{"id":62470,"date":"2024-12-30T11:26:25","date_gmt":"2024-12-30T11:26:25","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=62470"},"modified":"2025-01-06T18:23:26","modified_gmt":"2025-01-06T18:23:26","slug":"high-throughput-genomics-study-for-the-identification-of-novel-genes-functional-in-b-cell-non-hodgkin-lymphoma","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no4\/high-throughput-genomics-study-for-the-identification-of-novel-genes-functional-in-b-cell-non-hodgkin-lymphoma\/","title":{"rendered":"High Throughput Genomics Study for the Identification of Novel Genes Functional in B-Cell Non-Hodgkin Lymphoma"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Non-Hodgkin’s\nlymphoma (NHL) the most common type of lymphoma in which typically the\ncancerous lymphocytes are present at the lymph nodes. Non-Hodgkin’s lymphoma\naccounts for about approx. 90% of lymphoma in human beings1<\/sup>. There\nare a number of risk factors recognized which may be accountable for the\nmalignant transformation of non-Hodgkin\u2019s lymphoma such as some sort of immune\ndisorders, some infection, even the lifestyle of an individual, the genetics,\nhaving family history and profession all these have a strong impact. According\nto various studied on the types of non- Hodgkin\u2019s lymphoma, diffuse large\nB-cell lymphoma (DLBCL) is most common3<\/sup>. It has been seen that in\nDLBCL there is fast nodal or extra nodal tumour growth, however this could also\nblowout to the other regions of lymphatic system. These lymphatic systems take\nin the lymphatic vessels, adenoids, tonsils, thymus, spleen, and even bone\nmarrow. Rarely, non-Hodgkin’s lymphoma may even involve the organs excluding\nthe organs of lymphatic system 4<\/sup>.<\/p>\n\n\n\n

Nearly\n90% of non-Hodgkin\u2019s lymphomas develop in B cells. One of the types of B-cell\nnon-Hodgkin lymphoma is Burkitt lymphoma, which is most commonly diagnosed in\nyoung adults and children. Studies indicate that this lymphoma predominantly\naffects males. It arises from mature B-lymphocytes and is known to be one of\nthe most aggressive and rapidly proliferating cancers. Despite its severity,\nBurkitt lymphoma is relatively rare, accounting for only about 2% of all\nlymphoma cases diagnosed 5<\/sup>.<\/p>\n\n\n\n

DLBCL\nis considered as the most common form of B-cell non-Hodgkin lymphoma. DLBCL\nrepresents around 30 % of all the cases. This type of lymphoma is mainly found\nin old age population. It is an aggressive form of lymphoma and has a high rate\nof proliferation. Apart from just being diagnosed in the lymph system, this\ntype of lymphoma can be also found in other parts of the body like in breast,\nbrain, testes and even in the gastrointestinal (GI) tract as a primary disease6<\/sup>.<\/p>\n\n\n\n

 Follicular lymphoma is referred as most\nlethargic lymphoma, having relatively lower rate of proliferation as compared\nto other types of lymphoma. Accounting for around 20 % of all the lymphoma\ncases. This type of lymphoma is mainly expressed in moderate age range\npopulation and in older adults also. Bone marrow or the lymph nodes are main\nsite for the development of FL7<\/sup>. Mantle cell lymphoma (MCL) Accounts\nfor around 5% of all lymphoma diagnoses. It is majorly diagnosed in old aged\nmen. The sites for this type of lymphoma includes lymph nodes, bone marrow and\nspleen. MCL is one of the slow growing lymphomas8<\/sup>. According to\nvarious studied, the switching or translocation of position between two\nchromosomal segments is the genetic behind the cause of MCL. This type of\nlymphoma led to swelling of the lymph nodes and can spread to other parts of\nthe body through blood. common forms of treatment methods for MCL are\nchemotherapy and targeted therapy. Apart from these stem cell transplant can\nalso be an effective way for the treatment of MCL9<\/sup>.<\/p>\n\n\n\n

Marginal\nzone lymphomas are a slow-growing type of lymphoma that develops in mature B\ncells within the spleen. Treatment options for this lymphoma can include\nchemotherapy and, in some instances, surgery. If the lymphoma is linked to an\ninfection, antibiotics might also be used as part of the treatment 10<\/sup>.<\/p>\n\n\n\n

RNA-Seq data Analysis<\/strong><\/p>\n\n\n\n

Transcriptome\nanalysis is increasingly leveraging high-throughput RNA sequencing (RNA-Seq)\ntechniques. These methods surpass microarrays in several key areas, including\nsingle base pair resolution, minimal background noise, a broad range for\ndetecting transcript expression levels, greater reproducibility, reduced RNA\nsample requirements, and the capacity to discover transcripts not yet mapped to\na known genome11<\/sup>.<\/p>\n\n\n\n

Recent\nadvances in both technology and analytical methods now allow for the\nsimultaneous assessment of thousands of genes through next-generation\nsequencing. These developments have revolutionized cancer genomics, enabling\nlarge-scale and unbiased detection of genomic alterations. High-throughput mRNA\nsequencing (RNA-Seq) utilizes massively parallel sequencing to deliver a\ncomprehensive and unbiased overview of genome-wide transcription levels and\ntumor mutation status. During the RNA-Seq process, complementary DNA (cDNA) is\ngenerated to create short sequence reads by attaching millions of amplified DNA\nfragments to a solid surface and conducting the sequencing reaction. The\nresulting sequences are then aligned with a reference genome or transcript\ndatabase, providing a thorough description of the transcriptome under\ninvestigation.<\/p>\n\n\n\n

Hence the aim of this study was to- Identify novel genes involved in B-cell non-Hodgkin lymphoma using linear regression approach., Microarray data analysis using R and Bioconductor packages, Vigorous data analysis to identify variant in paired end RNAseq data on non- Hodgkin lymphoma, In-dept study about genes involved in non-Hodgkin lymphoma.<\/p>\n\n\n\n

Materials and Methods<\/strong><\/p>\n\n\n\n

Microarray Data Retrieval<\/strong><\/p>\n\n\n\n

Microarray gene expression data analysis of B- cell non-Hodgkin\u2019s lymphoma was retrieved from GEO database of NCBI. (https:\/\/www.ncbi.nlm.nih.gov\/) with accession ID: GSE132929 . The raw CEL file and CDF file as mentioned in table 1 were selected and downloaded. <\/p>\n\n\n\n

The\ndetails related data under study was shown in the table 1. The series consists\nof gene expression microarray data from non-Hodgkin lymphoma tumour for which\nMD Anderson Cancer Centre\nhave performed targeted DNA sequencing with the 380 gene LymphoSeq panel by\nusing Affymetrix U133 plus 2.0 microarray (HG-U133_Plus_2). Sample type- RNA,\nsource- biopsy, organism- Homo Sapiens, microarray chip- Affymetrix chip,\nplatform ID- GPL570.<\/p>\n\n\n\n

Table 1: list of accession ID, sample name and type of sample lymphoma used under study<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

