MLH1 Gene Expression in Peripheral Blood in Colon Cancer Patients and Their Association with Colon Cancer

This study examines the association between MLH1 gene expression in peripheral blood and colon cancer, utilizing a case-control approach to compare expression levels in patients and matched healthy controls.

MutL homolog 1 (MLH1) is a component of the heterodimeric complex MutLα that detects and fixes base-base mismatches and insertion/deletion loops caused by nucleotide misincorporation. In the absence of MLH1 protein, the frequency of non-repaired mismatches increases, resulting in organ cancer. The current study sought to quantify MLH1 gene expression and its relationship with tumor invasion (T) and lymph node invasion (N) in blood samples from patients with colorectal cancer (CRC). Blood samples were obtained from 36 CRC patients. RNA was extracted, and cDNA was synthesized using a kit. The primers were built using the exon-exon junction approach, and MLH1 and β-actin genes were tested 3x using real-time polymerase chain reaction (Real-Time PCR). Gene expression analysis software was used to analyze the data, and a t-test was used to examine the expression of MLH1 and its connection with T and N variables. In this study, 36 patients with colorectal cancer, including 15 (41.6%) women and 21 (58.4%) men, with a mean age of 57.35 ± 4.22 years and in the age range of 26-87 years, were included. The results showed that the ratio of MLH1 gene expression in patients decreased compared to that in healthy individuals, and the decrease in gene expression at different stages of the disease was significant. The results of this study showed that the reduction of MLH1 gene expression has an effective role in the development of CRC.

Colon cancer (CRC) is one of the most common types of cancer. It is the fourth leading cause of cancer-related death worldwide¹. CRC is more frequent in males than in females, and it is three to four times more common in industrialized countries than in developing countries. The age-standardized (global) incidence rate per 1 x 10⁵ of CRC incidences is 19.7 in both sexes, 23.6 in men, and 16.3 in females². Epidemiological studies have shown strong environmental and lifestyle associations with CRC. Obesity, red/processed meat, tobacco, alcohol, androgen deprivation therapy, and cholecystectomy are all associated with modestly increased CRC risks^2,3.

Chromosomal instability, microsatellite instability, and CpG island methylator phenotype (CIMP) play an important role in the tumorigenesis of CRC⁴. According to previous studies, approximately 250 different mutations have been identified in patients with CRC, which is equivalent to approximately 55% of known mutations related to DNA mismatch repair (MMR) genes. Defects in mismatch repair proteins can be caused by germline mutations in the MSH6, MLH1, PMS2, and MSH2 genes, and most of these mutations are found in MLH1 and MSH2 genes^4,5. The most important protein in the MMR system, which is usually involved in CRC, is MLH1. Recent studies have shown that any change in MLH1 expression may increase the risk of CRC. Germline mutations in MLH1 are responsible for Lynch Syndrome, an inherited type of CRC. In addition, 13%-15% of diffuse colon cancer cases are caused by MLH1 deficiency based on somatic promoter hypermethylation^6,7,8.

MLH1 gene is located on the short arm of chromosome 3 at position 22.2 and contains 21 exons⁹. The protein encoded by the MLH1 gene can cooperate with an endonuclease involved in mismatch repair, PMS2, to generate MutLα, which is part of the MMR system. MutLα is mainly involved in the repair of base-base mismatches and deletion and addition loops as a result of incomplete DNA replication. In addition, the encoded protein is involved in DNA damage signaling and can be converted to the ɣMutL form with the MLH3 protein, which is involved in DNA mismatch repair observed in meiosis^10,11,12. Studies have shown that MLH1 is involved in other major cellular activities, including regulation of cell cycle checkpoints, apoptosis, crossover recombination, and mitotic incompatibility¹³.

