Using High-Throughput SNP Technologies to Identify Variances Associated with Bladder Cancer

INTRODUCTION

Bladder Cancer

Bladder cancer is a common cancer that occurs when the cells within the bladder hyper proliferate and form tumors which can then metastasize to other parts of the body. This type of cancer commonly begins in the innermost lining of the bladder (urothelium or transitional epithelium). There are different types of bladder cancer which include 1) urothelial carcinoma, considered a transitional cell carcinoma and is the most common one, 2) squamous cell carcinoma, the subtype encountered only 1-2% of the time, 3) adenocarcinoma, less common at 1%, and 4) small cell carcinoma (less than 1%), and 5) sarcoma (less than 1%) (American Cancer Society, 2019).

According to the CDC, about 57,000 men and 18,000 women have bladder cancer, and about 12,000 men and 4,700 women die from the disease each year in the US. According to American Cancer Society data in 2022, there will be about 81,180 new cases of bladder cancer and 17,100 deaths from bladder cancer. Figure 1 shows the number of bladder cancer cases from 1999 to 2019. As you can see, the number increases in a relative linear relation up to the 60,000 which was reached in 2000. These data seem to support the evidence that bladder cancer is one of the most common cancers in the United States.




 

 

Figure 1: Annual number of new bladder cancer cases from 1999 to 2019 in the United States. This graph shows overall data of new bladder cancer cases from 1999 to 2019. By the year 1999, the case number had already reached 60,000 cases and it kept increasing every year. Obtained from the U.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool (2022).

Stages

Bladder cancer progression is separated into stages. The staging system used most often is called the Tumor Nodes Metastases System which is based on 3 keys: “T” used to describe the distance from the main (primary) tumor has grown through the bladder wall to nearby tissues, “N” used to describe any cancer cells spread to lymph nodes around the bladder , “M” usually indicates if the cancer cells have reached other sites such as distant organs (American Cancer Society, 2019). However, there is another numbering system which is not often used that ranges from stage 0 to stage 4. Stage 0 is the earliest stage in which cancer cells are found within the inner layer of the bladder. In stage I, cancer cells can be found growing within the connective tissue beneath the bladder lining. If the cancer cells continue to grow within the connective tissue layer and reach the muscle of the bladder wall, stage II has been reached. Stage III and IV are the two most advanced conditions of bladder cancer, with cancer cells growing through the muscle and into the fat layer, then invading other organs such as the womb, vagina, or the prostate (stage III). In stage IV, the cancer cells can metastasize to nearly all parts of the bladder and then invade other organs (Cancer Research UK, 2018).

Treatment

Treatments of bladder cancer are administered based on the stage it is in. Stage I is most often treated with transurethral resection (TURBT), which is a surgery to remove the tumor from the bladder through the urethra, and intravesical therapy treatment with fulguration 24 hours after surgery (Staff, 2021). The side effects of the TURBT include bloody urine, uncomfortable urination, and bladder infection. Intravesical therapy is a major treatment in which the liquid drug is administered into the bladder through a soft catheter and left for up to 2 hours (American Cancer Society, 2019). Some common side effects of intravesical therapy include irritation and a burning sensation in the bladder, and bloody urine. The type of radiation that is often used to combat bladder cancer is named External Beam Radiation Therapy and is used during the early stages of bladder cancer in patients who cannot undergo surgery or chemotherapy due to other complications. This type of radiation therapy can effectively prevent symptoms that are caused by advanced bladder cancer. The possible side effects include: nausea, vomiting, diarrhea, low blood counts, skin changes in the radiation area and bloody urine. Chemotherapy is used to treat bladder cancer where cancer cells are confined to the lining of the bladder and will have a high risk of progression or recurrence to a higher metastasis state. Side effects of chemotherapy include blood in the urine and frequent urination (American Cancer Society, 2021).
 

CCL4

CCL4 (C-C motif chemokine ligand 4) is a gene which is protein-coded located on 17q12, Gene ID 6351 (NCBI, 2022). The CCL4 gene has been found in chimpanzees, rhesus monkeys, dogs, cows, mice, rats, and chickens. The protein encoded by CCL4 is a mitogen-inducible monokine that is secreted and has chemokinetic and inflammatory functions. Also, It has been known to be one of the major HIV-suppressive factors produced by CD8+ T cells (NCBI, 2022). Particularly when it comes to urothelial bladder cancer, one of the most common cancers with a high mortality and recurrence rate, CCL4 plays a role in bladder cancer or its therapy. CCL4 has only been identified as being partially regulated by CC chemokines and being implicated in angiogenesis, tumor development, and metastasis of many malignancies. However, little is known about the function of distinct CC chemokines in bladder cancer.