S.No<\/strong><\/p>\n<\/td>\n

\n

Accession Id<\/strong><\/p>\n<\/td>\n

\n

Sample Name<\/strong><\/p>\n<\/td>\n

\n

Type<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1<\/p>\n<\/td>\n

\n

GSM3896454<\/p>\n<\/td>\n

\n

FL_001<\/p>\n<\/td>\n

\n

Follicular Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

2<\/p>\n<\/td>\n

\n

GSM3896455<\/p>\n<\/td>\n

\n

FL_002<\/p>\n<\/td>\n

\n

Follicular Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

3<\/p>\n<\/td>\n

\n

GSM3896519<\/p>\n<\/td>\n

\n

MCL_001<\/p>\n<\/td>\n

\n

Mantle Cell Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

4<\/p>\n<\/td>\n

\n

GSM3896520<\/p>\n<\/td>\n

\n

MCL_002<\/p>\n<\/td>\n

\n

Mantle Cell Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

5<\/p>\n<\/td>\n

\n

GSM3896563<\/p>\n<\/td>\n

\n

DLBCL_002<\/p>\n<\/td>\n

\n

Diffuse Large B-cell Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

6<\/p>\n<\/td>\n

\n

GSM3896564<\/p>\n<\/td>\n

\n

DLBCL_003<\/p>\n<\/td>\n

\n

Diffuse Large B-cell Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

7<\/p>\n<\/td>\n

\n

GSM3896650<\/p>\n<\/td>\n

\n

HGBL-NOS_016<\/p>\n<\/td>\n

\n

High-grade B-cell Lymphoma Not<\/p>\n

Otherwise Specified<\/p>\n<\/td>\n<\/tr>\n

\n

8<\/p>\n<\/td>\n

\n

GSM3896654<\/p>\n<\/td>\n

\n

HGBL-NOS_017<\/p>\n<\/td>\n

\n

High-grade B-cell Lymphoma Not<\/p>\n

Otherwise Specified<\/p>\n<\/td>\n<\/tr>\n

\n

9<\/p>\n<\/td>\n

\n

GSM3896656<\/p>\n<\/td>\n

\n

BL_001<\/p>\n<\/td>\n

\n

Burkitt’s Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

10<\/p>\n<\/td>\n

\n

GSM3896660<\/p>\n<\/td>\n

\n

BL_002<\/p>\n<\/td>\n

\n

Burkitt’s Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

11<\/p>\n<\/td>\n

\n

GSM3896721<\/p>\n<\/td>\n

\n

MZL_001<\/p>\n<\/td>\n

\n

Medial Zone Lymphoma<\/p>\n<\/td>\n<\/tr>\n

\n

12<\/p>\n<\/td>\n

\n

GSM3896722<\/p>\n<\/td>\n

\n

MZL_002<\/p>\n<\/td>\n

\n

Medial Zone Lymphoma<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Computational\nsoftware\u2019s and tools were used to identify of novel genes expressed in B-cell\nnon- Hodgkin\u2019s lymphoma. Figure 1 shows the workflow used for microarray data\nanalysis and annotation from reading raw files that is CEL file in R workspace\nupto functional enrichment. <\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1: Workflow used for the microarray data analysis to identify differentially expressed genes.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Affy\npackage 12<\/sup> of R and Bioconductor was used. The quality control plots\nwere generated over the raw data using AffyQCReport 13<\/sup> and afflmGUI 14<\/sup>\npackages. Normalization and background correction of CEL files was done using\nRMA function for generating expression set matrix expresso function was used. <\/p>\n\n\n\n

Statistical Analysis<\/strong><\/p>\n\n\n\n

The gene expression profile was analysed to identify differentially expressed genes (DEGs) in B-cell non-Hodgkin\u2019s lymphoma (NHL). DEGs are characterized by statistically significant differences in expression levels or read counts between experimental groups. <\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure <\/strong>2<\/strong>: Linear model codes to identify DEGs using R programing<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

\nThis\nanalysis is crucial for understanding the molecular mechanisms underlying NHL\nand for identifying potential biomarkers or therapeutic targets.conditions. LIMMA\npackage15<\/sup> of R and \n\n<\/p>\n\n\n\n

Bioconductor\nwas used for identification of differentially expressed probe sets. Expression\nestimation was obtained by the linear regression algorithm. An appropriate\ndesign matrix was created, and then linear model fitted to it. A list of top\ngenes differential expressed was retrieved by using top Table function as shown\nin figure 2.<\/p>\n\n\n\n

Gene Set Enrichment Analysis (GSEA)<\/strong><\/p>\n\n\n\n

Differentially\nexpressed genes were annotated for biological significance and analysed for its role in\ndifferent biological pathways . List of differentially expressed genes from\neach set of groups was annotated using DAVID and GO database.<\/p>\n\n\n\n

RNA-Seq data Retrieval<\/strong><\/p>\n\n\n\n

Further study includes RNAseq analysis paired end RNA-seq sequencing data for non-hodgkin\u2019s lymphoma was retrieved from ENA database with accession no.- SRX4624931. Two FASTQ format files were downloaded as mentioned in table 2. <\/p>\n\n\n\n

Table 2: list of FASTQ sequences taken for RNAseq data analysis- study accession ID, sample accession, experiment accession ID, Run accession ID.<\/strong><\/p>\n\n\n\n\n\n\n\n
\n

Study Accession<\/strong><\/p>\n<\/td>\n

\n

Sample<\/strong><\/p>\n

Accession<\/strong><\/p>\n<\/td>\n

\n

Experiment<\/strong><\/p>\n

Accession<\/strong><\/p>\n<\/td>\n

\n

Run<\/strong><\/p>\n

Accession<\/strong><\/p>\n<\/td>\n

\n

FASTQ<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

PRJNA488595<\/strong><\/p>\n<\/td>\n

\n

SAMN09936934<\/p>\n<\/td>\n

\n

SRX4624931<\/p>\n<\/td>\n

\n

SRR7769357<\/p>\n<\/td>\n

\n

SRR7769357_1.fastq.gz<\/p>\n<\/td>\n<\/tr>\n

\n

PRJNA488595<\/strong><\/p>\n<\/td>\n

\n

SAMN09936934<\/p>\n<\/td>\n

\n

SRX4624931<\/p>\n<\/td>\n

\n

SRR7769357<\/p>\n<\/td>\n

\n

SRR7769357_2.fastq.gz<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

The\npaired end sequence for non-Hodgkin\u2019s lymphoma sample was taken from ENA\ndatabase. Instrument model for the sequences was Illumina HiSeq 2000, library\nstrategy: RNA-Seq, library selection: cDNA, organism: Homo sapiens.<\/p>\n\n\n\n

RNA-Seq data Analysis<\/strong><\/p>\n\n\n\n

For\nquality control analysis of FASTQ files, begin by using the FastQC tool\navailable in the Galaxy tools panel 16<\/sup> you can locate it using the\nsearch box. Sequencing errors can skew the analysis and lead to inaccurate data\ninterpretation. Additionally, adapters may be present if the reads are longer\nthan the sequenced fragments, and trimming these adapters can enhance the\nmapping efficiency17<\/sup>. To trim the data, use the Trimmomatic tool in\nGalaxy, which performs various trimming tasks for Illumina paired-end and\nsingle-end data 18<\/sup>. To compile and review the quality control\nresults, use MultiQC to aggregate the outputs from FastQC 19<\/sup>.Steps\nused for RNA- Seq data analysis was shown in figure 3.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 3: workflow used for the RNAseq data analysis from raw files to identification of variants genes.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Alignment to the Reference (mapping)<\/strong><\/p>\n\n\n\n

To\nmap the reads from the input FASTQ file to the reference genome, the RNA STAR\ntool in Galaxy was used 18<\/sup>. In the reference genome tab, select the\noption to use a built-in reference and set it to \u2018hg19\u2019. The output will\ninclude the STAR log file, splice junctions .bed file, and mapped .bam file.\nNext, use MultiQC to aggregate the STAR log results. For visual analysis,\nreview the BAM file using the UCSC Genome Browser and IGV viewer.<\/p>\n\n\n\n

Genetic Variant Calling with Free Bayes<\/strong><\/p>\n\n\n\n

For\nvariant calling, FreeBayes 20 <\/sup>tool of galaxy was used. Input the BAM\nfile and select the reference genome as \u2018hg19\u2019<\/p>\n\n\n\n

Variant Analysis<\/strong><\/p>\n\n\n\n

VCFannotate\n21<\/sup> tool is used to intersect VCF records with BED annotations. As an\ninput VCF file from generated from FreeBayes tool and a BED file to intersect\nwith is provided. And then click on execute. SnpEff eff 22<\/sup> tool is\nused `to annotate variants. As an input VCF file and a pre- built database for\nSnpEff build is provided. And the click on execute. SnpSift Annotate 22<\/sup>\nis typically used to annotate IDs from dbSnp. Variant input file in VCF format\nand VCF File with ID field annotated were given as input. And then click on\nexecute. SnpSift GeneSets22<\/sup> tool is used for annotating GeneSets. As\nan input VCF file from SnpEff Eff tool was given along with the annotation\ndatabase i.e. MSigDB – oncogenic signature gene sets.\nhttp:\/\/www.gseamsigdb.org\/gsea\/downloads.jsp). <\/p>\n\n\n\n

