce. Thus, the identification of genes differentially expressed in CC will facilitate the development of new diagnostic tools.

Moreover, is necessary to design novel molecular tools for future implementation with better predictive values and the ability to effectively identify women at risk of developing CC. This will reduce the burden of CC globally, especially in low-and middle-income countries [17].

The aim of this study was to identify candidate genes that identified the risk of cervical adenocarcinoma from the NCBI database and already-published papers and to evaluate their in vivo expression in fresh cervical adenocarcinomas.

2. Materials and Methods

2.1. Selection of Candidate Genes

To strengthen the present study’s methodology, bibliographic research and transcriptomic libraries were used to identify genes of interest. For this purpose, the inclusion criteria were as follows: genes that were identified in gene expression studies, genes that exhibited changes in expression throughout cervical carcinogenesis, reported for Cervical Intraepithelial Neoplasia grades 1–3 (CIN1–3) and CC, and genes that were differentially expressed in cancer cells. The exclusion criteria were studies carried out on cell lines and on treated CC patients. CIN2+ or high-grade and CIN3 Squamous Intraepithelial Lesions and in situ carcinomas are considered as high-risk cervical lesions.

For the bibliographic research, the PubMed database was used with the following keywords, cervical cancer, adenocarcinoma, molecular markers, and differential expression, and by applying the above-mentioned inclusion and exclusion criteria. After obtaining a list of related published papers that met the inclusion and exclusion criteria, the genes involved in cellular pathways related to the hallmarks of cancer [18] were selected and analyzed.

2.2. Identification of Candidates from Datasets

The transcriptomic library search for the cervical lesions was carried out with the Array Express database using the following criteria, including Homo sapiens transcriptomes, RNA assays, cervical tissues, and different stages of the carcinogenic process, as well as the Affymetrix Human matrix gene Chip U133. The obtained libraries were analyzed using the Gene Expression Omnibus (GEO), RRID:SCR_005012. After obtaining the transcriptomes, multiple comparisons were made using the Partek Genomics Suite 6.6v software, and the cutoff parameters were p = 0.05 and 1.5-fold change. Genes with the required level of statistical significance (p < 0.05) and a fold change cutoff of 1.5 were identified, and it was found that this combined criterion was significantly better for ranking candidate gene than p-value alone [19]. Fold change and statistical cut-offs modulate the outcome of microarray data, and these criteria suggest different biological meaning, with a fold change of 1.5 proving to be a better eliminator of background noise along with the p-value [20].

2.3. Biological Samples

Cervical samples were collected from women who attended the Colposcopy Clinic of the Sanitary Jurisdiction II at Ciudad Juárez, Chihuahua, Mexico. Women over 18 years old were invited to participate in the present protocol, all patients signed the informed consent letter, and a clinical history was obtained from each patient.

In total, ten CC samples biopsies and ten normal cervical scraping samples without lesions and free of HPV infection were used as a control. Patients without cervical lesions participated in the CC prevention program. The biopsies and scraping samples were reviewed by a pathologist immediately after collection, confirming the diagnosis. If more than 60% of epithelial cells were observed, then the samples were used for the detection of molecular gene expression.

2.4. DNA/RNA Extraction and cDNA Synthesis

DNA extraction from cervical samples was performed using the phenol/chloroform method and the extracted DNA was stored at −20 °C until use. Total RNA was extracted using TRIzol reagent and quantified using a Nanodrop 2000 device (Thermo Fisher Scientific, Wilmingtton, DE, USA). cDNA was synthesized with an ImProm-II Reverse Transcription System (Promega Co., Madison, WI, USA) as described by the manufacturer.

2.5. HPV Detection

The presence of the HPV in cervical samples was evaluated using the general gp5+/6+ primers directed to a region of the hpv/l1 gene [21]. High-risk hpv16 sequences were identified by using specific primers [22] (Figure 1).

Figure 1.