The MLH1 gene plays a key role in the DNA mismatch repair (MMR) system. A defect in the function of this gene can lead to the accumulation of genetic mutations and, as a result, the development of colorectal cancer¹⁴. Previous studies have shown that about 55% of mutations associated with MMR genes in patients with CRC are related to MLH1 gene mutations. In addition, decreased MLH1 gene expression can lead to Lynch syndrome, which is an inherited form of colorectal cancer^15,16. Also, MLH1 gene defect based on somatic promoter hypermethylation has been observed in 13%-15% of sporadic colorectal cancer cases¹⁷. These scientific evidence show that the MLH1 gene acts as an important biomarker in colorectal cancer, and its expression analysis can provide valuable information about the function of the MMR pathway and the genetic risk of CRC¹⁸. Measuring MLH1 expression levels in the peripheral blood of patients with colon cancer can provide valuable information about the functionality of the MMR pathway, which is often disrupted in colon cancer. This method can be used for prognostic purposes and to understand genetic susceptibility to colon cancer^19,20. A study on the relationship between MLH1 415 locus G to C mutation and sporadic colorectal cancer in Chinese patients found that the frequency of the MLH1 C/C genotype was significantly higher in sporadic CRC patients than in controls, suggesting a genetic susceptibility to sporadic CRC in Chinese patients²¹. Another study compared the gene expression of CRC genetic biomarkers in peripheral blood and biopsy samples of inflammatory bowel disease (IBD) patients, highlighting the potential of peripheral blood gene expression analysis for understanding colon cancer-related biomarkers²².

Considering the important role of the MLH1 gene and studies conducted in recent decades with molecular analysis by profiling mRNA expression, cancers have been classified with higher accuracy. The purpose of this study was to quantitatively investigate the expression of MLH1 in peripheral blood samples of patients with CRC using real-time PCR, and to investigate its relationship with pathological factors, stages of tumor progression to the layers of the intestinal wall (T), and stages of invasion to lymph nodes (N). This study was carried out in 36 CRC patients to potentially establish quantitative changes in gene expression as biomarkers for CRC screening, prognosis, and diagnosis using peripheral blood samples.

A case-control research was conducted at Affiliated Hospital 2 of Nantong University between April 2021 and May 2023. Engage with the hospital's administrative department to establish the study framework. Ethical approval was obtained by submitting the study proposal to the Nantong University Ethics Committee. Ethical guidelines were followed to ensure confidentiality and informed consent.

1. Patient recruitment and study design

1. Sampling for inclusion of participants in the study
   1. Investigate the expression of the MLH1 gene in the peripheral blood of 36 individuals with colon cancer and a control group. Develop clear inclusion and exclusion criteria for participant selection.
   2. Recruit individuals diagnosed with specific types of colon cancer (rectosigmoid, cecum, ascending, transverse, and descending colon cancers who were regarded as having colorectal tumors) and have informed consent.
   3. Exclude individuals with other cancer diagnoses or conditions that could confound gene expression analysis. In addition, exclude participants with a history of blood transfusion in the last 3 months, a history of chemotherapy or radiation therapy in the last 6 months, a history of alcohol or drug use, and a history of autoimmune diseases or inflammatory bowel diseases.
2. Categorize clinical risk factors according to the TNM (Tumor, Nodes, and Metastasis) staging system, considering cancer mass, tumor size, and invasion to adjacent organs^23,24.
   1. Divide different cancer stages (0-4) based on available pathology file data.
   2. Segment the rate of tumor growth and progression in the intestinal wall layers (T index) into four distinct groups (T1-4, T0-TX) and lymph node invasion (N index) into four groups (N1-3, N0-NX).
3. Prepare a control group with healthy individuals.
   1. Match the control group to the patient group in terms of age and sex. Collect detailed demographic data for each participant to ensure comparability.
4. Informed consent process
   1. Prepare informed consent documents that explain the study's purpose, procedures, and potential risks.
   2. Engage with participants, providing them with ample time to ask questions. Collect signed informed consent forms before proceeding with sample collection.

