Sepsis, a prevalent syndrome characterized by acute and heightened mortality and disability rates, is delineated as multiple organ dysfunction arising from immune dysfunction triggered by infection. In the United States, the annual incidence of infections may escalate to 1.7 million, resulting in over 250,000 fatalities. Survivors contend with diverse chronic complications, markedly diminishing their quality of life. Presently, treatment modalities predominantly adhere to empirical and symptomatic approaches, encompassing broad-spectrum antimicrobial agents, fluid resuscitation, anti-shock measures, infection prevention, vasoactive drugs, among others. Targeted therapeutic interventions addressing etiology and immune response are limited [1]. The economic burden of sepsis treatment ranks among the highest within hospitalized diseases. Initiated in 2002, the Sepsis Survivor Movement promulgated evidence-based treatments aiming to curtail associated mortality rates. However, the complexity, heterogeneity, and formidable nature of the disease pose significant challenges, particularly in early identification—a formidable task confronted by the global healthcare system [2]. Regrettably, compliance with sepsis bundling remains suboptimal, and each hour of delay in completing sepsis bundling treatment amplifies the relative risk of mortality by 4% [3]. As a diagnostic and prognostic tool for sepsis, high sensitivity is imperative. The Quick Sepsis-related Organ Failure Assessment (qSOFA) score, introduced in Sepsis 3.0, serves as a valuable reference for frontline clinicians in disease diagnosis. Nevertheless, a study by Flavia R. et al. posits that qSOFA may inadequately identify a substantial cohort of high-risk patients, and clinical practitioners may exhibit a proclivity to misuse it. The inclination to employ qSOFA as an exclusionary screening tool for sepsis contradicts the Sepsis 3.0 recommendation, thereby risking delayed diagnosis and potential oversight of cases [4]. Despite ongoing updates to sepsis diagnostic criteria, the efficacy of commonly utilized diagnostic markers such as lactate and procalcitonin in diagnosis and prognosis has been scrutinized. Consequently, there exists an imperative to discern novel biomarkers to enhance diagnostic precision.

The evolution of proteomics, leveraging mass spectrometry, has yielded substantial insights into comprehending the potential molecular mechanisms underlying diseases, particularly facilitating protein component classification. Within this domain, data-independent acquisition (DIA) has garnered popularity owing to its enhanced capabilities in detection and quantification. The underlying principle involves the isolation and fragmentation of precursor ions within predefined windows, followed by the analysis of all resulting fragment ions through a high-resolution mass spectrometer. Consequently, DIA exhibits notable attributes such as heightened reproducibility, accuracy, and profound capabilities for proteomic analysis, enabling extensive screening of differentially expressed proteins (DEPs). This, in turn, renders DIA conducive to integration into large-scale biomarker research initiatives. The synergistic application of DIA with other advanced technologies holds promise for further augmenting analytical performance [5,6,7]. Subsequently, the enzyme-linked immunosorbent assay (ELISA) emerges as an analytical technique employed for the qualitative or quantitative analysis of target proteins. ELISA relies on the enzymatic catalysis-induced color development and color intensity of the substrate, thereby facilitating accurate assessment. This methodology proves invaluable in elucidating the presence and concentration of specific proteins, contributing to a comprehensive understanding of molecular events and paving the way for diverse applications in both basic research and clinical diagnostics.

Since its inception, molecular biology has undergone notable transformations. As research endeavors have deepened, it has become evident that explicating large and intricate systems through the study of individual components is insufficient. In response, the field of bioinformatics has emerged and found widespread application, particularly in the processing of large-scale mass spectrometry data. A prominent tool in this context is Gene Ontology (GO), a frequently employed enrichment analysis method for investigating gene or protein functions. This classification system delineates functions into biological processes, cellular components, and molecular functions. GO analysis aids researchers in comprehending the biological processes in which target genes or proteins may participate, the cellular environments they inhabit, and the molecular functions they fulfill. Another crucial resource is the Kyoto Encyclopedia of Genes and Genomes (KEGG), which facilitates the analysis of signaling pathways involved and provides insights into the specific pathways where differentially expressed genes/proteins converge. This knowledge is instrumental in understanding the broader functional context of the studied genes or proteins. In conjunction with these resources, the construction of a protein-protein interaction (PPI) network is pivotal. This involves connecting each protein as a node and establishing edges with other related proteins to form a complex network. Utilizing specialized software, the interconnections and interactions between various proteins in the network can be described, enabling the prediction of potential core biomarkers. This integrated approach, encompassing GO, KEGG, and PPI analyses, enhances our ability to unravel the intricate relationships and functionalities within molecular systems [8,9,10].

Biomarkers play a pivotal role in enhancing early diagnosis and the classification of disease severity, thereby facilitating targeted management and appropriate treatment strategies. This, in turn, contributes to the formulation of precise therapeutic approaches, ultimately improving the overall quality of life for patients. The integration of Data-Independent Acquisition (DIA) and enzyme-linked immunosorbent assay (ELISA) technologies enables large-scale screening for differentially expressed proteins (DEPs). Augmented by bioinformatics, this approach enhances the positivity rate of predictive biomarkers, establishing connections with the clinical information of patients. This integrated methodology expedites the identification of potential biomarkers with diagnostic and prognostic value. As a result, it introduces novel diagnostic tools into clinical practice, offering a transformative impact on the early detection and management of diseases [11].