Data Enrichment of RNA-Seq Data<\/strong><\/p>\n\n\n\n

Variant\ngenes were then further annotated for biological intervention and pathway\nanalysis. Annotated list of variants from SnpSift GeneSet was searched against\nGO database.<\/p>\n\n\n\n

Results and Discussions<\/strong><\/p>\n\n\n\n

Microarray Data Analysis and Annotation Results<\/strong><\/p>\n\n\n\n

Quality\ncontrol analysis of microarray data. The quality control analysis was performed\nusing affylmGUI and AffyQCReport packages of R and Bioconductor. The quality\nassessment of affymetrix gene chip data of 6 set (i.e. 12 samples) of CEL files\nfor 6 different types of B-cell non- Hodgkin\u2019s lymphoma were analysed by\nplotting quality control plots.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 4: Boxplot showing the distribution of expression value of each arrays selected for the study. Box plot shows the distribution of gene expression values across different samples<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

In figure 4  shows the  comparison of overall probe intensity readings of all arrays considered under study. Any  variation in array may suggest a potential issue with this particular array and further it is normalized for statistical analysis. <\/p>\n\n\n\n

The figure 5 shows the RNA degradation phots for 12 arrays of 6 different types of B-cell non-Hodgkin\u2019s lymphoma. This RNA degradation plot was computed on normalized data. Each line in the plot represent an array. <\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 5: RNA Degradation Plot for 12 B-cell Non-Hodgkin\u2019s lymphoma arrays.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Figure\n5 shows that diffuse large B-cell lymphoma (DLBCL_02.CEL) shows the highest\nslope value i.e. 6.76 where as Mantle cell lymphoma (MCL_02.CEL) has the\nsmallest slope value i.e. 3.33. The slope is within the recommended range,\nsuggesting that all the samples are of high quality. Additionally, there is a\nstrong correlation among the various arrays in the dataset. <\/p>\n\n\n\n

Figure 6 shows the image array plot of microarray chip plotted using AffylmGUI package of R and Bioconductor. The plot (a, b) Burkitt\u2019s lymphoma , (c, d) Diffuse large B-cell Lymhoma, (e, f) Follicular Lymphoma, (g, h) high grade B-cell lymphoma, (i, j) mantle cell lymphoma and (k,l) medial zone lymphoma. Chip images shows that overall intensity of probes across chips.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 6: image array plot of microarray chip plotted using AffylmGUI package of R and Bioconductor.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Microarray Data Analysis<\/strong><\/p>\n\n\n\n

Linear\nregression algorithm was used for microarray data analysis using LIMMA package\nof R and Bioconductor. LIMMA package provides a platform for the comparative\nanalysis between various RNA targets instantaneously in the complex designed\nexperiment. The Empirical Bayesian methods (i.e. ebayes ()) are very useful in\ngetting a stable set of results even with the small number of arrays. The\nexpression data have log- ratio M for the two-colour array platform.<\/p>\n\n\n\n

Firstly,\nto fit the linear model to the microarray expression data to each sample\ngroups. For this the matrix was designed and 6 set of groups were made\naccording to the type of B-cell non-Hodgkin\u2019s lymphoma that is Group\n1-Burkitt\u2019s lymphoma (BL), Group 2- diffuse large B-cell lymphoma (DLBCL), Group\n3- follicular lymphoma, Group 4 – High- grade B-cell Lymphoma (HGBL-NOS), Group\n5- Mantle Cell Lymphoma (MCL) and Group 6- Medial Zone Lymphoma (MZL). The\ncontrast matrix was created between the pair of groups: Group 2-1, Group 3-2, Group\n4-3, Group 5-4, Group 6-5 and Group 6-1. The contrasts Fit method was linear\nmodel was applied over the contrast matrix. Finally, Empirical Bayes method was\napplied for computing the logFC value, average log2- expression, moderated\nt-statistics, adjusted p-value and B-statistic. Then the top Table method was\nused to execute the top 10 ranked differentially expressed genes from the two\ndatasets<\/p>\n\n\n\n

Table 3: top 5 Differentially Expressed Genes between Group 1-Burkitt\u2019s lymphoma (BL) and Group 2- diffuse large B-cell lymphoma (DLBCL).<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

gene ID<\/strong><\/p>\n<\/td>\n

\n

Gene name<\/strong><\/p>\n<\/td>\n

\n

logFC<\/strong><\/p>\n<\/td>\n

\n

AveExpr<\/strong><\/p>\n<\/td>\n

\n

t<\/strong><\/p>\n<\/td>\n

\n

P. Value<\/strong><\/p>\n<\/td>\n

\n

adj.P.Val<\/strong><\/p>\n<\/td>\n

\n

B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

205000_at<\/p>\n<\/td>\n

\n

DDX3Y<\/p>\n<\/td>\n

\n

-5.88593<\/p>\n<\/td>\n

\n

6.157944<\/p>\n<\/td>\n

\n

-13.0315<\/p>\n<\/td>\n

\n

3.83E-07<\/p>\n<\/td>\n

\n

0.010476<\/p>\n<\/td>\n

\n

5.243563<\/p>\n<\/td>\n<\/tr>\n

\n

212489_at<\/p>\n<\/td>\n

\n

COL5A1<\/p>\n<\/td>\n

\n

-3.25426<\/p>\n<\/td>\n

\n

3.667428<\/p>\n<\/td>\n

\n

-11.141<\/p>\n<\/td>\n

\n

1.46E-06<\/p>\n<\/td>\n

\n

0.012645<\/p>\n<\/td>\n

\n

4.522844<\/p>\n<\/td>\n<\/tr>\n

\n

224590_at<\/p>\n<\/td>\n

\n

XIST<\/p>\n<\/td>\n

\n

5.197332<\/p>\n<\/td>\n

\n

3.602337<\/p>\n<\/td>\n

\n

11.10209<\/p>\n<\/td>\n

\n

1.50E-06<\/p>\n<\/td>\n

\n

0.012645<\/p>\n<\/td>\n

\n

4.505694<\/p>\n<\/td>\n<\/tr>\n

\n

202311_s_at<\/p>\n<\/td>\n

\n

COL1A1<\/p>\n<\/td>\n

\n

-4.59185<\/p>\n<\/td>\n

\n

3.52187<\/p>\n<\/td>\n

\n

-10.7873<\/p>\n<\/td>\n

\n

1.91E-06<\/p>\n<\/td>\n

\n

0.012645<\/p>\n<\/td>\n

\n

4.362945<\/p>\n<\/td>\n<\/tr>\n

\n

1552787_at<\/p>\n<\/td>\n

\n

HELB<\/p>\n<\/td>\n

\n

2.641911<\/p>\n<\/td>\n

\n

3.789506<\/p>\n<\/td>\n

\n

10.59467<\/p>\n<\/td>\n

\n

2.22E-06<\/p>\n<\/td>\n

\n

0.012645<\/p>\n<\/td>\n

\n

4.272042<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 4: top 5 Differentially Expressed Genes Group 2- diffuse large B-cell lymphoma (DLBCL) and Group 3- follicular lymphoma,<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

