\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[greek,ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Aberrant expression of SPRING1 is involved in the progression of B-cell
acute lymphoblastic leukemia (B-ALL)}



\author[1]{Moein Farshchian}%
\author[1]{hossein barzegar}%
\author[1]{Seyed Saeed Khatami}%
\author[2]{Reihaneh Alsadat Mahmoudian}%
\author[3]{Mohammed Allami}%
\author[4]{Eman Jassim Mohammed}%
\author[1]{Elahe Dehghani Firouzabadi}%
\author[1]{Reza Alemohammad}%
\author[1]{Sara Chitgaran}%
\author[1]{Fatemeh Nasrabadi}%
\author[1]{Vahid Moghimi}%
\author[2]{Ali Ghasemi}%
\author[3]{Maryam M. Matin}%
\affil[1]{Stem Cell and Regenerative Medicine Research Group Academic Center for Education Culture and Research (ACECR}%
\affil[2]{Mashhad University of Medical Sciences}%
\affil[3]{Ferdowsi University of Mashhad Faculty of Sciences}%
\affil[4]{Mustansiriyah University College of Science}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
\textbf{Background:} Precursor B-cell acute lymphoblastic leukemia
(B-ALL) is one of the most common types of leukemias in children. The
majority of B-ALL patients are distinguished by chromosomal
rearrangements; however, alternative splicing and epigenetic
deregulations can also change the expression level of transcripts
correlated with B-ALL. Therefore, the identification of prognostic and
predictive biomarkers as well as the use of individualized treatments
can help in B-ALL therapy. In this study, we performed an RNA-seq
analysis to determine differentially expressed RNA transcripts in B-ALL.
\textbf{Methods:} The RNA-seq data of 79 B-ALL and 14 non-malignant ITP
(immune thrombocytopenic purpura) samples were obtained from the Gene
Expression Omnibus (GEO) database. Moreover, RNA-seq was performed for
Iranian patients with B-ALL to identify differentially expressed genes
(DEGs). In order to experimentally validate the findings, the mRNA
expression of \emph{SPRING1} (or C12orf49) was evaluated in bone marrow
aspiration samples of B-ALL patients using quantitative reverse
transcription-PCR. \textbf{Results:} Differential expression analysis
revealed 920 downregulated and 1216 upregulated genes in B-ALL compared
to ITP samples. Quantitative RT-PCR revealed the significant
upregulation of \emph{SPRING1} (80\%) in B-ALL patients. Functional
enrichment analysis exhibited that \emph{SPRING1} was principally
associated with lipopolysaccharide-mediated signaling pathways.
\textbf{Conclusion:} Our results provided evidence for the involvement
of \emph{SPRING1} in the B-ALL pathogenesis. However, further functional
and clinical research is needed to understand its role in dysregulation
of lipopolysaccharide-mediated signaling pathways in B-ALL.%
\end{abstract}%



\sloppy


\textbf{Aberrant expression of \emph{SPRING1} is involved in the
progression of B-cell acute lymphoblastic leukemia (B-ALL)}

Hossein Barzegar\textsuperscript{1,10}, Seyed Saeed
Khatami\textsuperscript{1,2,10}, Reihaneh Alsadat
Mahmoudian\textsuperscript{3}, Mohammed Allami\textsuperscript{2,4},
Eman Jassim Mohammed\textsuperscript{5}, Elahe Dehghani
Firouzabadi\textsuperscript{1,6}, Reza Alemohammad\textsuperscript{1},
Sara Chitgaran\textsuperscript{1,2}, Fatemeh
Nasrabadi\textsuperscript{1}, Vahid Moghimi\textsuperscript{1,6}, Ali
Ghasemi\textsuperscript{7}*, Maryam M. Matin\textsuperscript{2,8}*,
Moein Farshchian\textsuperscript{1,9}*

\textsuperscript{1}Stem Cell and Regenerative Medicine Research Group,
Academic Center for Education, Culture, and Research (ACECR), Khorasan
Razavi, Mashhad, Iran

\textsuperscript{2}Department of Biology, Faculty of Science, Ferdowsi
University of Mashhad, Mashhad, Iran