Figure 1

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HPV 16 genotypification. Lane 1: molecular weight. Lane 2–4: positive samples.

2.6. Evaluation of Gene Expression

The gene expression of selected genes was evaluated using semiquantitative PCR with specific primers for each gene. The obtained PCR products were detected using electrophoresis in an agarose gel with ethidium bromide staining. The gel images were analyzed using the EDAS 290 Kodak program (Eastman Kodak Company, Molecular Imaging Systems, Roachester, NY, USA) and densitometric analysis was performed to determine the net band intensity and, thus, the relative expression of each analyzed gene. The 18S rRNA constitutive gene expression was used to normalize the data. Table 1 lists the primer sets and amplification conditions.

Table 1.

Details of nucleotide primers used in this study.

Gene
Symbol		 Primer Sequence (5′-3′) Tm °C Amplicon Length (pb) Identification Number/Reference
URG4 Fw GCATCAGAGAGACGAACAGC 62 180 NM_017920.4
Rv GCACGTCCAGCACCATAG
P63 Fw GAGCTGAGCCGTGAATTC 55 319 AB082923.1
Rv CCTTCCTGTCTCTTCCTGG
HOXC6 Fw GAGGAAAAGCGGGAAGAG 60 250 NM_004503.4
Rv CGTGGTGAAAGAGAGTTGTG
RARβ Fw GTGTCCTTCCTGATTCATGC 62 163 [23]
Rv CCACTCTACCACAGCTTTCAC
MCM7 Fw GCTGCATTGATGAGTTCG 45 271 NM_005916.4
Rv CGTAGGTCATTGTCTCGG
PCNA Fw CTCCCAAGATCGAGGATG 55 249 NM_002592.2
Rv GACCAGATCTGACTTTGGAC
CISD2 Fw GGCTGCTGCAATTTGAAG 57 264 NM_001008388.5
Rv GTGTACGGAGGGTCAACTG
IL-10 Fw CCATTCCAAGCCTGACCAC 60 181 [24]
Rv GAATCCCTCCGAGACACTG
E6/E7 Fw ACCGAAAACGGTTGAACCGAAAACGGT 60 500 [25]
Rv GAG CTG TCG CTT AAT TGC TC
TAP73 Fw GAGCAGTACCGCATGACC 65 290 NM_005427.4
Rv CGTGAACTCCTCCTTGATG
COX2 Fw GCTGTATCCTGCCCTTCTG 55 291 AY462100.1
Rv CGGGAAGAACTTGCATTG
CA9 Fw CGGCTACAGCTGAACTTCC 60 238 NM_001216.3
Rv GTAGCTCACACCCCCTTTG
MACC1 Fw CAATGGAAGCCCTTTTGC 60 247 NM_182762.4
Rv GGTGACGGAAGAGCTTTAGC
HOXC8 Fw GAGCTCCTACTTCGTCAACC 55 250 NM_022658.4
Rv GTCTCCGTGGCAGCTAAG
CTHRC1 Fw GGACACCCAACTACAAGCAG 55 380 NM_138455.4
Rv CCAGCACCAATTCCTTCAC
BCL2 Fw CGACTCCTGATTCATTGGG 55 550 NM_000633.2
Rv GCTTTGCATTCTTGGACG
VEGF Fw CTTCAAGCCATCCTGTGTGC 55 147 [24]
Rv GCTCATCTCTCCTATGTGC
CRABP1 Fw GCACGCAAACTCTTCTTGAAG 60 133 [26]
Rv CGGACATAAATTCTGGTGCAG
cMYC Fw CCTCAACGTTAGCTTCACC 65 242 NM_002467.6
Rv GAAGGGAGAAGGGTGTGAC
SURVIVIN Fw GTCCCTGGCTCCTCTACTG 65 222 NM_001168.3
Rv CACTGGGCCTGTCTAATCAC
67LR Fw GGCTGTGCTGAAGTTTGC 57 216 NM_002295.6
Rv CCACATAGCGCAGAGGAG
CDKN2A Fw GAAGGTCCTACAGGGCCACA 68 211 NM_000077.4
Rv CAACACAGTGAAAAGGCAGAAGC
ELAVL2 Fw GACAAACTATGATGAGGCTGC 68.1 330 NM_004432.5
Rv CCCTGTCCTCTTGTCCATATTC
HS6ST2 Fw CGTACCGCTCGGAGGATG 63.5 313 NM_001077188.2
Rv GTGAGCTCGGTCCAGTCG
ZIC2 Fw GGAGCAGAGCAACCACGTC 64.5 268 NM_007129.5
Rv GTGCATGTGCTTCTTCCTGTC
18S Fw TTTGCGAGTACTCAACACCA 60 280 [27]
Rv GTTGTCCSGSCCSTTGGCTA
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2.7. Statistical Analysis