gene ID<\/strong><\/p>\n<\/td>\n

\n

gene name<\/strong><\/p>\n<\/td>\n

\n

logFC<\/strong><\/p>\n<\/td>\n

\n

AveExpr<\/strong><\/p>\n<\/td>\n

\n

t<\/strong><\/p>\n<\/td>\n

\n

P.Value<\/strong><\/p>\n<\/td>\n

\n

adj.P.Val<\/strong><\/p>\n<\/td>\n

\n

B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

205000_at<\/p>\n<\/td>\n

\n

DDX3Y<\/p>\n<\/td>\n

\n

5.929566<\/p>\n<\/td>\n

\n

6.157944<\/p>\n<\/td>\n

\n

13.12814<\/p>\n<\/td>\n

\n

3.60E-07<\/p>\n<\/td>\n

\n

0.019665<\/p>\n<\/td>\n

\n

-2.81698<\/p>\n<\/td>\n<\/tr>\n

\n

236694_at<\/p>\n<\/td>\n

\n

TXLNGY<\/p>\n<\/td>\n

\n

4.810079<\/p>\n<\/td>\n

\n

5.333022<\/p>\n<\/td>\n

\n

11.63416<\/p>\n<\/td>\n

\n

1.01E-06<\/p>\n<\/td>\n

\n

0.019708<\/p>\n<\/td>\n

\n

-2.84956<\/p>\n<\/td>\n<\/tr>\n

\n

201909_at<\/p>\n<\/td>\n

\n

RPS4Y1<\/p>\n<\/td>\n

\n

5.565347<\/p>\n<\/td>\n

\n

9.05931<\/p>\n<\/td>\n

\n

11.3349<\/p>\n<\/td>\n

\n

1.26E-06<\/p>\n<\/td>\n

\n

0.019708<\/p>\n<\/td>\n

\n

-2.85752<\/p>\n<\/td>\n<\/tr>\n

\n

224588_at<\/p>\n<\/td>\n

\n

XIST<\/p>\n<\/td>\n

\n

-7.67613<\/p>\n<\/td>\n

\n

5.520689<\/p>\n<\/td>\n

\n

-11.1535<\/p>\n<\/td>\n

\n

1.44E-06<\/p>\n<\/td>\n

\n

0.019708<\/p>\n<\/td>\n

\n

-2.86262<\/p>\n<\/td>\n<\/tr>\n

\n

224590_at<\/p>\n<\/td>\n

\n

XIST<\/p>\n<\/td>\n

\n

-5.06838<\/p>\n<\/td>\n

\n

3.602337<\/p>\n<\/td>\n

\n

-10.8266<\/p>\n<\/td>\n

\n

1.85E-06<\/p>\n<\/td>\n

\n

0.020256<\/p>\n<\/td>\n

\n

-2.8724<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 5: top 5 Differentially Expressed Genes between Group 3- follicular lymphoma and Group 4 – High- grade B-cell Lymphoma (HGBL-NOS)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

gene ID<\/strong><\/p>\n<\/td>\n

\n

gene name<\/strong><\/p>\n<\/td>\n

\n

logFC<\/strong><\/p>\n<\/td>\n

\n

AveExpr<\/strong><\/p>\n<\/td>\n

\n

t<\/strong><\/p>\n<\/td>\n

\n

P.Value<\/strong><\/p>\n<\/td>\n

\n

adj.P.Val<\/strong><\/p>\n<\/td>\n

\n

B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

236694_at<\/p>\n<\/td>\n

\n

TXLNGY<\/p>\n<\/td>\n

\n

5.478579<\/p>\n<\/td>\n

\n

5.333022<\/p>\n<\/td>\n

\n

13.25107<\/p>\n<\/td>\n

\n

3.32E-07<\/p>\n<\/td>\n

\n

0.015375<\/p>\n<\/td>\n

\n

3.576298<\/p>\n<\/td>\n<\/tr>\n

\n

205000_at<\/p>\n<\/td>\n

\n

DDX3Y<\/p>\n<\/td>\n

\n

5.628086<\/p>\n<\/td>\n

\n

6.157944<\/p>\n<\/td>\n

\n

12.46066<\/p>\n<\/td>\n

\n

5.62E-07<\/p>\n<\/td>\n

\n

0.015375<\/p>\n<\/td>\n

\n

3.408812<\/p>\n<\/td>\n<\/tr>\n

\n

201909_at<\/p>\n<\/td>\n

\n

RPS4Y1<\/p>\n<\/td>\n

\n

5.545314<\/p>\n<\/td>\n

\n

9.05931<\/p>\n<\/td>\n

\n

11.2941<\/p>\n<\/td>\n

\n

1.30E-06<\/p>\n<\/td>\n

\n

0.023638<\/p>\n<\/td>\n

\n

3.114081<\/p>\n<\/td>\n<\/tr>\n

\n

207245_at<\/p>\n<\/td>\n

\n

UGT2B17<\/p>\n<\/td>\n

\n

3.912282<\/p>\n<\/td>\n

\n

2.877049<\/p>\n<\/td>\n

\n

10.50753<\/p>\n<\/td>\n

\n

2.38E-06<\/p>\n<\/td>\n

\n

0.032547<\/p>\n<\/td>\n

\n

2.876181<\/p>\n<\/td>\n<\/tr>\n

\n

236302_at<\/p>\n<\/td>\n

\n

PPM1E<\/p>\n<\/td>\n

\n

-3.56836<\/p>\n<\/td>\n

\n

3.611297<\/p>\n<\/td>\n

\n

-9.52999<\/p>\n<\/td>\n

\n

5.36E-06<\/p>\n<\/td>\n

\n

0.058296<\/p>\n<\/td>\n

\n

2.525727<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 6: top 5 Differentially Expressed Genes between Group 4 – High- grade B-cell Lymphoma (HGBL-NOS) and Group 5- Mantle Cell Lymphoma (MCL)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

gene ID<\/strong><\/p>\n<\/td>\n

\n

gene name<\/strong><\/p>\n<\/td>\n

\n

logFC<\/strong><\/p>\n<\/td>\n

\n

AveExpr<\/strong><\/p>\n<\/td>\n

\n

t<\/strong><\/p>\n<\/td>\n

\n

P.Value<\/strong><\/p>\n<\/td>\n

\n

adj.P.Val<\/strong><\/p>\n<\/td>\n

\n

B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

205000_at<\/p>\n<\/td>\n

\n

DDX3Y<\/p>\n<\/td>\n

\n

-5.31837<\/p>\n<\/td>\n

\n

6.157944<\/p>\n<\/td>\n

\n

-11.775<\/p>\n<\/td>\n

\n

9.11E-07<\/p>\n<\/td>\n

\n

0.034027<\/p>\n<\/td>\n

\n

5.194026<\/p>\n<\/td>\n<\/tr>\n

\n

208711_s_at<\/p>\n<\/td>\n

\n

CCND1<\/p>\n<\/td>\n

\n

-4.94661<\/p>\n<\/td>\n

\n

5.602951<\/p>\n<\/td>\n

\n

-10.8168<\/p>\n<\/td>\n

\n

1.87E-06<\/p>\n<\/td>\n

\n

0.034027<\/p>\n<\/td>\n

\n

4.733261<\/p>\n<\/td>\n<\/tr>\n

\n

203509_at<\/p>\n<\/td>\n

\n

SORL1<\/p>\n<\/td>\n

\n

4.687515<\/p>\n<\/td>\n

\n

8.648969<\/p>\n<\/td>\n

\n

10.47631<\/p>\n<\/td>\n

\n

2.44E-06<\/p>\n<\/td>\n

\n

0.034027<\/p>\n<\/td>\n

\n

4.553245<\/p>\n<\/td>\n<\/tr>\n

\n

201909_at<\/p>\n<\/td>\n

\n

RPS4Y1<\/p>\n<\/td>\n

\n

-4.96028<\/p>\n<\/td>\n

\n

9.05931<\/p>\n<\/td>\n

\n

-10.1026<\/p>\n<\/td>\n

\n

3.31E-06<\/p>\n<\/td>\n

\n

0.034027<\/p>\n<\/td>\n

\n

4.344845<\/p>\n<\/td>\n<\/tr>\n

\n

225046_at<\/p>\n<\/td>\n

\n

LOC102724951<\/p>\n<\/td>\n

\n

-3.11704<\/p>\n<\/td>\n

\n

4.842963<\/p>\n<\/td>\n

\n

-10.0359<\/p>\n<\/td>\n

\n

3.49E-06<\/p>\n<\/td>\n

\n

0.034027<\/p>\n<\/td>\n

\n

4.306405<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 7: top 5 Differentially Expressed Genes between Group 5- Mantle Cell Lymphoma (MCL) and Group 6- Medial Zone Lymphoma (MZL). <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

gene ID<\/strong><\/p>\n<\/td>\n

\n

gene name<\/strong><\/p>\n<\/td>\n

\n

logFC<\/strong><\/p>\n<\/td>\n

\n

AveExpr<\/strong><\/p>\n<\/td>\n

\n

t<\/strong><\/p>\n<\/td>\n

\n

P.Value<\/strong><\/p>\n<\/td>\n

\n

adj.P.Val<\/strong><\/p>\n<\/td>\n

\n

B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1558697_a_at<\/p>\n<\/td>\n