\textsuperscript{3}Metabolic Syndrome Research Center, Mashhad
University of Medical Sciences, Mashhad, Iran

\textsuperscript{4}Department of Dentistry, Al-Manara College for
Medical Sciences, Maysan, Iraq

\textsuperscript{5}Department of Microbiology, College of Science,
Mustansiriyah University, Baghdad, Iraq

\textsuperscript{6}Department of Biology, Faculty of Science, Hakim
Sabzevari University, Sabzevar, Iran

\textsuperscript{7}Department of Pediatrics Hematology \& Oncology,
School of Medicine, Mashhad University of Medical Sciences, Mashhad,
Iran

\textsuperscript{8}Novel Diagnostics and Therapeutics Research Group,
Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad,
Iran

\textsuperscript{9}Current Address: Division of Oncology, Laboratory of
Cellular Therapy, Department of Medical and Surgical Sciences for
Children and Adults, University Hospital of Modena and Reggio Emilia,
Modena, Italy

\textsuperscript{10}These authors have contributed equally to this work

*\textbf{Corresponding Author:} Moein Farshchian

\textbf{Affiliation:} Stem Cell and Regenerative Medicine Research
Group, Iranian Academic Center for Education, Culture and Research
(ACECR) Razavi Khorasan, Mashhad Branch, Iran

\textbf{E-mail:} moein.farshchian@unimore.it

\textbf{Address:} ACECR Central building, Ferdowsi University Campus,
Mashhad- Azadi Square

*\textbf{Corresponding Author:} Maryam M. Matin

\textbf{Affiliation:} Department of Biology, Faculty of Science,
Ferdowsi University of Mashhad, Mashhad, Iran

\textbf{E-mail:} matin@um.ac.ir

*\textbf{Corresponding Author:} Ali Ghasemi

\textbf{Affiliation:} Department of Pediatrics Hematology \& Oncology,
School of Medicine, Mashhad University of Medical Sciences, Mashhad,
Iran

\textbf{E-mail:} ghasemial@mums.ac.ir



\textbf{Abstract}

\textbf{Background:} Precursor B-cell acute lymphoblastic leukemia
(B-ALL) is one of the most common types of leukemias in children. The
majority of B-ALL patients are distinguished by chromosomal
rearrangements; however, alternative splicing and epigenetic
deregulations can also change the expression level of transcripts
correlated with B-ALL. Therefore, the identification of prognostic and
predictive biomarkers as well as the use of individualized treatments
can help in B-ALL therapy. In this study, we performed an RNA-seq
analysis to determine differentially expressed RNA transcripts in B-ALL.

\textbf{Methods:} The RNA-seq data of 79 B-ALL and 14 non-malignant ITP
(immune thrombocytopenic purpura) samples were obtained from the Gene
Expression Omnibus (GEO) database. Moreover, RNA-seq was performed for
Iranian patients with B-ALL to identify differentially expressed genes
(DEGs). In order to experimentally validate the findings, the mRNA
expression of \emph{SPRING1} (or C12orf49) was evaluated in bone marrow
aspiration samples of B-ALL patients using quantitative reverse
transcription-PCR.

\textbf{Results:} Differential expression analysis revealed 920
downregulated and 1216 upregulated genes in B-ALL compared to ITP
samples. Quantitative RT-PCR revealed the significant upregulation
of\emph{SPRING1} (80\%) in B-ALL patients. Functional enrichment
analysis exhibited that \emph{SPRING1} was principally associated with
lipopolysaccharide-mediated signaling pathways.

\textbf{Conclusion:} Our results provided evidence for the involvement
of \emph{SPRING1} in the B-ALL pathogenesis. However, further functional
and clinical research is needed to understand its role in dysregulation
of lipopolysaccharide-mediated signaling pathways in B-ALL.

