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Tumor Discovery PTMAP5–hsa-miR-22-3p–KIF2C axis in HCC development

form of primary liver cancer. Despite significant upstream regulatory processes involving pseudogenes
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advancements in liver cancer treatment over the past and lncRNAs. The established PTMAP5–hsa-miR-22-3p–
few years, the 5-year survival rate for patients remains KIF2C competing endogenous RNA (ceRNA) subnetwork
comparatively low. Persistent infections with hepatitis may contribute to a comprehensive understanding of
B virus (HBV) or hepatitis C virus (HCV), along with HCC pathogenesis and could provide potential diagnostic
risk factors such as alcoholism and diabetes mellitus, are markers or therapeutic targets for the disease.
identified as key contributors to liver cancer incidence.
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Preventive measures, including the prevention of HBV and 2. Materials and methods
HCV transmission, hepatitis B vaccination, treatment of 2.1. Prediction of upstream miRNAs
chronic liver disease, and antiviral therapies, have proven 11
effective in reducing the occurrence of HCC. At present, The miRTarBase dataset provides information on
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alpha-fetoprotein levels and ultrasonography results are miRNA-mRNA targeting connections (microRNA-target
the most commonly used diagnostic tools for liver cancer. interactions), corroborated by experimental findings. In
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However, a majority of individuals with liver cancer are this study, we used miRTarBase (https://mirtarbase.cuhk.
diagnosed at advanced stages when detected, resulting in edu.cn/) to predict upstream miRNAs for key genes. Our
limited treatment options and poor prognoses. Therefore, analysis revealed a significant negative correlation between
early diagnosis is critical for improving survival rates, and hsa-miR-22-3p and KIFC2 in HCC, suggesting a potential
research on specific biomarkers, especially novel ones regulatory pathway associated with HCC progression. The
(such as miRNAs), holds great promise. expression levels of KIF2C mRNA across various healthy
and cancerous human tissues were assessed using GEPIA. 12
RNA molecules are broadly classified into two types:
those that encode proteins and are directly involved in 2.2. Co-expression analysis of KIF2C
protein synthesis, known as messenger RNAs (mRNAs), Co-expression analysis was performed utilizing
and those that do not, referred to as non-coding RNAs computational tools, specifically UALCAN. and GEPIA.
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(ncRNAs). Long non-coding RNAs (lncRNAs) constitute This rigorous methodology enabled us to extract and
approximately 80% of ncRNAs and are regulated by a identify the top 100 co-expressed genes from each
diverse array of transcription factors. Pseudogenes, which platform. By intersecting these gene sets, we identified 37
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possess highly homologous DNA sequences to functional genes that were co-expressed with KIF2C and common to
genes, have lost their original protein-coding ability. both databases.
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LncRNAs can lead to transcriptional silencing or activation
within the genome by recruiting epigenetic modifiers of 2.3. Kyoto encyclopedia of genes and genomes
DNA, while pseudogenes can regulate the expression (KEGG) pathway enrichment analysis of
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of their parental genes by binding to shared miRNAs. differentially expressed miRNAs
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The involvement of pseudogenes and lncRNAs in cancer Biochemical reaction pathways in cells often rely on
development and advancement has become a focal point the activity of differentially expressed proteins. KEGG
of recent research in the field. pathway enrichment analysis of these genes provides
In this study, we followed the methods described in valuable insights into the biological processes and
Meng et al. to develop a network linked to the advancement signaling pathways in which they may be involved. To
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of HCC through various bioinformatics studies. The initial further interpret gene functions, we applied the statistical
step of our investigation involved retrieving the GSE87630 tools available in DAVID (https://david.ncifcrf.gov) for
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and GSE45267 datasets from the gene expression omnibus functional annotations. Gene ontology (GO) and KEGG
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(GEO) version 10 database. We employed the GEO2R tool pathway enrichment analyses were conducted utilizing
to conduct an in-depth analysis of differentially expressed DAVID, helping us gain a deeper insight into these genes,
genes (DEGs) within these datasets. Common DEGs and identify signaling pathways notably enriched with
between the datasets were identified using Venn diagram DEGs.
software, resulting in 346 DEGs, including 69 upregulated
and 277 downregulated genes in HCC. Subsequently, we 2.4. Analysis using the StarBase and MiRNet
conducted protein–protein interaction (PPI) analysis to databases
identify central hub genes within this cohort of 346 genes. We employed StarBase to analyze miRNAs in relation to
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Following expression correlation and survival analysis, gene expression and their interactions with pseudogenes
hsa-miR-22-3p was identified as the miRNA most likely or lncRNAs. In our quest to unravel intricate regulatory
to bind to KIF2C. Further, investigation explored potential networks, we applied a stringent threshold for identifying

Volume 3 Issue 3 (2024) 2 doi: 10.36922/td.2846