\n

KIAA0430<\/p>\n<\/td>\n

\n

-2.52147<\/p>\n<\/td>\n

\n

3.380706<\/p>\n<\/td>\n

\n

-10.2835<\/p>\n<\/td>\n

\n

2.85E-06<\/p>\n<\/td>\n

\n

0.142036<\/p>\n<\/td>\n

\n

2.919754<\/p>\n<\/td>\n<\/tr>\n

\n

208711_s_at<\/p>\n<\/td>\n

\n

CCND1<\/p>\n<\/td>\n

\n

4.221763<\/p>\n<\/td>\n

\n

5.602951<\/p>\n<\/td>\n

\n

9.231765<\/p>\n<\/td>\n

\n

6.97E-06<\/p>\n<\/td>\n

\n

0.142036<\/p>\n<\/td>\n

\n

2.509209<\/p>\n<\/td>\n<\/tr>\n

\n

235643_at<\/p>\n<\/td>\n

\n

SAMD9L<\/p>\n<\/td>\n

\n

-2.97355<\/p>\n<\/td>\n

\n

5.655163<\/p>\n<\/td>\n

\n

-8.51194<\/p>\n<\/td>\n

\n

1.35E-05<\/p>\n<\/td>\n

\n

0.142036<\/p>\n<\/td>\n

\n

2.175097<\/p>\n<\/td>\n<\/tr>\n

\n

223185_s_at<\/p>\n<\/td>\n

\n

BHLHE41<\/p>\n<\/td>\n

\n

-3.07238<\/p>\n<\/td>\n

\n

3.202969<\/p>\n<\/td>\n

\n

-8.48091<\/p>\n<\/td>\n

\n

1.39E-05<\/p>\n<\/td>\n

\n

0.142036<\/p>\n<\/td>\n

\n

2.159586<\/p>\n<\/td>\n<\/tr>\n

\n

235802_at<\/p>\n<\/td>\n

\n

PLD4<\/p>\n<\/td>\n

\n

-2.81842<\/p>\n<\/td>\n

\n

3.997129<\/p>\n<\/td>\n

\n

-8.24667<\/p>\n<\/td>\n

\n

1.74E-05<\/p>\n<\/td>\n

\n

0.142036<\/p>\n<\/td>\n

\n

2.03932<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 8: top 5 Differentially Expressed Genes between Group 6- Medial Zone Lymphoma (MZL) and Group 1-Burkitt\u2019s lymphoma (BL) <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\n

gene ID<\/strong><\/p>\n<\/td>\n

\n

Gene Name<\/strong><\/p>\n<\/td>\n

\n

logFC<\/strong><\/p>\n<\/td>\n

\n

AveExpr<\/strong><\/p>\n<\/td>\n

\n

t<\/strong><\/p>\n<\/td>\n

\n

P.Value<\/strong><\/p>\n<\/td>\n

\n

adj.P.Val<\/strong><\/p>\n<\/td>\n

\n

B<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

201710_at<\/p>\n<\/td>\n

\n

MYBL2<\/p>\n<\/td>\n

\n

-3.60436<\/p>\n<\/td>\n

\n

7.591107<\/p>\n<\/td>\n

\n

-12.7644<\/p>\n<\/td>\n

\n

4.58E-07<\/p>\n<\/td>\n

\n

0.00988<\/p>\n<\/td>\n

\n

5.859247<\/p>\n<\/td>\n<\/tr>\n

\n

202729_s_at<\/p>\n<\/td>\n

\n

LTBP1<\/p>\n<\/td>\n

\n

-3.2543<\/p>\n<\/td>\n

\n

6.373022<\/p>\n<\/td>\n

\n

-12.3197<\/p>\n<\/td>\n

\n

6.20E-07<\/p>\n<\/td>\n

\n

0.00988<\/p>\n<\/td>\n

\n

5.667491<\/p>\n<\/td>\n<\/tr>\n

\n

212489_at<\/p>\n<\/td>\n

\n

COL5A1<\/p>\n<\/td>\n

\n

-3.53426<\/p>\n<\/td>\n

\n

3.667428<\/p>\n<\/td>\n

\n

-12.0996<\/p>\n<\/td>\n

\n

7.23E-07<\/p>\n<\/td>\n

\n

0.00988<\/p>\n<\/td>\n

\n

5.568211<\/p>\n<\/td>\n<\/tr>\n

\n

203589_s_at<\/p>\n<\/td>\n

\n

TFDP2<\/p>\n<\/td>\n

\n

-2.9582<\/p>\n<\/td>\n

\n

4.952128<\/p>\n<\/td>\n

\n

-11.4574<\/p>\n<\/td>\n

\n

1.15E-06<\/p>\n<\/td>\n

\n

0.01256<\/p>\n<\/td>\n

\n

5.260737<\/p>\n<\/td>\n<\/tr>\n

\n

202311_s_at<\/p>\n<\/td>\n

\n

COL1A1<\/p>\n<\/td>\n

\n

-4.70515<\/p>\n<\/td>\n

\n

3.52187<\/p>\n<\/td>\n

\n

-11.0534<\/p>\n<\/td>\n

\n

1.56E-06<\/p>\n<\/td>\n

\n

0.014176<\/p>\n<\/td>\n

\n

5.05276<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

The\ntable-3,4,5,6,7,8 are the result of the top 10 differentially expressed gene in\ngroup 2-1, group 3-2, group 4-3, group 5-4, group 6-5 and group 6-1\nrespectively. Each table gives the list of geneID of the differentially\nexpressed genes, the logFC , AveExpr, t value, pvalue, adj. pvalue, B value for\neach gene. LogFC is the estimation of the log2 fold change corresponding to the\ncontrasts. AveExpr is the average log2 expression for probes. t is the\nmoderated t-statistic, pvalue is the cutoff value for adjusted p-values , the\ngenes having lower p-values are listed. B is the log-odds that gene is\ndifferentially expressed.<\/p>\n\n\n\n

Functional Annotation and Enrichment Analysis of DEGs<\/strong><\/p>\n\n\n\n

The\nfunctional annotation and data enrichment were done over each set of top10\ndifferentially expressed genes enlisted by using limma package as shown in\ntable- 3,4,5,6,7,8. The gene IDs were annotated using the DAVID database to\nobtain the corresponding gene symbols. These gene symbols were then further\nannotated with the Gene Ontology database.<\/p>\n\n\n\n

Pathway Enrichment<\/strong><\/p>\n\n\n\n

Pathway enrichment was done to annotated which gene was involved in the cancer causing pathways, so that such information can be used in designing target specific drugs. Pathway analysis was done over each set of differentially expressed gene. Out of which the most effective result was seen in the group 5-4 (table-6). The genes expressed in this group were DDX3Y, CCND1, SORL1, RPS4Y1, LOC102724951, KRAS, FGD6, DNMT3A, KNL1, TXLNGY. All gene were identified in GO database for the pathway enrichment analysis table 9 shows the list of  Gene Ontology pathways along with gene names that are annotated.  <\/p>\n\n\n\n

Table 9: List of genes and the pathway involved annotated using GO database<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

S.No<\/p>\n<\/td>\n

\n

GO Pathway<\/p>\n<\/td>\n

\n

Gene name<\/p>\n<\/td>\n<\/tr>\n

\n

1<\/p>\n<\/td>\n

\n

Angiogenesis (P00005)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

2<\/p>\n<\/td>\n

\n

EGF receptor signaling pathway (P00018)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

3<\/p>\n<\/td>\n

\n

FGF signaling pathway (P00021)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

4<\/p>\n<\/td>\n

\n

Integrin signaling pathway (P00034)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

5<\/p>\n<\/td>\n

\n

PDGF signaling pathway (P00047)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

6<\/p>\n<\/td>\n

\n

PI3 kinase pathway (P00048)<\/p>\n<\/td>\n

\n

KRAS, CCDN1<\/p>\n<\/td>\n<\/tr>\n

\n

7<\/p>\n<\/td>\n

\n

Ras Pathway (P04393)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

8<\/p>\n<\/td>\n

\n

TGF-beta signaling pathway (P00052)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