\textbf{Keywords:} B-cell acute lymphoblastic leukemia, B-ALL,
Biomarker, RNA-seq, SPRING1\textbf{Introduction}

Acute lymphoblastic leukemia (ALL) is the most common cancer in children
under five years of age, with an annual incidence rate of nearly 60\%
(36.2 per million person-year) and a 5-year survival rate of 90\%
worldwide . Moreover, it accounts for nearly 25\% of all acute leukemias
in adults with a 5-year survival rate of 75-85\% . Accumulating evidence
suggests that excessive proliferation of B (B-ALL) and T (T-ALL) lineage
lymphoid progenitors in the bone marrow, blood, and extramedullary sites
leads to the formation of ALL . Many genetic alterations are associated
with the onset of ALL, such as chromosomal translocations, mutations,
and aneuploidies that occur in genes responsible for controlling cell
cycle regulation and lymphoid cell development . B cell precursor ALL
(B-ALL) is associated with an infectious and in hereditary etiology
distinguished by chromosomal changes, resulting in protein dysregulation
via the formation of chimeric genes . B-ALL patients carry a chromosomal
translocation and acquire secondary genetic alterations, which lead to
the differentiation and proliferation of B cells . The genetic
heterogeneity in B-ALL is a promising source of biomarkers used for
early detection, monitoring tools, and new chemotherapy targets .
Despite the advances in the treatment of B-ALL, this disease still plays
a key role in the mortality rate of patients . Consequently, the
identification of specific biomarkers, pathways, and gene networks
related to B-ALL is crucial for developing new strategies to improve
prognostic capabilities, accelerate early diagnosis, and provide
therapeutic strategies at early stages of the disease . A comprehensive
understanding of the possible causes of B-ALL can be based on integrated
bioinformatic analysis of high-throughput sequencing, identification of
differential expression profiles, and prediction of possible biomarker
function . Next-generation sequencing (NGS) methods, such as
transcriptome sequencing have revealed the enormous genetic diversity of
B-ALL and could be a promising diagnostic platform .

In this study, we aimed to identify a novel B-ALL-related marker that
could serve as a diagnostic, prognostic, and therapeutic target as well
as provide new insights into the underlying molecular mechanisms of this
disease. To facilitate this, we have performed transcriptome analysis to
identify a genetic biomarker for B-ALL. Moreover, we conducted
differentially expressed genes (DEGs) analysis, weighted gene
co-expression network analysis (WGCNA), and gene ontology (GO)
enrichment analysis on a B-ALL clinical cohort, in parallel with other
samples from the GEO data bank, to identify genes and pathways
associated with B-ALL. Here, we identified \emph{SPRING1} (or C12orf49)
as a novel marker in B-ALL using RNA-seq analysis and validated its mRNA
expression level with quantitative real-time PCR.



\textbf{Materials and Methods}

\textbf{Data resource}

RNA-seq data profiles were obtained from the Gene Expression Omnibus
(GEO) database (https://www.ncbi.nlm.nih.gov/geo/). These data profiles
have included 69 B-ALL and 12 ITP samples from three different projects,
including PRJNA397402, PRJNA475264, and PRJNA549254 . Moreover, we
performed RNA-seq analysis for 10 Iranian B-ALL and 2 ITP samples, and
the raw RNA-seq data is available under accession number PRJNA589314.
Data profiles and the number of samples are indicated in Table 1. FASTQ
raw data were trimmed by Trimmomatic software and mapped to the human
reference genome (GRCh38/hg38) using STAR (version: 2.7.3a). Then,
HTSeq-count (version: 1.99.2) was utilized for gene quantification and
DEGs were identified by the DESeq2 package in the R environment (version
4.0.5). A |log2 fold change (FC)| \textgreater{} 2 with \emph{P}
-adjusted values \textless{} 0.05 and baseMean \textgreater{} 100 was
considered significant for validating DEGs .

\textbf{Network analysis and module detection}

Network analysis identifies gene sets among different subtypes that
interact together and are concordantly expressed. The Weighted gene
co-expression network analysis (WGCNA) package constructed the gene
network and determined modules to identify the function of candidate
genes using all 93 samples . After highly co-expressed genes were
clustered into co-expressed modules, the module-trait association for
all genes was used by topological analysis to generate gene dendrograms
to identify modules . Then, the top 10 GO enrichment pathways for each
module were identified using the GO enrichment analysis function.
Finally, network visualization and functional analysis were performed
using Cytoscape software version 3.9.1 (http://www.cytoscape.org) .