Based on the obtained results, the assumptions of normality and homogeneity of variance for each variable were analyzed using the Shapiro–Wilk and Levene statistical tests, respectively. Variables that did not meet the assumptions were transformed. The analysis of the relative expression data obtained was performed using Student’s t distribution test (with a significance level of 0.05), while Fisher’s exact test was performed to compare the proportion of amplification of each gene between the normal group and the cancer group (a significance level of 0.05).

3. Results

3.1. Overexpressed Gene Expression Identification from NCBI Analysis

The bioinformatics analysis of the NCBI database aided in selecting 21 genes from 26 published papers, as follows: P63, URG4, HOXC6, HOXC8, RARβ, MCM7, PCNA, CISD2, IL-10, E6/E7, TAP73, COX2, CA9, MACC1, CTHRC1, BCL2, VEGF, CRABP1, cMYC, Survivin, and 67LR genes (Table 2). These genes were reported to be overexpressed in different types of cervical cancer at a statistically significant level, and they participated in several cellular pathways such as the cell cycle, cellular proliferation, immune system, apoptosis, angiogenesis, etc.

Table 2.

Differentially expressed genes in cervical cancer reported in the literature.

Protein Gene Name Molecular Function/Biological Process Type of Cancer p-Value Reference
Tumor protein p63 p63 DNA binding/transcription, transcription regulation Cervical cancer 0.001 [28]
Minichromosome maintenance complex component 7 MCM7 DNA binding/cell cycle Cervical cancer, CIN 3, invasive cancer 0.002, 0.035 [29,30,31]
Upregulator of cell proliferation URG4 Proliferation Cervical cancer 0.0001 [32]
Retinoic acid receptor beta RARβ DNA binding/transcription, transcription regulation Cervical cancer NR [33]
Vascular endothelial growth factor C VEGFC Growth factor/angiogenesis Cervical cancer 0.002 [34]
Interleukine 10 IL-10 Cytokine Invasive squamous cell carcinoma of the cervix <0.05 [35]
BCL2 apoptosis regulator BCL-2 Apoptosis Cervical cancer <0.001 [36]
CDGSH Iron-Sulfur Domain-Containing Protein 2 CISD2 RNA binding/Autophagy Cervical cancer <0.001 [37]
Cyclooxygenase 2 COX-2 Angiogenesis Cervical cancer 0.0152 [38]
Tumor suppression protein P73 TAP73 P53binding/positive regulation apoptosis process Cervical cancer 0.001 [39]
Carbonic anhydrase 9 CA9 Proliferation Uterine cervical cancer 0.008 [40]
Survivin SURVIVIN Apoptosis Cervical cancer, squamous cell carcinomas 0.0001
<0.05		 [41,42]
Laminin Receptor 67 kD, Ribosomal Protein SA 67LR Laminin binding/cell adhesion Squamous cell carcinomas, carcinoma in situ 0.0001 [43]
Myc proto-oncogene protein cMYC DNA binding transcription factor/proliferation Cervical cancer, squamous cell carcinoma <0.0001
<0.05		 [44,45,46]
Collagen triple helix repeat-containing CTHRC1 Cell migration Squamous cell carcinoma <0.001 [47]
Proliferating cell nuclear antigen PCNA DNA binding/DNA replication Squamous cell carcinoma NR [48]
MET Transcriptional Regulator MACC1 MACC1 Growth factor activity/transcription regulator Cervical cancer 0.039 [49]
Homeobox protein Hox-C6 HOXC6 DNA binding/transcription regulator Cervical cancer 0.016 [50,51]
Homeobox protein Hox-C8 HOXC8 DNA binding/transcription regulator Cervical cancer <0.0001 [52]
Cellular retinoic acid-binding protein 1 CRABP 1 Cell cycle Cervical cancer <0.001 [53]
Proteins E6/E7 E6/E7 DNA binding/transcription regulation, modulation of host cell apoptosis Cervical cancer 0.034 [25]
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3.2. Identification of Overexpressed Gene from Transcriptomic Libraries for CIN2+