9<\/p>\n<\/td>\n

\n

VEGF signaling pathway (P00056)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

10<\/p>\n<\/td>\n

\n

p53 pathway (P04398)<\/p>\n<\/td>\n

\n

KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

11<\/p>\n<\/td>\n

\n

CCKR signaling map (P06959)<\/p>\n<\/td>\n

\n

CCDN1<\/p>\n<\/td>\n<\/tr>\n

\n

12<\/p>\n<\/td>\n

\n

Cell cycle (P00013)<\/p>\n<\/td>\n

\n

CCDN1<\/p>\n<\/td>\n<\/tr>\n

\n

13<\/p>\n<\/td>\n

\n

Wnt signaling pathway (P00057)<\/p>\n<\/td>\n

\n

CCDN1<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 7: Pie chart showing pathway enrichment of differentially expressed genes of group 5-4<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

As\nshown in the table 9, all total 14 pathways were found during pathway\nenrichment using GO database. These pathways were Angiogenesis (P00005), EGF\nreceptor signalling pathway (P00018), FGF signalling pathway (P00021), Integrin\nsignalling pathway (P00034), PDGF signalling pathway (P00047), PI3 kinase\npathway (P00048), Ras Pathway (P04393), TGF-beta signalling pathway (P00052),\nVEGF signalling pathway (P00056), p53 pathway feedback loops 2 (P04398), CCKR signalling\nmap (P06959), Cell cycle (P00013), Wnt signalling pathway (P00057) as shown in\nfigure-7. Out of 10 genes which were annotated only 2 genes were found involved\nin these 14 pathways. These genes were KRAS proto-oncogene, GTPase (KRAS) and\ncyclin D1(CCND1). KRAS gene was involved in 11 out of 14 pathways where as\nCCDN1 gene was involved in 4 out of 14 pathways. It was seen that KRAS gene was\ninvolved in most of the cancer-causing pathways like PI3 kinase pathway\n(P00048), EGF receptor signalling pathway (P00018), p53 pathway (P04398),\nTGF-beta signalling pathway (P00052), Ras Pathway (P04393).<\/p>\n\n\n\n

RNA-Seq Data Analysis and Annotation Results<\/strong><\/p>\n\n\n\n

The\nquality control analysis of the RNA-Seq data was conducted on two raw\npaired-end Fastq files of non-Hodgkin\u2019s lymphoma obtained from the ENA\ndatabase. The FastQC tool from Galaxy was employed for assessing the quality of\nthe RNA-Seq data. FastQC generates basic text and HTML reports that include\nquality control plots covering various metrics such as basic statistics, per\nbase sequence quality, per sequence quality scores, per base sequence content,\nper base GC content, per sequence GC content, per base N content, sequence\nlength distribution, sequence duplication levels, overrepresented sequences,\nand kmer content. Subsequently, MultiQC was used to consolidate the results\nfrom the two FastQC analyses into a single report FastQC result was shown in Figure\n8.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 8: Quality Control plots by MultiQC tool, FastQC sequence counts shows the number of unique and duplicate reads in both samples.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Variant Data Analysis and Annotation<\/strong><\/p>\n\n\n\n

The\nvariant data analysis was done using Galaxy tools. Firstly, FreeBayes tool was\nused to generate VCF file over the raw Fastq data. Then VCFannotat tool was\nused to intersect VCF records with BED annotation. Further annotation was done\nusing SnpEff eff tool. And finally, SnpSift Geneset tool was used to add\nannotation from oncogenic signature gene sets of MSigDB. As shown in the table 10,\nthe number variants in SNP were 216,177 and 212,767 in seq1 and seq2\nrespectively. <\/p>\n\n\n\n

Table 10: Number variants in seq1 and seq2 by type of mutations<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Type<\/p>\n<\/td>\n

\n

Total (seq1)<\/p>\n<\/td>\n

\n

Total (seq2)<\/p>\n<\/td>\n<\/tr>\n

\n

SNP<\/p>\n<\/td>\n

\n

216,177<\/p>\n<\/td>\n

\n

212,767<\/p>\n<\/td>\n<\/tr>\n

\n

MNP<\/p>\n<\/td>\n

\n

16,969<\/p>\n<\/td>\n

\n

17,025<\/p>\n<\/td>\n<\/tr>\n

\n

INS<\/p>\n<\/td>\n

\n

6,881<\/p>\n<\/td>\n

\n

7,531<\/p>\n<\/td>\n<\/tr>\n

\n

DEL<\/p>\n<\/td>\n

\n

7,585<\/p>\n<\/td>\n

\n

7,439<\/p>\n<\/td>\n<\/tr>\n

\n

MIXED<\/p>\n<\/td>\n

\n

548<\/p>\n<\/td>\n

\n

542<\/p>\n<\/td>\n<\/tr>\n

\n

INV<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

DUP<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

BND<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

INTERVAL<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Total<\/p>\n<\/td>\n

\n

248,160<\/p>\n<\/td>\n

\n

245,304<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

It\nwas seen that there were more MNP number variants in seq2 than seq1 that is MNP\nfor seq2 was 17,025 whereas that for seq1 was 16,969. Insertion variants\naccounts 6,881 for seq1 and 7,531 foe seq2. Deletion variants in seq1 was 7,585\nand for seq2 it was 7,439. Mixed variants were 548 and 542 for seq1 and seq2\nrespectively. Therefore, the total number variants in seq1 were 248,160 and\nthat for seq2 it was 245,304. Table 11 shows the number of variants by\nfunctional class that is missense, nonsense and silent genes count along with their\npercentage for each sequence is shown in the table 11.<\/p>\n\n\n\n

Table 11: Number of effects by functional class Missense, Nonsense and Silent mutation along with count and percentage values<\/strong><\/p>\n\n\n\n\n\n\n\n\n
\n

Type<\/strong><\/p>\n<\/td>\n

\n

Count1<\/strong><\/p>\n<\/td>\n

\n

Percent1<\/strong><\/p>\n<\/td>\n

\n

Count2<\/strong><\/p>\n<\/td>\n

\n

Percent2<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

MISSENSE<\/p>\n<\/td>\n

\n

14,581<\/p>\n<\/td>\n

\n

49.546%<\/p>\n<\/td>\n

\n

10,664<\/p>\n<\/td>\n

\n

44.884%<\/p>\n<\/td>\n<\/tr>\n

\n

NONSENSE<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

0.656%<\/p>\n<\/td>\n

\n

143<\/p>\n<\/td>\n

\n

0.602%<\/p>\n<\/td>\n<\/tr>\n

\n

SILENT<\/p>\n<\/td>\n

\n

14,655<\/p>\n<\/td>\n

\n

49.798%<\/p>\n<\/td>\n

\n

12,952<\/p>\n<\/td>\n

\n

54.514%<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 9: bar plot shows the percentage of variations in various genomic regions.<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

By\nthe analysis of figure 9, it was seen that there is maximum variation that is\nabove 50% was seen in the intron region of the sequence. About 6-8% variation\nwas in exon regions\nof the sequence. There were relatively lesser number of variations in the up\nregulated genes than the down-regulating genes. There were lesser number of\nvariations in untranslated region (UTR) at the 5\u2019 end than at the 3\u2019 end.  <\/p>\n\n\n\n

The\nSnpSift GeneSets tool is used to add annotations from MSigDB, a collection of\nannotated gene sets from different sources including Gene Ontology (GO), KEGG,\nReactome. Table 12 shows the result of Snpsift Geneset result, list of up and\ndown regulated genes, the Geneset size and the variation in them with respect\nto the reference oncogenic signature gene sets of MSigDB. Result shows that\nthere were 189 gene sets and 10913 genes.<\/p>\n\n\n\n

Table 12: List of UP and Down regulated variant genes along with count value of Geneset size and the variation.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Gene_Set<\/strong><\/p>\n<\/td>\n