\textbf{Patients and ethics statement}

Bone marrow mononuclear cells (BMMNCs) samples were obtained from 20
B-ALL patients and 20 non-malignant ITP (immune thrombocytopenic
purpura) patients who had not received any chemoradiotherapy before
aspiration and had no history of any cancer type, respectively.
Specimens were obtained from Dr. Sheikh Children's Hospital of Mashhad
University of Medical Sciences (MUMS), Mashhad, Iran. All individual
participants signed informed consent forms prior to their inclusion in
the study. The ethics committee of the Ferdowsi University of Mashhad,
Mashhad, Iran approved the research protocol (ethical code:
IR.UM.REC.1400.058) and it was performed following the Helsinki
Declaration.

\textbf{RNA extraction, RNA-sequencing library, and comparative
real-time PCR analysis}

Total RNA was isolated from BMMNCs using the TriPure reagent (QIAGEN,
Netherlands) according to the manufacturer's instructions. RNA-seq
libraries were manufactured by the KAPA HyperPrep kit with RiboErase and
the RNA-seq was performed by 100 million paired-end reads on an Illumina
NovaSeq 6000 platform (CeGaT company, Tubingen, Germany). Additionally,
total RNA was treated with \emph{DNase I} (Thermo Fisher Scientific,
Waltham, MA) to eliminate genomic DNA, and the first strand cDNA was
synthesized using the First Strand cDNA Synthesis Kit (AddScript cDNA
Synthesis kit) according to the manufacturer's protocol. The mRNA
expression level of \emph{SPRING1} (or C12orf49) was determined by
comparative real-time PCR using SYBR Green Master Mix (AMPLIQON,
Denmark) on a CFX96 BioRad System thermocycler (Roche, Germany) with
specific primers (forward primer 5'-CCCAACAAGCAACTTCTC-3' and reverse
primer 5'-TCTCCATAGCAATACTTTGC-3') through calculating the\emph{SPRING1}
mRNA expression level using the 2\selectlanguage{greek}\textsuperscript{-ΔΔCt}\selectlanguage{english}method and
beta-glucuronidase (\emph{GUSB} ) as an endogenous control.

\textbf{Statistical analysis and data mining using GEPIA}

Gene expression profile interactive analysis (GEPIA)
(http://gepia.cancer-pku.cn/detail.php) was performed to indicate the
expression level, stage-specific expression of \emph{SPRING1} , and the
effect of \emph{SPRING1} on survival in diffuse large B-cell lymphoma as
a lymphoid disorder. Statistical analysis and visualization were
performed using IBM SPSS 26 statistical software (La Jolla, CA, USA) and
GraphPad Prism (version 9.0.0.121). All \emph{P} -values \textless{}
0.05 were considered as significant. The measurement of the predictive
efficacy of \emph{SPRING1} was carried out via the use of receiver
operating characteristic (ROC) curve analysis and the area under the ROC
curve (AUC) for B-ALL . The AUC value is between 0 to 1, which indicates
the diagnostic power based on: 0.9-1.0 (favorable), 0.8-0.9
(appropriate), 0.7-0.8 (acceptable), 0.6-0.7 (weak), and 0.5-0.6 (less)
.

\textbf{Results}

\textbf{Transcriptome profiling and WGCNA analysis correlated to B-ALL
phenotypes}

Considering the importance of early diagnosis of B-ALL which can lead to
effective treatment of patients, we evaluated the expression profile of
B-ALL compared to ITP samples. RNA-seq data related to Iranian samples
were deposited in the SRA BioProject database with accession number
PRJNA589314. A total of 2136 dysregulated genes were recognized
(adjusted \emph{P} -value \textless{} 0.05), including 920 downregulated
genes and 1216 upregulated genes, which are demonstrated as a volcano
plot in Fig. 1.