SNPs

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation among people (National Library of Medicine, 2022). Each SNP denotes a variation in a single nucleotide, or DNA building block. An example of an SNP is the substitution of the nucleotide thymine (T) with the nucleotide cytosine (C) in a specific DNA stretch. SNPs can be used as genetic markers in analysis, and some SNPs located within a gene may directly affect the protein structure or gene expression levels. SNPs are common throughout the genome and can therefore be used in association studies, which postulate that two closely spaced alleles (a gene and a marker) are inherited together, as indications to find disease-causing genes. (Shastry, 2007). However, SNPs within genes that control telomere maintenance, mitosis, inflammation, and apoptosis, however, have not been thoroughly studied in bladder cancer.In this investigation, we used bioinformatics tools to identify these gene variances within the CCL4 gene and other regions of chromosome 17. We believe that any variances found in a bladder cancer clinical cohort that are not found in a normal cohort may have an association with the observed phenotypic tendencies of bladder cancer.

METHODS

To identify a good candidate gene, we accessed NCBI’s Gene Expression Omnibus (GEO) and sifted through microarray data. Once we settled on a published bladder cancer investigation, we took the accession number and entered it into the GEO2R database to identify genes with high expression. Through the careful analysis of each generated graph, the gene CCL4 was identified as a candidate with high expression in bladder cancer samples but low expression in normal samples. We then accessed the Sequence Read Archive (SRA) database and identified a bladder cancer clinical cohort composed of a total of 105 individuals. Ensembl’s FTP database was used to obtain chromosome 17’s sequence to be used as a reference. The fastq-dump tool was used to access the SRA database and download single end reads as fastq files which were then quality checked using the Babraham Institute’s FastQC program. The resultant HTML file was then evaluated to ascertain whether the bar graphs principally clustered within the green field. Trimmomatic was used to trim any fastq file reads that fell below a Phred score of 40 and then reanalyzed with FastQC. Bowtie2 was used to map the reads to the reference chromosome 17. The sequences were then indexed and locally aligned with the Burrows-Wheeler Aligner software package (bwa index and bwa mem). Next, The Sequence Alignment Map (SAM) tools were used to convert SAM output files into compressed Binary Alignment Map (BAM) files and then sort the files according to specific coordinates. BCFtools was used to call and view the detected SNPs, filter, and then convert the files into variant calling format (VCF). Python’s Pandas library was used to eliminate any variances found in both the bladder cancer clinical cohort and the normal cohort. The filtered variances were then compared by chromosomal position to NCBI’s Entrez Database to identify the variances’ SNP accession number. Ensembl’s Variant Effect Predictor was then used to identify the genotypic effect of the identified SNPs.

RESULTS

The analysis of chromosome 17 resulted in the identification of hundreds of variants which were common in at least 70% of the individuals in the bladder cancer cohort. After removing those that were also found in the normal population and were not currently characterized as SNPs, only 14 remained. These were then further evaluated using the Variant Effect Predictor which detected the following effects: 1) Intron variant- impacts alternative splicing by interfering with the splice site recognition (Lin, 2019). 2) non coding transcript variant- potentially results in the loss of the start or stop codons (Dhamija, 2018). 3) nonsense-Mediated mRNA Decay (NMD) transcript variant- targets on the nonsense-mediated mRNA decay function, which under typical circumstances decreases gene expression mistakes by removing mRNA transcripts with early stop codons. 4) regulatory region variant- variant within the non-coding genomic region that has been demonstrated to be associated with different diseases. 5) upstream and downstream gene variants - variants that can affect a gene’s regulatory region and may cause damage to the gene. 6) transcription factor (TF) binding site variant- a sequence variant located within a transcription factor binding site which is involved in the regulation of transcription.

As stated earlier, our analysis detected variances within chromosome 17 totaling 13526. After filtering for characterized SNPs that were not found in the normal cohort, we were left with a total of 14 which are listed in Table 1 below. According to VEP results, ten of the 14 SNPs have at least one effect which may be associated with bladder cancer.

Table 1: Analysis of the bladder cancer cohort identified 14 SNPs with various genetic effects.Table of the gene’s location and the SNPs that correspond with and have effects on bladder cancer: Positions on chromosome 17 were entered in the Entrez database to obtain the SNP accession number. Those variants that were not found to be characterized are marked as “None.” Variant effects were listed as determined by the Entrez database.

Table 1: (In the doc I uploaded)

 

DISCUSSION

Our analysis shows that there is a strong association between the identified 14 SNPs and bladder cancer. Although many variants were identified, not all met the requirements for a common SNP. Still further analysis is needed in order to have a complete picture. Future experiments can involve the analysis of all 23 chromosomes instead of just limiting it to chromosome 17. Further research must also be conducted to correlate these findings with in vivo data. Initial DNA can be collected and then purified for sequencing from a clinical cohort. This can be repeated every 10 years as they are simultaneously observed for phenotypic signs of bladder cancer. In order to analyze all 23 chromosomes, at least 30 TB of storage space is required. This coupled with the demand for random access memory places limits on computing power.

CONCLUSION

Our analyses were able to detect several variances within chromosome 17. After filtering our results for those that were characterized as SNPs on NCBI’s Entrez database and those that were not found in normal sequences, we were left with 14 SNPs. Although half were predicted to not have detectable genetic effects, the other half shared genetic consequences ranging from a regulatory region variant to NMD. These SNPs existed in all 105 patients’ chromosome 17 and did not exist in normal human mutations. This association presents itself as highly plausible biomarkers that can be used in bladder cancer diagnosis and target treatments.
 

 

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