\n

Gene_Set_Size<\/strong><\/p>\n<\/td>\n

\n

Variants<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

AKT_UP.V1_DN<\/p>\n<\/td>\n

\n

187<\/p>\n<\/td>\n

\n

1906<\/p>\n<\/td>\n<\/tr>\n

\n

AKT_UP.V1_UP<\/p>\n<\/td>\n

\n

169<\/p>\n<\/td>\n

\n

2014<\/p>\n<\/td>\n<\/tr>\n

\n

ATF2_S_UP.V1_DN<\/p>\n<\/td>\n

\n

187<\/p>\n<\/td>\n

\n

2035<\/p>\n<\/td>\n<\/tr>\n

\n

ATF2_S_UP.V1_UP<\/p>\n<\/td>\n

\n

192<\/p>\n<\/td>\n

\n

1656<\/p>\n<\/td>\n<\/tr>\n

\n

KRAS.300_UP.V1_DN<\/p>\n<\/td>\n

\n

140<\/p>\n<\/td>\n

\n

905<\/p>\n<\/td>\n<\/tr>\n

\n

KRAS.300_UP.V1_UP<\/p>\n<\/td>\n

\n

142<\/p>\n<\/td>\n

\n

1134<\/p>\n<\/td>\n<\/tr>\n

\n

PTEN_DN.V1_DN<\/p>\n<\/td>\n

\n

184<\/p>\n<\/td>\n

\n

1300<\/p>\n<\/td>\n<\/tr>\n

\n

PTEN_DN.V1_UP<\/p>\n<\/td>\n

\n

186<\/p>\n<\/td>\n

\n

1205<\/p>\n<\/td>\n<\/tr>\n

\n

P53_DN.V1_DN<\/p>\n<\/td>\n

\n

194<\/p>\n<\/td>\n

\n

1890<\/p>\n<\/td>\n<\/tr>\n

\n

P53_DN.V1_UP<\/p>\n<\/td>\n

\n

194<\/p>\n<\/td>\n

\n

1935<\/p>\n<\/td>\n<\/tr>\n

\n

RAF_UP.V1_DN<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

2578<\/p>\n<\/td>\n<\/tr>\n

\n

RAF_UP.V1_UP<\/p>\n<\/td>\n

\n

193<\/p>\n<\/td>\n

\n

2154<\/p>\n<\/td>\n<\/tr>\n

\n

E2F3_UP.V1_DN<\/p>\n<\/td>\n

\n

161<\/p>\n<\/td>\n

\n

1787<\/p>\n<\/td>\n<\/tr>\n

\n

E2F3_UP.V1_UP<\/p>\n<\/td>\n

\n

191<\/p>\n<\/td>\n

\n

2696<\/p>\n<\/td>\n<\/tr>\n

\n

MYC_UP.V1_DN<\/p>\n<\/td>\n

\n

172<\/p>\n<\/td>\n

\n

1538<\/p>\n<\/td>\n<\/tr>\n

\n

MYC_UP.V1_UP<\/p>\n<\/td>\n

\n

181<\/p>\n<\/td>\n

\n

2607<\/p>\n<\/td>\n<\/tr>\n

\n

MTOR_UP.V1_DN<\/p>\n<\/td>\n

\n

181<\/p>\n<\/td>\n

\n

1878<\/p>\n<\/td>\n<\/tr>\n

\n

MTOR_UP.V1_UP<\/p>\n<\/td>\n

\n

169<\/p>\n<\/td>\n

\n

2625<\/p>\n<\/td>\n<\/tr>\n

\n

CYCLIN_D1_UP.V1_DN<\/p>\n<\/td>\n

\n

190<\/p>\n<\/td>\n

\n

2021<\/p>\n<\/td>\n<\/tr>\n

\n

CYCLIN_D1_UP.V1_UP<\/p>\n<\/td>\n

\n

187<\/p>\n<\/td>\n

\n

1650<\/p>\n<\/td>\n<\/tr>\n

\n

RB_DN.V1_DN<\/p>\n<\/td>\n

\n

123<\/p>\n<\/td>\n

\n

1578<\/p>\n<\/td>\n<\/tr>\n

\n

RB_DN.V1_UP ATK PTEN<\/p>\n<\/td>\n

\n

117<\/p>\n<\/td>\n

\n

1655<\/p>\n<\/td>\n<\/tr>\n

\n

ATM_DN.V1_DN<\/p>\n<\/td>\n

\n

146<\/p>\n<\/td>\n

\n

930<\/p>\n<\/td>\n<\/tr>\n

\n

ATM_DN.V1_UP<\/p>\n<\/td>\n

\n

146<\/p>\n<\/td>\n

\n

1136<\/p>\n<\/td>\n<\/tr>\n

\n

ERBB2_UP.V1_DN<\/p>\n<\/td>\n

\n

197<\/p>\n<\/td>\n

\n

4182<\/p>\n<\/td>\n<\/tr>\n

\n

ERBB2_UP.V1_UP<\/p>\n<\/td>\n

\n

190<\/p>\n<\/td>\n

\n

1896<\/p>\n<\/td>\n<\/tr>\n

\n

RELA_DN.V1_DN<\/p>\n<\/td>\n

\n

139<\/p>\n<\/td>\n

\n

1018<\/p>\n<\/td>\n<\/tr>\n

\n

RELA_DN.V1_UP<\/p>\n<\/td>\n

\n

149<\/p>\n<\/td>\n

\n

1302<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Visualisation of Variants<\/strong><\/p>\n\n\n\n

Visual\nanalysis of the variants (VCF file) was done using Vcf.iobio, UCSC browser,\nwhich is integrated with the galaxy, which enable the user to analysis large\nset of datasets.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 10: Vcf.iobio resulting view on the dataset 1<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 11: Vcf.iobio resulting view on the dataset 2<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

As\nshown in the figure- 10 and 11, vcf.iobio shows high level variant calling\n(VCF) metrics in real time. The above figures show the variant density plot,\nTs\/Tv ratio plots, base changes plots, variant types, insertion & deletion\nlength plot and variant quality plots.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 12: variant calling of KRAS gene shown in VCF result annotation in UCSC genome browser<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

As\nshown in figure- 12, variant alignment of KRAS gene was viewed on the VCF\nresults from the SnpSift GeneSet tool at UCSC genome browser. This alignment\nshows that the variation of KRAS gene located on chromosome number 12 (ch12).\n22 set of variation was shown. Out of which 6 variation were in UTR between\nT-C, A-C and G-C. In the intronic region 3 variants were show between C-T.\nwhile there were 5 missense variant between G-A. These variants were analysed\nusing the dbSNP database.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 13: variant calling of RAF gene shown in VCF result annotation in UCSC genome browser<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

As\nshown in figure- 13, variant alignment of RAF gene was viewed on the VCF\nresults from the SnpSift GeneSet tool at UCSC genome browser. This alignment\nshows that the variation of RAF gene located on chromosome number 3 (chr 3). 29\nset of variants were shown(figure-10). Out of which 6 variation were in 3\u2019UTR\nbetween T-C, C-T and GAAA-CAAT. About 3 synonymous variants were show between\nACT-GCC. These variants were analysed using the dbSNP database.<\/p>\n\n\n\n

Functional annotation and Pathway Enrichment of variant genes<\/strong><\/p>\n\n\n\n

The\nfunctional annotation and data enrichment were done over the annotated list of\nvariant genes enlisted in table 12 by using SnpSift GeneSets tool of galaxy.\nThe geneID were annotated in the GO database. Pathway enrichment was done to\nannotated which gene was involved in the cancer-causing pathways, so that such\ninformation can be used in designing target specific drugs. Pathway enrichment\nwas performed over 189 variant gene. Out of these 189 genes 13 genes were found\ninvolved in the cancer-causing pathways. These 15 genes were MTOR, PKCA, JAK2,\nCCND1, ATF2, KRAS, RAF, RB, E2F3, PTEN, P53, ATM, MYC, ERBB2, RELA. The table\nbelow shows the list of various pathways in which these genes are involved.<\/p>\n\n\n\n

Table 13: List of variant genes and the pathway involved annotated using GO database.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