WGCNA was applied to identify the association between the gene
expression patterns of . Our analysis identified 18 separate gene
co-expression modules, each containing a group of genes that tended to
be co-expressed across samples. Based on WGCNA results, blue and
turquoise modules were positively correlated with B-ALL status, while
yellow and magenta modules were negatively correlated (Fig. 2A). Key
modules were identified by evaluating the association between these 18
modules and clinical traits, and the results are demonstrated as a
heatmap in Fig. 2B. Accordingly, 12 models were identified, whose key
genes included \emph{ADPRHL1, SPRING1, LINC01343, FCN1, UBASH3B, FHL1,
LINC00692, BCL7A, MYSM1, NRIP1, GAB1, H2AC20} . Moreover, the expression
level of key genes was significantly correlated to some biological
processes (BP), such as the lipopolysaccharide-mediated signaling
pathway via GO enrichment analysis. The \emph{in-silico} data showed
significant upregulation of \emph{SPRING1} in B-ALL samples.
Accordingly, we selected \emph{SPRING1} as a novel marker to investigate
its effects in B-ALL tumorigenesis.

\textbf{Study population}

The general mean age of the B-ALL patients (60\% males and 40\% females)
and ITP subjects (55\% males and 45\% females) was 6.05 \selectlanguage{ngerman}± 3.77 and 5.05
± 3.05 years, respectively. The immunophenotyping and cytogenetics data
of patients are demonstrated in Table 2. Most of the patients expressed
CD3/10/13/19/33/34/79a/ and 117, while the expression of CD2/7/41/ and
61 markers was indicated in fewer patients.

\textbf{The mRNA expression of \emph{SPRING1} in B-ALL patients}

We examined the mRNA expression level of \emph{SPRING1} in 20 B-ALL
patients and 20 non-malignant ITP cases by comparative qRT-PCR. The
qRT-PCR data are demonstrated as a box plot in Fig. 3A based on the log2
FC of mRNA expression level. A significant upregulation of\emph{SPRING1}
(80\%) was detected in B-ALL patients. The mean FC of\emph{SPRING1} was
3.21 in patients. Moreover, RNA-seq data revealed the upregulation of
\emph{SPRING1} in B-ALL patients compared to ITP samples, as shown in
Fig. 3B. Interestingly, the \emph{SPRING1}expression level is increased
in B-ALL patients compared to ITP based on both \emph{in-silico} and
qRT-PCR data.

\textbf{Diagnostic value of \emph{SPRING1} for B-ALL patients}

The diagnostic value of the \emph{SPRING1} mRNA expression was
quantified by ROC curve and AUC measurement (Fig. 4). The area under the
ROC curve of \emph{SPRING1} (\emph{P} \textless{} 0.0001) was 0.89 ±
0.04 (95\% CI: 0.792-0.987), which is consistent with the appropriate
accuracy (Sensitivity=80\%, Specificity=75\%). Accordingly, the ROC
curve of \emph{SPRING1} can be applied for auxiliary diagnosis of B-ALL
patients.

\textbf{Validating the mRNA expression of \emph{SPRING1}}

We examined the mRNA expression level of \emph{SPRING1} in diffuse large
B-cell lymphoma as a lymphoid disorder via the GEPIA database. The
findings indicated an increase in the \emph{SPRING1} expression level,
as demonstrated in Fig. 5A. The overexpression of \emph{SPRING1} did not
differ in the stage-specific status (Fig. 5B). Moreover, there was a
correlation between the overexpression of \emph{SPRING1} and overall
survival of patients based on GEPIA database analysis (Fig. 5C).



\textbf{Discussion}

Molecular mechanisms involved in leukemogenesis, such as structural
chromosomal rearrangements, alternative splicing, and epigenetic
deregulation can alter the expression of transcripts that are associated
with the B-ALL development. Identifying these mechanisms through
molecular diagnostic technologies will provide a better understanding of
the disease pathogenesis, leading to the rapid expansion of molecular
biomarkers related to B-ALL. Identification of these novel biomarkers
can improve the precision of risk diagnosis, personalized treatment,
better monitoring of treatment response, development of new risk-adapted
targeted therapies, and patient outcome .