Using the Array Express platform data, 161 transcriptomic libraries were classified as CIN2+ accessed through the GSE63514 GEO repository [54] and GSE5787 [55]. Then, the data of the different cervical stages were compared with that of the normal group by using the Genomics Suite. After a stringent gene expression analysis (p ≤ 0.05), only the CDKN2A, ZIC2, ELAVL2, and HS6ST2 genes were selected (Table 3). All these genes exhibited increased expression (>1.5-fold change) in the CIN2+ samples. Interestingly, these genes exhibited >2-fold expression for CIN2+ compared to CIN1, suggesting that they are potential and predictive cervical cancer markers.

Table 3.

Differential expression according to the stage of cervical intraepithelial neoplasia.

Classification
Gene CIN I CIN II CIN III Cancer
CDKN2A 2.91 7.99 11.11 12.49
ZIC2 1.38 2.12 4.05 13.15
ELAVL2 2.11 2.92 4.37 7.29
HS6ST2 2.67 2.90 6.28 6.51
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Expression level does not have explicit unit.

3.3. Biological Samples and Characteristics

The non-cancer patients were grouped according to their age, where 90% of these patients were over 35 years old, while cancer patients were mostly over 35 years old (60%); among these patients, 80% had histologically confirmed adenocarcinoma. Almost all the patients were multiparous with two or more pregnancies. Regarding the use of hormonal contraceptive methods, all patients with no lesions reported the use of hormonal treatments, but only 70% of women with cancer reported the use of hormonal treatments (Figure 2).

Figure 2.

Figure 2

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Clinical data of the patients.

As for HPV infection, all cancerous samples were HPV16 positive, while the normal tissue samples were HPV negative.

3.4. Selection of Differentially Expressed Genes in Cervical Cancer

The 25 selected genes (21 from the literature and 4 from transcriptome databases) were grouped according to their role in cellular mechanisms: URG4, P63, MCM7, PCNA, Tap73, CRABP1 67LR, HS6ST2, ZIC2, HOxC6, HOXC8, RARB, E6/E7, CDKN2A, and ELAVL2 for cell cycle, cell division and proliferation; Survivin, BCL2, and CISD2 for apoptosis; COX2, CTHRC1, VEGF, and cMYC genes for angiogenesis; MACC1 and CA9 for invasion and metastasis; and IL-10 for anti-inflammatory response.

To validate the selected genes, total RNA was subjected to RT-PCR assays in normal and CC samples. As can be observed in Figure 3, even when the bioinformatics analysis indicated differential expression, intriguing in vivo evaluation results were observed. Finally, only 13 genes were in vivo differentially expressed (Table 4): HOXC6, HOXC8, RARβ, E6/E7, CDKN2A, ELAVL2, URG4, CISD2, CA9, BCL2, Survivin, MACC1, and IL-10.

Figure 3.

Figure 3

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Relative expression of evaluated genes in normal and cancerous tissues, classified by hallmarks of cancer. * Significant differences.