S.No<\/strong><\/p>\n<\/td>\n

\n

Pathways<\/strong><\/p>\n<\/td>\n

\n

Genes<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1<\/p>\n<\/td>\n

\n

Apoptosis signaling pathway (P00006)<\/p>\n<\/td>\n

\n

ATF2, PKCA, P53, RELA<\/p>\n<\/td>\n<\/tr>\n

\n

2<\/p>\n<\/td>\n

\n

EGF receptor signaling pathway (P00018)<\/p>\n<\/td>\n

\n

PKCA, RAF, ERBB2, KRAS<\/p>\n<\/td>\n<\/tr>\n

\n

3<\/p>\n<\/td>\n

\n

FGF signaling pathway (P00021)<\/p>\n<\/td>\n

\n

PKCA, KRAS, RAF<\/p>\n<\/td>\n<\/tr>\n

\n

4<\/p>\n<\/td>\n

\n

Inflammation mediated by chemokine and cytokine signaling pathway (P00031)<\/p>\n<\/td>\n

\n

JAK2, PTEN, KRAS, RELA, RAF<\/p>\n<\/td>\n<\/tr>\n

\n

5<\/p>\n<\/td>\n

\n

Integrin signalling pathway (P00034)<\/p>\n<\/td>\n

\n

SRC, KRAS, RAF<\/p>\n<\/td>\n<\/tr>\n

\n

6<\/p>\n<\/td>\n

\n

P53 pathway feedback loops 1 (P04392)<\/p>\n<\/td>\n

\n

P53<\/p>\n<\/td>\n<\/tr>\n

\n

7<\/p>\n<\/td>\n

\n

PDGF signaling pathway (P00047)<\/p>\n<\/td>\n

\n

JAK2, PKCA, KRAS, MYC,<\/p>\n

RAF, MTOR,<\/p>\n<\/td>\n<\/tr>\n

\n

8<\/p>\n<\/td>\n

\n

PI3 kinase pathway (P00048)<\/p>\n<\/td>\n

\n

JAK2, PTEN, KRAS, CCND1<\/p>\n<\/td>\n<\/tr>\n

\n

9<\/p>\n<\/td>\n

\n

Ras Pathway (P04393)<\/p>\n<\/td>\n

\n

ATF2, KRAS, RAF<\/p>\n<\/td>\n<\/tr>\n

\n

10<\/p>\n<\/td>\n

\n

p53 pathway feedback loops 2 (P04398)<\/p>\n<\/td>\n

\n

RB, E2F3, PTEN, KRAS,<\/p>\n

P53, ATM, MYC<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n

Table\n13 shows the pathway enrichment analysis using the GO database identified a\ntotal of 45 pathways, with 10 of these associated with cancer. These\ncancer-related pathways include the Apoptosis signalling pathway (P00006), EGF\nreceptor signalling pathway (P00018), FGF signaling pathway (P00021),\nInflammation mediated by chemokine and cytokine signaling pathway (P00031),\nIntegrin signalling pathway (P00034), P53 pathway feedback loops 1 (P04392),\nPDGF signaling pathway (P00047), PI3 kinase pathway (P00048), Ras Pathway (P04393),\nand p53 pathway feedback loops 2 (P04398).<\/p>\n\n\n\n

The\nKRAS gene 23<\/sup> was involved in eight pathways: EGF receptor signalling\npathway (P00018), FGF signaling pathway (P00021), Inflammation mediated by\nchemokine and cytokine signaling pathway (P00031), Integrin signalling pathway\n(P00034), PDGF signaling pathway (P00047), PI3 kinase pathway (P00048), Ras\nPathway (P04393), and p53 pathway feedback loops 2 (P04398). The ATF2 gene was\nassociated with two pathways: Apoptosis signaling pathway (P00006) and Ras\nPathway (P04393). The PKCA gene participated in four pathways: Apoptosis\nsignaling pathway (P00006), EGF receptor signaling pathway (P00018), FGF\nsignaling pathway (P00021), and PDGF signaling pathway (P00047). The P53 gene\nwas involved in three pathways: Apoptosis signaling pathway (P00006), P53\npathway feedback loops 1 (P04392), and p53 pathway feedback loops 2 (P04398).\nThe RELA gene was associated with two pathways: Apoptosis signaling pathway\n(P00006) and Inflammation mediated by chemokine and cytokine signaling pathway\n(P00031). The RAF gene24 <\/sup>was present in five pathways: EGF receptor\nsignaling pathway (P00018), FGF signaling pathway (P00021), Inflammation\nmediated by chemokine and cytokine signaling pathway (P00031), Integrin\nsignaling pathway (P00034), and Ras Pathway (P04393). The ERBB2 gene25<\/sup>\nwas linked to the EGF receptor signaling pathway (P00018), and the JAK2 gene\nwas associated with three pathways.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

Non-Hodgkin’s\nlymphoma (NHL) is the most prevalent type of lymphoma, typically characterized\nby cancerous lymphocytes in the lymph nodes. NHL accounts for approximately 90%\nof all lymphoma cases in humans. Gene expression analysis has significantly\nadvanced our understanding of various biological processes, enhancing\ntarget-specific drug design. This study involved microarray data analysis to\nidentify differentially expressed genes using a linear regression algorithm on\nraw CEL files with R and Bioconductor packages. Functional annotation and\nenrichment of these genes were performed using the DAVID and GO databases. The\nfinal analysis revealed that two genes, KRAS and CCND1, were involved in\ncancer-related pathways.<\/p>\n\n\n\n

Further\ninvestigation into variant gene expression and their involvement in NHL was\nconducted through RNA-Seq data analysis using Galaxy tools. The RNA-Seq results\nindicated that out of 189 variant gene sets and 10,913 genes, around 15 genes\nwere implicated in 10 cancer-causing pathways. Among these 15 genes, KRAS, RAF,\nand PKCA were found in multiple cancer pathways. Visualization using the UCSC\nGenome Browser showed significant variations in these genes compared to the\nreference genome. KRAS, CCND1, and RAF exhibited notable differences in gene\nexpression in B-cell NHL.<\/p>\n\n\n\n

Research\nindicates that the KRAS gene encodes the K-ras protein, which is part of the\nRAS\/MAPK signaling pathway and is classified as an oncogene with the potential\nto cause cancer. KRAS is also implicated in NHL. CCND1 (Cyclin D1) encodes a\nprotein involved in regulating CDK kinases in the cell cycle, and mutations in\nthis gene can lead to various cancers, including intestinal and stomach cancer.\nThe RAF gene provides instructions for a protein involved in the RAS\/MAPK\nsignaling pathway, transmitting chemical signals from outside the cell to the\nnucleus. Further wet lab analysis is recommended for these genes due to their\npotential role in targeted drug design for NHL.<\/p>\n\n\n\n

Acknowledgement<\/strong><\/p>\n\n\n\n

I would like to acknowledge\nAmity Institute of biotechnology, Amity University Uttar Pradesh, Lucknow\ncampus for providing us facilities to conducting this study. This research\nproject is not funded by any specific grant from funding agencies in the public,\ncommercial, or non-profit sectors.<\/p>\n\n\n\n

Funding Sources <\/strong><\/p>\n\n\n\n

The author received no financial support for the\nresearch, authorship, and\/or publication of this article<\/p>\n\n\n\n

Conflicts of Interest<\/strong><\/p>\n\n\n\n

The author do not have any conflict of interest.<\/p>\n\n\n\n

Data Availability Statement<\/strong><\/p>\n\n\n\n

The manuscript incorporates all datasets produced or\nexamined throughout this research study. <\/p>\n\n\n\n

Ethics Statement<\/strong><\/p>\n\n\n\n

This research did not involve human participants,\nanimal subjects, or any material that requires ethical approval<\/p>\n\n\n\n

Informed Consent Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, and\ntherefore, informed consent was not required<\/p>\n\n\n\n

Clinical Trial Registration<\/strong><\/p>\n\n\n\n

This research does not involve any clinical trials<\/p>\n\n\n\n

Authors\u2019 Contribution<\/strong><\/p>\n\n\n\n

Ankit Singh Negi: Data Collection, Methodology, Writing \u2013 Original Draft.<\/p>\n\n\n\n

Ruchi Yadav: Conceptualization, Analysis, Writing \u2013 Review & Editing<\/p>\n\n\n\n

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