This study aimed to evaluate the transcriptome profile of B-ALL patients
to find a novel diagnostic or prognostic biomarker through RNA-seq data
analysis. The differential expression analysis, WGCNA, and GO analysis
was performed to identify patterns of gene expression alterations and
biological pathways related to DEGs\emph{.} Our first findings
demonstrated the \emph{SPRING1} upregulation in B-ALL patients based on
WGCNA analysis and qRT-PCR. Moreover, there was a positive correlation
between the lipopolysaccharide-mediated signaling pathway
and\emph{SPRING1} activity based on GO enrichment analysis. These data
propose that \emph{SPRING1} has an imminent role in B-ALL tumorigenesis
and can be a potential diagnostic marker for this disease. This
integrated approach which includes a combination of bioinformatics and
experimental validation, could be a powerful strategy to identify
potential biomarkers and increase our understanding of the underlying
biology of B-ALL.

Lipid rafts serve as a principal modality for signaling in cancer cells,
where cholesterol assumes a pivotal function in this context.
Accordingly, targeting cholesterol metabolism may be used as a viable
therapeutic approach for the treatment of B-ALL . It is noteworthy that
the disruption of cholesterol metabolism may lead to the formation of
malignancy, such as breast, prostate, and colorectal cancers;
consequently, there is a positive correlation between the cholesterol
levels and cancer incidence . SPRING1 protein (or C12orf49) is localized
in the Golgi and plays a crucial role in regulation of fatty acid
metabolism and tumor pathogenesis (18,19). SPRING1 binds to the
peptidase-site membrane-bound transcription factor 1 (MBTPS1) and
triggers the cleavage of its substrate, specifically sterol regulatory
element binding protein (SREBP) transcription factors
(19,21,22).\emph{SPRING1} plays a key role in the activation and
cleavage of the SREBP precursor protein through the protease activity
and maturation of S1P during its transport from the endoplasmic
reticulum to the Golgi . Therefore, \emph{SPRING1} is essential for
maintaining both precursor and mature SREBP protein levels . SREBPs, as
a large class of transcription factors, regulate lipid homeostasis by
modulating the expression of enzymes necessary for endogenous
cholesterol, fatty acid (FA), triacylglycerol, and phospholipid
synthesis . Activation of SREBP occurs through its transport from the
endoplasmic reticulum to the Golgi, where the S1P enzyme cleaves the
endoplasmic reticulum loop of SREBP . It has been reported that S1P
cleavage activity depends on its interaction with \emph{SPRING1} .
Moreover, SREBPs transcription factors regulate cholesterol biosynthesis
and uptake for cellular function . Cholesterol is a major component of
membrane lipids, which plays a critical role in cancer development as
the primary platform in the membrane for signaling regulation in cancer
. Consequently, regulation of membrane cholesterol balance is essential
to maintain cellular homeostasis. Dysregulation of \emph{SPRING1} leads
to unbalanced cholesterol metabolism and the possibility of some
disorders, such as tumor formation, cardiovascular, and
neurodegenerative diseases .

It has been reported that the function of \emph{SPRING1} is associated
with divergent prognostic outcomes across various tumor types, including
kidney, breast, liver, and sarcoma by The Cancer Genome Atlas (TCGA)
database analysis . The upregulation of \emph{SPRING1} was indicated in
CRC tissues compared to non-cancerous tissue samples that correlated to
tumor growth. Therefore, \emph{SPRING1} was identified as a potential
biomarker that provides therapeutic benefits for CRC patients .
Moreover, the overexpression of \emph{SPRING1} in breast cancer tissues
was associated with significantly reduced survival rates of the patients
as demonstrated by TCGA database analysis . Additionally, the
significant upregulation of \emph{SPRING1} was confirmed in clear cell
renal cell carcinoma-small renal masses (ccRCC-SRMs) compared to renal
oncocytoma, which was associated with decreased overall survival rate of
patients based on proteome analysis . Consistent with previous studies,
our data showed significant overexpression of \emph{SPRING1} in B-ALL
patients. Taken together, our data propound that the
\emph{SPRING1}dysregulation can impact the lipopolysaccharide-mediated
signaling pathway and the downstream genes, which can correlate to B-ALL
formation and development.

