Abstract

   This study aimed to identify prognostic marker genes for renal clear
   cell carcinoma (RCCC) and construct a regulatory network of
   transcription factors and prognostic marker genes. Three hundred
   eighty-six genes were significantly differentially expressed in RCCC,
   with functional enrichment analysis suggesting a relationship between
   these genes and kidney function and development. Cox and Lasso
   regression analyses revealed 10 prognostic marker genes (RNASET2, MSC,
   DPEP1, FGF1, ATP1A1, CLDN10, PLG, SLC44A1, PCSK1N, and LGI4) that
   accurately predicted RCCC patient prognosis. Upstream transcription
   factors of these genes were also identified, and in vitro experiments
   suggested that ATP1A1 may play a key role in RCCC patient prognosis.
   The findings of this study provide important insights into the
   molecular mechanisms of RCCC and may have implications for personalized
   treatment strategies.

   Keywords: Renal clear cell carcinoma, Single-factor Cox analysis, Lasso
   regression analysis, Multiple-factor Cox analysis, Prognostic risk
   assessment model, Prognostic marker gene, ATP1A1

1. Introduction

   Renal clear cell carcinoma (RCCC) is the most common type of kidney
   cancer, accounting for approximately 70–80% of all kidney cancers
   [[35]1]. Its diagnosis mainly relies on histological and imaging
   examinations, including renal ultrasound, CT, and MRI [[36]2]. For
   early-stage RCCC patients, partial or total nephrectomy is the main
   treatment option, while molecular targeted therapy becomes an important
   treatment approach for advanced or metastatic RCCC patients [[37]3].
   The incidence and mortality of RCCC are increasing globally, severely
   affecting people's life and health [[38]4].

   The clinical symptoms of RCCC are not specific and are often ignored or
   misdiagnosed [[39]5]. Therefore, it is significant to find biomarkers
   that can accurately predict the prognosis of RCCC patients [[40]6].
   Recently, with the continuous development of genomics and
   bioinformatics technologies, many studies have shown that prognostic
   biomarkers can provide clinicians with the basis for prognosis
   evaluation, thereby guiding patient treatment options and prognosis
   management [[41][7], [42][8], [43][9]]. Therefore, it is clinically
   significant to identify and study prognostic biomarkers of RCCC
   [[44]10].

   Researchers have recently identified a series of molecular markers
   related to RCCC prognosis through genomics and transcriptomics methods
   [[45]11]. Ki67, a proliferation marker, is important in RCCC prognosis
   evaluation [[46]12]. Previous studies have found that high Ki67
   expression levels are associated with pathological grade, tumor size,
   lymph node metastasis, and survival rate in RCCC [[47]13]. The VHL gene
   is one of the most common mutated genes in RCCC and an important
   regulatory factor in RCCC pathogenesis [[48]14]. Studies have shown
   that VHL gene mutations are closely related to RCCC prognosis, and its
   mutation leads to disease progression and poor prognosis [[49]15].
   HIF-1α is an important regulatory gene that regulates tumor metabolism
   and angiogenesis [[50]16]. Multiple studies have found that high
   expression of HIF-1α is associated with high-grade and malignant
   pathological features of RCCC and is also related to poor prognosis
   [[51][17], [52][18], [53][19]]. Survivin is an important anti-apoptotic
   factor that participates in the growth and proliferation of tumor cells
   [[54]20]. Many studies have shown that high expression of Survivin is
   associated with a poor prognosis of RCCC [[55][21], [56][22],
   [57][23]].

   Despite the discovery of many molecular biomarkers associated with the
   prognosis of renal clear cell carcinoma (RCCC) patients, the accuracy
   and precision of predicting RCCC patient prognosis remain limited
   [[58][24], [59][25], [60][26]]. It is because the biological
   characteristics of RCCC are complex, and its prognosis is not only
   influenced by tumor molecular features but also by several other
   factors, such as patient age, gender, disease course, and treatment
   methods [[61]27]. Additionally, different studies have varied in
   selecting prognostic markers and analysis methods, leading to
   inconsistent results [[62]28]. Therefore, more large-scale studies are
   needed to identify the optimal combination of prognostic markers to
   improve the accurate prediction of RCCC patient prognosis [[63]29].

   This study aimed to screen the prognostic markers for RCCC using gene
   differential expression analysis, functional enrichment analysis,
   single-factor Cox analysis, Lasso regression analysis, and
   multiple-factor Cox analysis. Additionally, the study aimed to
   construct a transcription factor-prognostic marker gene regulatory
   network and validate the effect of key markers on the biological
   characteristics of RCCC cells. This study's scientific and clinical
   significance lies in identifying potential key prognostic markers that
   may provide new targets for the individualized treatment of RCCC.
   Furthermore, by constructing the transcription factor-prognostic marker
   gene regulatory network, this study has provided insights into the
   molecular mechanism and interaction of prognostic markers in RCCC,
   which may open up new directions and ideas for future research.
   Additionally, the prognostic risk evaluation model developed in this
   study can provide clinical physicians with valuable references to