2. Extraction and purification of RNA

1. Collection of peripheral blood samples
   1. Obtain commercially available EDTA-coated tubes that are certified for clinical or laboratory use. Verify the expiration date of the tubes. Check the integrity of the tube packaging.
   2. Instruct the participant to sit comfortably. Using a sterile syringe, draw 5 mL of blood from the antecubital vein. Ensure that the participant is relaxed, with the arm extended to expose the vein. Immediately transfer the blood samples into the prepared tubes, ensuring minimal exposure to air.
   3. Maintain the samples at 4 °C during transport to the laboratory to preserve RNA integrity.
2. Use an RNA blood mini kit for RNA isolation following manufacturer's instructions.
   1. Briefly, lyse red blood cells by incubating the whole blood sample with Buffer EL on ice. Centrifuge to pellet the leukocytes and discard the supernatant. Lyse the leukocytes with Buffer RLT and homogenize the lysate using a shredder column.
   2. Add ethanol to the homogenized lysate and transfer to a spin column. Wash the column with Buffer RW1 and Buffer RPE. Elute the purified RNA by adding RNase-free water to the column and centrifuging.
3. Assessing RNA amount and quality
   1. Quantification of RNA: Employ a spectrophotometer to measure RNA concentration and purity. Open the software on the connected computer. Select the Nucleic Acid option from the main menu. Apply 1 µL of RNA sample onto the sample area.
   2. Test purity and integrity of RNA
      NOTE: The integrity and size distribution of total RNA purified with the RNA blood mini kit can be checked by spectrophotometery and gel electrophoresis. Ribosomal RNAs should appear as sharp bands or peaks. The apparent ratio of 28S rRNA to 18S rRNA should be approximately 2:1. If the ribosomal bands or peaks of a specific sample are not sharp but appear as a smear towards smaller-sized RNAs; it is likely that the sample suffered major degradation either before or during RNA purification.
      1. For spectrophotometry measurement, lower the arm of the device and click Measure to obtain the A260/A280 ratio. Evaluate the RNA sample purity, aiming for an A260/A280 ratio between 1.8 and 2.0.
      2. Perform 1% agarose gel electrophoresis as described below.
         1. Prepare the 1% agarose solution by heating agarose powder in TAE buffer until dissolved. Add ethidium bromide to the agarose solution to achieve a final concentration of 0.5 µg/mL. Pour the agarose-ethidium bromide solution into a gel casting tray and allowing it to solidify.
            NOTE: Ethidium bromide is a fluorescent dye that intercalates with RNA to enable visualization under UV light.
         2. Mix the RNA samples with loading dye and carefully load them into the wells of the solidified gel. Run the gel electrophoresis at 100 V for approximately 30 min to separate the RNA fragments by size. Visualize the RNA bands by placing the gel on a UV transilluminator or imaging system, which causes the ethidium bromide-stained RNA to fluoresce.
4. Storage of extracted RNA
   1. Take the extracted RNA samples. Aliquot the RNA into sterile microcentrifuge tubes, using 10 µL volumes per aliquot. Store the RNA aliquots at -80 °C or lower.
5. Reverse transcription of RNA into cDNA
   1. Follow the reverse transcription kit protocol. Thaw and prepare the necessary reagents on ice and at room temperature.
   2. Perform a genomic DNA elimination reaction to remove any genomic DNA contamination.
   3. Prepare the reverse transcription master mix on ice, which contains all components except the template RNA.
   4. Add the template RNA from the DNA elimination step to the reverse transcription master mix. Incubate the reverse transcription reaction at 42 °C for 15 min.
   5. Inactivate the reverse transcriptase enzyme by incubating at 95 °C for 3 min. Use an aliquot of the finished cDNA for immediate real-time PCR or store the cDNA at -20 °C for later use.

3. Primer design for real-time PCR

1. Selecting target genes and internal controls
   1. Choose the MLH1 gene as the target for quantification due to its association with colon cancer. Select β-actin as the internal control for normalization of gene expression data.
2. Primer design process
   1. Launch the primer design software and input the gene sequence of MLH1 obtained from the Ensemble or UCSC Genome Browser.
   2. Set the melting temperature (Tm) for primers between 58-60 °C, optimal GC content at 40%-60%, and primer length between 18-24 nucleotides.
   3. Input the designed primer sequences into the NCBI BLAST database to confirm specificity.
      1. Go to the BLAST website, select Nucleotide BLAST. Paste the primer sequences into the search box and click BLAST.
      2. Analyze the results to ensure no off-target amplification is predicted. See Table 1 for details.