\textbf{Conclusion}

In summary, differential expression analysis, WGCNA, and GO analysis
highlighted several transcripts in B-ALL. Here, we indicated the
upregulation of \emph{SPRING1} in B-ALL patients compared to ITP samples
for the first time. The findings propose that \emph{SPRING1} directly
affects cholesterol metabolism. However, further functional and clinical
analysis are needed to fully understand the role of \emph{SPRING1} in
B-ALL.

\textbf{Acknowledgments} The authors wish to thank their colleagues at
the Mashhad Academic Center for Education, Culture, and Research (ACECR)
for the kindest collaboration.

\textbf{Consent for publication}

Not applicable.

\textbf{Availability of data and materials}

The data used and/or analyzed during the current study are available
from the corresponding authors upon reasonable request.

\textbf{Funding}

This study was supported by a Grant from the Vice-Chancellor of Mashhad
University of Medical Sciences (grant number: 971860). The authors
appreciate the support provided by the Research and Technology Council
of the Ferdowsi University of Mashhad, Mashhad-Iran (grant number:
3/50365). This study was part of a Master of Science student's
dissertation.

\textbf{Ethics approval and consent to participate}

All procedures were approved by Mashhad University of Medical Sciences,
and Ferdowsi University of Mashhad and performed according to 1964
Helsinki Declaration.

\textbf{Informed consent}

Informed consent was obtained from all individual participants included
in the study.

\textbf{Declaration of competing interest}

The authors declare that they have no competing interests.

\textbf{Data availability}

All raw data are available upon request.

\textbf{References}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Abbreviations &\tabularnewline
\midrule
\endhead
AUC & area under the curve\tabularnewline
B-ALL & B-cell acute lymphoblastic leukemia\tabularnewline
BMMNCs & bone marrow mononuclear cells\tabularnewline
BP & biological process\tabularnewline
ccRCC-SRMs & clear cell renal cell carcinoma-small renal
masses\tabularnewline
CRC & colorectal cancer\tabularnewline
DEGs & differentially expressed genes\tabularnewline
FA & fatty acid\tabularnewline
GEPIA & Gene expression profiling interactive analysis\tabularnewline
GO & gene ontology\tabularnewline
GEO & gene expression omnibus\tabularnewline
ITP & immune thrombocytopenic purpura\tabularnewline
Log2 FC & log2 fold change\tabularnewline
MBTPS1 & membrane-bound transcription factor peptidase site
1\tabularnewline
MUMS & Mashhad University of Medical Sciences\tabularnewline
NGS & next-generation sequencing\tabularnewline
NCBI & National Center for Biotechnology Information\tabularnewline
qRT-PCR & quantitative reverse transcription-polymerase chain
reaction\tabularnewline
RNA-seq & RNA sequencing\tabularnewline
ROC & receiver operating characteristic\tabularnewline
RPKM/FPKM & reads/ fragments per kilobase of transcript per million
mapped reads\tabularnewline
SPRING1 & SREBF pathway regulator in Golgi 1\tabularnewline
SRA & sequence read archive\tabularnewline
SREBP & sterol regulatory element binding protein\tabularnewline
S1P & site-1 protease\tabularnewline
T-ALL & T-cell acute lymphoblastic leukemia\tabularnewline
TCGA & The Cancer Genome Atlas\tabularnewline
WGCNA & weighted gene co-expression network analysis\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{Figure 1.} Volcano plot of DEGs between B-ALL patients and
normal samples. Statistically significant DEGs were defined with
adjusted \emph{P} -value \textless{} 0.05 and absolute log2 FC
\textgreater{} 2.0 as the cut-off threshold.