Table 4.

p-values of evaluated genes of hallmarks of cancer.

Hallmark of Cancer Gene p-Value
Cell cycle, cell division, and proliferation URG4 0.0395 *
P63 0.3191
MCM7 0.1041
PCNA 0.0974
Tap73 0.0889
CRABP1 0.4246
67LR 0.0680
HS6ST2 0.0511
ZIC2 0.1618
HOXC6 0.0060 *
HOXC8 0.0373 *
RARβ 0.0031 *
E6/E7 0.0078 *
CDKN2A 0.0001 *
ELAVL2 0.0013 *
Immune system IL-10 0.0190 *
Apoptosis Survivin 0.0047 *
BCL2 0.0001 *
CISD2 0.0086 *
Angiogenesis COX2 0.0524
CTHRC1 0.3900
VEGF 0.4728
cMYC 0.0859
Invasion and metastasis MACC1 0.0024 *
CA9 0.0326 *
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* Statistical difference.

The frequency of expression of the 13 genes was evaluated in normal and cancer samples, and it was found that only 5 genes were expressed more often in CC. These genes are MACC1, HOXC8, BCL2, RARβ, and the oncoproteins E6/E7. Figure 4 shows the expression frequency of the 13 genes among normal and cancer samples.

Figure 4.

Figure 4

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Frequency of expression of genes in normal and cancer samples. * Statistical difference.

4. Discussion

Thirteen genes were found to be differentially expressed in cervical adenocarcinoma samples using microarray databases and literature reports, but only the MACC1, HOXC8, BCL2, and RARβ genes were the most representative of the expressed genes. These candidate genes could be considered for detecting cervical adenocarcinomas, as they are involved in the cellular division and proliferation, invasion, apoptosis, and immune system hallmarks.

Recently, the Global Strategy for CC Elimination Initiative was announced by the WHO. This initiative endeavors to screen 70% of women globally using a high-performance test [56]. However, the success of this initiative will depend on access to public health services in each region.

The role of HPV in cancer is widely known [57]. It is accepted that the molecular mechanism of cervical epithelial transformation entails E6/E7 viral oncoprotein expression, where p53 and Rb suppressor proteins are targeted by these viral oncoproteins. As expected, E6/E7/HPV gene expression was over-represented in the cervical carcinoma samples. This is supported by studies indicating that E6/E7 RNA expression is a valuable molecular marker tool to identify CIN2+ detection or women at high risk of developing cervical cancer [58,59]. Furthermore, it has been reported that HPV-16 infection is associated with cervical adenocarcinoma [60]. Currently, commercial tests such as PreTect HPV-Proofer 7® or the macro/micro test are already available for assessing viral expression [61,62]. As MACC1, HOXC8, BCL2, and RARβ are overexpressed genes, we hypothesize that these genes are related to or indirectly influenced by viral oncoproteins. Moreover, it has been previously reported that HPV oncoproteins enhance RARβ expression [63].

Basic research on the transformation of cervical keratinocytes has aided in comprehending the expression of the HOX homeotic gene family, including the HOXC8 and HOXC5 genes [64,65]. In this case, genes modulated in adenocarcinomas, such as HOXC genes, could be directly related to and activated by viral sequences [65]. Thus, the evidence on the homeotic gene’s role in cancer shows that the cellular differentiation epithelial mechanisms are directly related to cervical adenocarcinomas via HPV infection [66]. Furthermore, there is evidence that HPV-16 infection modulates the HOXC genes via the E7 oncoprotein, with H3K4me3 and H3K27me3 as the gene promoters [67].

One of the most important transcription factors studied in cancer is the cMYC gene, and its important role in cell proliferation has been demonstrated [68]. Even though no statistical significance was observed, its role in cancer is important [69].