4. Real-time PCR

1. Real-time PCR reaction setup. See Table 2 for details.
   1. Centrifuge the reagents at 4 °C for 5 min at 10,000 x g to collect contents at the bottom of the tubes.
   2. Prepare a master mix according to the commercial kit protocol, which includes the SYBR Green, buffer, dNTPs, MgCl₂, and Taq polymerase.
   3. Allocate the master mix into labeled PCR tubes, ensuring consistency in volume across samples.
   4. Add the appropriate volume of forward and reverse primers for MLH1 and β-actin into their respective tubes.
   5. Dilute the RNA samples to a consistent concentration of 100 ng/µL before reverse transcription and reverse transcribe to cDNA using a reverse transcription kit.
2. Amplification and data collection
   1. Place the PCR tubes into the real-time PCR system.
   2. Program the thermal cycler with the optimized cycling conditions: 95 °C for 20 s for denaturation, 54 °C for 30 s for annealing, and 72 °C for 30 s for extension. See Table 3 for details.
   3. Set the machine to run for 45 cycles.
   4. Monitor the reaction in real time on the system's software interface, observing the amplification curves for each sample.
   5. Include a negative control without cDNA to check for contamination. Repeat the PCR run 3x for each sample to ensure the reliability of the data.
3. Data analysis
   1. After completing the cycles, use the system's software to analyze the threshold cycle (Ct) values. Export the Ct values to a spreadsheet program for further analysis.
   2. Calculate the relative gene expression using the 2^-ΔΔCt method²⁵, normalizing the data to the β-actin control.

5. Immunohistochemistry and genetic analyses

1. Conduct immunohistochemistry on tissue sections to correlate MLH1 protein expression with gene expression levels.
   1. Prepare tissue slides and apply anti-MLH1 antibody.
   2. Use an HRP-conjugated secondary antibody and DAB (3,3'-Diaminobenzidine) substrate kit. Develop slides according to kit instructions and analyze them with a light microscope.
2. Perform methylation-specific PCR and microsatellite instability testing to differentiate between Lynch syndrome and sporadic CRC.
   1. Use a commercial DNA extraction kit. Follow the kit protocol for both blood and tumor tissues.
   2. Treat DNA with a bisulfite conversion kit. Use methylation-specific primers designed for the MLH1 promoter region. Perform PCR as per kit protocol.
   3. Run gel electrophoresis on the PCR products to visualize methylation status.
   4. For microsatellite instability (MSI) testing, capillary electrophoresis using an genetic analyzer. Compare microsatellite lengths by analyzing fluorescently labeled PCR products.

6. Statistical analysis

1. Employ commercial statistical analysis software for data analysis.
   1. Open the software and create a new dataset for gene expression and demographic data.
   2. Input the relative gene expression values and corresponding demographic data into the dataset.
   3. Use the Kolmogorov-Smirnov test to assess data normality.
      1. Select Analyze from the top menu, choose Non-parametric tests, and then Legacy Dialogs.
      2. Click on Kolmogorov-Smirnov Test and input the variable for gene expression. Execute the test and interpret the output for the significance of distribution normality.
   4. Conduct T-tests to compare MLH1 gene expression between patient and control groups.
      1. Select Analyze, then Compare Means, and choose Independent-Samples T Test.
      2. Assign group membership (patient or control) as the grouping variable and gene expression as the test variable. Run the test and interpret the two-tailed significance level.
   5. Calculate fold changes in gene expression using the 2^-ΔΔCt method²⁵.

In this study, 36 patients with colon cancer were examined for MLH1 gene expression in the peripheral blood and its relationship with colon cancer. Analysis of demographic variables showed that 15 patients (41.6%) were women, and 21 patients (58.4%) were men. The mean age of the patients was 57.35 ± 4.22 years, and the age range was 26-87 years. The body mass index (BMI) status of the patients showed that 14 patients (38.8%) had a normal BMI (18.5 and 24.9 kg/m²), and 22 patients (61.2%) did not have a BMI within the normal range (below 18.5 or above 24.9 kg/m²). The histopathological characteristics of the colorectal cancer cases were as follows: tumor grade was classified according to the WHO grading system²⁶, with 12 cases (33.3%) well-differentiated, 18 cases (50%) moderately differentiated, and 6 cases (16.7%) poorly differentiated. Lymphovascular invasion was present in 14 cases (38.9%), and perineural invasion was observed in 9 cases (25%). All the patients had clear resection margins. The results of examining the variable of tumor location showed that 20 tumors (55.5%) were in the colon, and 16 (45.5%) were in the rectum. Of all the tumors, 4 tumors (11.1%) were smaller than 10 mm, and 32 tumors (88.9%) were larger than 10 mm (Table 4).

We conducted a stratified analysis of MLH1 promoter hypermethylation and Lynch syndrome. Of the 36 colon cancer patients, 10 (27.8%) had MLH1 promoter hypermethylation, while genetic sequencing identified 5 patients (13.9%) with germline mutations consistent with Lynch syndrome. The remaining 21 patients (58.3%) did not exhibit MLH1 hypermethylation or Lynch syndrome, suggesting sporadic CRC (Table 5).