\textbf{Figure 2.} Weighted Gene Co-Expression Network Analysis (WGCNA)
to identify Gene Modules. \textbf{(A)} The clustering dendrogram of
differentially expressed genes in B-ALL. A total of 18 co-expression
modules were identified, each leaf (vertical line) in the dendrogram was
assigned to one co-expression gene, and the color row below the
dendrogram represents the modules. The \emph{SPRING1} was clustered in
the turquoise module, \textbf{(B)} Heatmap plot of the adjacencies of
modules. Red represents a positive correlation, and green represents a
negative correlation. The amount of correlation and the
calculated\emph{P} -values are also shown in each cell. The module
assignment is shown on the left. The yellow, blue, turquoise, and
magenta modules have the highest correlation with B-ALL cancer. (For
interpretation of the references to color in this figure legend, the
reader is referred to the web version of this article.)

\textbf{Figure 3.} (A) Box plot representing a descriptive analysis of
relative gene expression of \emph{SPRING1} in B-ALL patients and ITP
samples; (B) Mean of log2 fold change for the \emph{SPRING1} mRNA
expression.

\textbf{Figure 4.} ROC curve of \emph{SPRING1} for diagnosis of B-ALL
patients. The AUC value differs from 0 to 1, which indicates the
diagnostic power based on favorable (0.9-1.0), appropriate (0.8-0.9),
acceptable (0.7-0.8), weak (0.6-0.7), and less (0.5-0.6). ROC: receiver
operating characteristic; AUC: area under the ROC curve.

\textbf{Figure 5.} Validating the role of \emph{SPRING1} in lymphoid
disorder by the GEPIA dataset. (A) Box plot shows expression
of\emph{SPRING1} in cancer (left) and normal (right) samples; (B) Stage
plot shows SPRING1 expression in 4 stages; (C) Overall survival between
patients with the higher (red) and lower (blue)
\emph{SPRING1}expression.

\textbf{Table legends}

\textbf{Table 1.} The characteristics of different datasets used in this
study

\textbf{Table 2.} The immunophenotype data of 20 B-ALL patients

Figure 1\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 2\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{A}

\textbf{B}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Expression of \emph{SPRING1} in B-ALL patients compared to the ITP group
based on RNA-seq analysis and qRT-PCR\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Log2 Fold change \emph{SPRING1} & Log2 Fold change \emph{SPRING1} & Log2
Fold change \emph{SPRING1}\tabularnewline
RNA-seq data analysis & Iranian patients & 6.25 (\emph{P}-adjusted
\textless{} 0.0001)\tabularnewline
& Non-Iranian patients & 7.06 (\emph{P}-adjusted \textless{}
0.0001)\tabularnewline
Real-time PCR & Iranian patients & 3.21 (\emph{P} \textless{}
0.0001)\tabularnewline
\bottomrule
\end{longtable}

Figure 4\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 5\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Table 1. Dataset's characteristics & Table 1. Dataset's characteristics
& Table 1. Dataset's characteristics & Table 1. Dataset's
characteristics & Table 1. Dataset's characteristics\tabularnewline
[?] & \textbf{Bio Project} & \textbf{Type} & \textbf{Number of samples} &
\textbf{Platform}\tabularnewline
1 & PRJNA589314 & B-ALL & 10 & Illumina NovaSeq 6000\tabularnewline
& & ITP & 2 &\tabularnewline
2 & PRJNA397402 & ITP & 7 & Illumina HiSeq 4000\tabularnewline
3 & PRJNA475264 & B-ALL & 69 & Illumina HiSeq 2500\tabularnewline
4 & PRJNA549254 & ITP & 5 & Illumina HiSeq 2500\tabularnewline
Total & Total & Total & 93 &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllllllll@{}}
\toprule
\begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
Table 2. Immunophenotype data of 20 B-ALL patients\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.04\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD3}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD7}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD10}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD10+/} \textbf{CD19+}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD13}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD19}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD33}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD34}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD41}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD61}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD79a}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Cyto CD79a}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{CD117}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{TDT Immature lymph}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{MPO (Myelomono)}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
Patient Yes No\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
5 15\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
20 -\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
9 11\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
20 -\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
13 7\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17 3\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
20 -\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
13 7\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17 3\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
1 19\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
4 16\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
14 6\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
1 19\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
20 -\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
17 3\strut
\end{minipage} & \begin{minipage}[t]{0.04\columnwidth}\raggedright\strut
15 5\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