Recently, it has been proposed that MCM7 gene expression could be a prognostic factor in breast luminal cancer [70]. The MCM7 gene could play an important role in cervical cancer cells, allowing cellular replication. For its promoter activity, the E2F transcription factor is necessary. In HPV-infected cells, the HPV/E7 protein releases the E2F transcription factor from the Rb-E2F complex, promoting cell growth and cellular function [71].

BCL2 expression is involved in apoptosis, and its overexpression inhibits apoptosis [72,73]. Furthermore, it has been proven that BCL2 expression is a predictor of neoadjuvant chemotherapy in urothelial bladder and breast cancer [74,75]. According to this study, BCL2 expression could be useful as a marker and a predictor of neoadjuvant chemotherapy for adenocarcinomas [76].

Regarding the contrasting results, where not all of the selected genes were overexpressed in cancer samples, they can be explained in part by the fact that the obtained statistically significant p-values were not necessarily representative. A limitation of this study is the small number of fresh samples used to validate the candidate genes. We hypothesize that, even with the small number of samples used (randomized selection), there is strong evidence that the selected genes, HOXC6, HOXC8, RARβ, BCL2, and E6/E7, can be used as a pan early cervical adenocarcinoma test. The lack of correlation in the gene overexpression of CC samples could be explained by the intra/inter-heterogeneity of the samples.

There is evidence that MACC1 overexpression predicts a poor clinical outcome of hepatitis B virus-related hepatocellular carcinoma [77]. This could suggest that MACC1 expression is a viral target. Evidently, TNF-α regulates the induction of MACC1 via NF-κB and the transcription factor c-Jun in an inflammatory environment [78]. In cervical cells harboring the HPV sequences in an inflammatory environment, the MACC1 gene expression could be involved in virus infections.

Finally, there is enough information on the role of CDKN2A, ZIC2, ELAVL2, and HS6ST2 genes in cancer. Thus, they could be considered as important molecular markers useful for cervical screening programs in CIN2+ high-risk cervical samples. A major limitation of this study was the small number of samples used; therefore, it is necessary to conduct studies with a larger number of samples to validate the study data. Efforts are being taken to identify distinctive molecular makers that help in the early diagnosis of diverse types of cancer, and the results of these efforts could depend on the variability of cancers, populations, and risk factors. Thus, it is necessary to continue research to find specific molecular markers that help reduce the incidence rate of cancer.

5. Conclusions

In conclusion, this exploratory pilot study, through its robust and holistic analysis, provides evidence that MACC1, HOXC8, RARβ, BCL2, and E6/E7 could be promising molecular markers for the detection of cervical adenocarcinomas. In addition, the CDKN2A, ZIC2, ELAVL2, and HS6ST2 genes can be used in the screening of CIN2+ cervical samples.

Acknowledgments

All authors thank Universidad Autónoma de Ciudad Juárez for the support and infrastructure; CONAHCYT for a scholarship granted to Karla B. López-Cordova for her master’s degree studies; UIM Endocrinología Experimental, Hospital de Especialidades, CMN Siglo XXI, Mexico, for supporting the research visit in their facilities; and Cecilia Díaz-Hernández in the Colposcopy Clinic, Sanitary Jurisdiction II of Ciudad Juárez, Mexico, for providing the samples.

Author Contributions

Conceptualization, F.J.-V.; formal analysis, D.M.-R.; investigation, K.B.L.-C.; methodology, F.J.-V.; resources, F.J.-V.; writing—review and editing, I.A.G.-M., M.S.-V., C.L.V.-R., A.M.E.-A., and S.A.M.-E. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The present work was an exploratory, experimental, cross-sectional pilot study. The project was approved by the bioethics committee of the Universidad Autónoma de Ciudad Juárez, Chihuahua state (CBE-ICB/004-01-14), approved 16 May 2014.

Informed Consent Statement

All subjects signed an informed consent letter before samples were taken. The study was conducted in accordance with the Declaration of Helsinki and following the STROBE statement [79].

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

Conflicts of Interest

All the authors declare no competing interests.

Funding Statement

This research received no external funding.

Footnotes

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