In the present study, genetic testing and family history were used to identify cases of hereditary MLH1 deficiency versus somatic MLH1 deficiency. Out of the 36 patients, 12 patients had a family history of Lynch syndrome, and genetic sequencing confirmed MLH1 germline mutations in 5 of these patients (13.9%). The remaining 7 patients with a family history showed no germline mutations but had somatic MLH1 deficiencies, possibly due to acquired mutations or epigenetic changes, such as hypermethylation. Of the 24 patients without a family history of Lynch syndrome, 15 (41.7%) had somatic MLH1 deficiency due to hypermethylation or somatic mutations. The remaining 9 patients without a family history had normal MLH1 expression levels, suggesting other mechanisms driving CRC.

The hereditary MLH1 deficiency group showed the most significant reduction in MLH1 expression, with an average fold change of 0.4, which was statistically significant (p <0.01). The somatic MLH1 deficiency group also showed a significant reduction in expression with an average fold change of 0.6 (p <0.001). The group with normal MLH1 expression levels did not show a statistically significant difference from the healthy control group (Table 6).

Figure 1 compares MLH1 gene expression levels between the two groups. Specifically, CRC patients exhibited significantly lower levels of MLH1 gene expression compared to the healthy individuals (p ≤ 0.001; Figure 1).

The level of gene expression at each stage of the disease compared to that in healthy people is shown in Table 7. The level of MLH1 gene expression was significant in Stage II (p ≤ 0.02), Stage III (p ≤ 0.01), and Stage IV (p ≤ 0.03). This indicates a decrease in gene expression in these stages of the disease in sick people compared to healthy people.

Figure 1: MLH1 gene expression. Comparison of MLH1 gene expression in blood samples of two groups of colorectal cancer patients and healthy people. The difference is significant (p ≤ 0.001). The statistical test used is the independent t-test. Error bars indicate the standard error of the mean (SEM). Please click here to view a larger version of this figure.

Table 1: Primers used in the study. The sequence of the designed primers and the length of the PCR product for the MLH1 target gene. The annealing temperature (Ta) is also provided.

Table 2: Reaction setup.

Table 3: Temperature cycle and time spent in technique real-time PCR.

Table 4: Demographic and clinical characteristics of patients.

Table 5: Data on MLH1 promoter hypermethylation and Lynch syndrome status.

Table 6: MLH1 gene expression levels in CRC patients. The mean fold-change indicates the level of MLH1 gene expression relative to the control group. A fold-change of less than 1 indicates a reduction in expression.

Table 7: Gene expression at different stages. The relative level of gene expression in each of the different stages of the disease compared to healthy people.

This study was conducted with the aim of investigating the expression of the MLH1 gene. In this study, it was shown that the level of MLH1 gene expression was decreased in sick people compared to healthy people. Based on fold change studies, it has been shown that the expression of the MLH1 gene in Stage II, Stage III, and Stage IV in sick people compared to healthy people had a significant decrease.

Colorectal cancer is a major problem in the management of cancer in the world due to its prevalence and survival rate^3,27. Considering that the life expectancy of some patients with colorectal cancer does not even reach 5 years and also because the needs of patients with cancer are different in different years after diagnosis, a complete investigation of colorectal cancer is an essential need^28,29.

In the meantime, launching completely specific molecular diagnostic tests along with clinical symptoms can be an effective help to diagnose people at risk and even people without clinical symptoms of colorectal cancer^30,31. Defects in mismatch repair proteins can result from mutations in genes such as MLH1. The most important protein of DNA mismatch repair involved in colon cancer is MLH1^32,33.

According to studies, MLH1 gene is related to many cancers, including colorectal, stomach, esophagus and cervix³⁴. The MLH1 gene plays an essential role in DNA repair, and this protein helps correct errors that occur during DNA replication in preparation for cell division³⁵. Therefore, better identification of the mechanism and expression of the MLH1 gene can determine how colorectal cancer occurs, and by identifying this mechanism, appropriate measures can be taken to prevent and treat colorectal cancer³⁶.

Guo et al. conducted a study in China with the aim of investigating MLH1 and MSH2 gene expression with clinicopathological features in colorectal cancer. In this study, clinical and pathological data of 88 patients undergoing cancer treatment were collected. The fold change of MLH1 and MSH2 in the tissues was measured by qRT-PCR, and the relationship between MLH1 and MSH2 was analyzed using the patients' pathological data. The results of this study confirmed the results of our study and showed that the expression of MLH1 and MSH2 genes is lower in cancer tissues than in other tissues³⁷.

In another study conducted by Liu et al., the analysis of the expression of MLH1 and PMS2 genes in elderly patients with colorectal cancer and its relationship with clinical pathology features was investigated. In this retrospective cohort study, elderly patients with colorectal cancer from January 2014 to December 2018 who were admitted to a Beijing hospital were divided into two groups, MLH1 and PMS. The pathological characteristics and gene expression were compared in two groups. The results showed that MLH1 gene expression is decreased in patients and this decrease in gene expression is also evident in different stages of cancer and the decrease in gene expression in patients is related to age of onset, rapid tumor progression, poor differentiation and pathological staging³⁸.

This decrease in gene expression has been shown in other studies in other countries. In studies conducted in the United States³⁹, Iran⁴⁰, and Ireland⁴¹, a decrease in MLH1 gene expression has been shown in patients with colorectal cancer.

The current standard involves universal tumor analysis of resected CRC specimens using IHC and molecular techniques. Patients with abnormal IHC findings are subsequently referred for definitive mutational testing. However, recent developments in our understanding of molecular genetics have transformed screening and diagnostic practices for Lynch syndrome⁴². A study by Alipoor et al. quantitatively analyzed MLH1 gene expression and its association with depth of tumor invasion (T) and lymph node invasion (N) factors in blood samples of Iranian patients with CRC. The study found that MLH1 gene expression was significantly associated with T and N stages, suggesting that MLH1 gene expression analysis could provide additional information beyond IHC techniques⁴³. Moreover, a study by Halil et al. analyzed the impact of each specific MMR gene loss and clinical characteristics of patients with dMMR CRC who received immune checkpoint inhibitors (ICIs). The study found that overall response rates (ORRs) in MLH1 ± PMS2 and MSH2 ±MSH6 groups were 68% and 57.1%, respectively, without statistical difference. This suggests that MLH1 gene expression analysis could be a valuable tool for predicting response to ICI therapy in dMMR CRC patients⁴⁴. Further research is needed to validate the role of MLH1 gene expression analysis in CRC management and to integrate it into clinical practice.

Considering the important and key role of the MLH1 gene in the repair of DNA inconsistencies and in accordance with the results of previous studies, it can be concluded that colorectal cancer is related to the decrease in the expression of the MLH1 gene. Therefore, with the reduction of gene expression, there is no possibility of correcting the defective cycle of replication, and the risk of creating a mutation will increase; therefore, this decrease in gene expression will play an important role in the process of disease development, treatment and the final outcome of the disease for people.

The results showed that the reduction of MLH1 gene expression has an effective role in the development of colorectal cancer. But the point in the importance of this gene is its role in cancer stages, which reduces gene expression in each stage of the disease. Therefore, this gene can be considered more in future studies; because there is a possibility that in the future it can play an effective role in improving the treatment status of colorectal cancer. Also, conducting more studies to find out other aspects seems necessary.

Critical steps within the protocol include strict adherence to RNA extraction and purification procedures, as the quality of RNA is paramount for accurate gene expression analysis. Additionally, the real-time PCR setup and the specific cycling conditions are crucial for reliable amplification and quantification of the MLH1 gene. Modifications and troubleshooting of the technique were addressed throughout the study. For instance, when suboptimal RNA purity was initially encountered, as indicated by spectrophotometer measurements, the extraction protocol was refined to include additional washing steps to remove potential contaminants. The technique's limitations include the reliance on peripheral blood, which may not fully represent the MLH1 expression levels within the tumor microenvironment.

In this study, we analyzed MLH1 gene expression in peripheral blood samples from 36 colorectal cancer patients. We recognize that this sample size is limited, which could affect the statistical power of our findings. Future studies with larger cohorts are warranted to validate our results and to better understand the role of MLH1 expression in colorectal cancer. Furthermore, the convenience sampling method may introduce selection bias, potentially affecting the generalizability of the results.

The authors declare no conflict of interest.

We would like to express our gratitude and appreciation to everyone who helped us complete this research endeavor.