Introduction

Cancer is a complex group of diseases characterized by the uncontrolled proliferation of abnormal cells that can invade surrounding tissues [1]. This uncontrolled growth results from accumulated genetic and epigenetic alterations that disrupt normal cellular mechanisms, including cell cycle regulation, apoptosis and DNA repair [2]. Globally, cancer remains one of the leading causes of morbidity and mortality. The dangers of cancer lie not only in the biological damage it causes, but also in the long-term quality of life of patients who experience a decline due to the side effects of therapy, such as extreme fatigue, chronic pain, immune system disorders and metabolic changes [3]. Furthermore, the high economic burden of cancer treatment adds to social stress [4].

Access to cancer diagnostic tools remains highly limited in developing countries. This limitation is one of the main reasons why 70 % of new cancer cases are diagnosed at an advanced stage. Consequently, opportunities for effective treatment are limited, leading to low patient survival rates. Detecting cancer at an early stage is one of the most critical factors associated with successful treatment outcomes [5]. Early cancer detection enables timely interventions and the possibility of less invasive treatments, resulting in more effective disease management and improved patient outcomes [6].

Cancer biomarkers serve as specific early indicators for early cancer detection and help guide therapy before the cancer progresses to an incurable stage. Furthermore, biomarkers are crucial for determining the likelihood of disease recurrence and for evaluating patient follow-up after chemotherapy, radiotherapy, or surgical treatment. Therefore, the development of more affordable and accurate diagnostic tools is essential, particularly in countries with limited healthcare resources [7]. Cancer biomarkers are generally detected using conventional techniques such as polymerase chain reaction (PCR) [8], western blotting [9], immunofluorescence [10] and liquid chromatography-mass spectrometry (LC-MS) [11]. However, these techniques have several major limitations, including high cost, relatively long analysis times, the need for specialized laboratory facilities and the requirement for trained personnel [12].

Up to the present time, many types of cancer remain difficult to detect at an early stage due to their non-specific symptoms and the limitations of conventional diagnostic methods [13]. This condition leads to most cancer cases being identified only at advanced stages, when the chances of successful therapy have significantly decreased [14]. Thus, the development of reliable, rapid and sensitive biomarker detection methods, especially for early-stage cancer, is urgently needed. In this regard, biosensors are promising candidates for the specific and simultaneous detection of multiple biomarkers, while also enabling analysis of associated biological interactions owing to their ease of modification [7].

Biosensors are integrated devices combining a bioreceptor and a transducer, capable of converting a biological reaction into a measurable signal that can subsequently be amplified and analyzed [14,15]. The biorecognition element plays the most critical role in a biosensor, as it determines the system’s selectivity and sensitivity through specific binding to the target analyte. Therefore, the selection of an appropriate bioreceptor is a crucial first step in biosensor design and development [14].

In biosensor systems, a major problem that often arises is non-specific binding. This can cause changes in the electrical signal, resulting in inaccurate measurement results. Therefore, the recognition element used must be highly specific to the analyte or target being detected. In this regard, amino acid-based proteins are well-suited for use as recognition elements. Among the various types of proteins, antibodies are the most widely used proteins for molecular recognition due to their highly specific ability to recognize targets [15]. However, in many cases, antibodies are not always available or cannot be produced. Even when available, antibodies may not have the appropriate specificity and affinity. Commercial antibodies also often exhibit poor characterization and variability between production batches [16]. On the other hand, the development of nanobodies (small antibody domains) for quantitative detection or simply for target capture is still relatively slow. Furthermore, both antibodies and nanobodies are produced through antigen immunization, which complicates their production and costs [17].

Due to various limitations in the use of antibodies, nucleic acid aptamers, typically 25 to 80 bases in length, were introduced as an alternative in vitro bioreceptor that can bind targets with high affinity and specificity [18]. This bioreceptor has several advantages, including long shelf life, resistance to heat and high temperatures, and can be modified without reducing binding affinity [19]. Therefore, aptamers are considered an alternative to monoclonal antibodies in many biotechnology applications, such as therapy and disease detection [20]. However, the development of aptamers through experimental methods such as systematic evolution of ligands by exponential enrichment (SELEX) requires significant time and energy and has limited reproducibility. In addition, aptamers have low resistance to nucleases, so they are not effective for clinical applications [18].

Recently, researchers have turned their attention to developing biologically neutral and physically stable protein scaffolds. One of the key advantages of this approach is that the scaffold protein design results in chemically uniform molecules that can be easily tailored to recognize a wide variety of analytes without requiring significant changes to the biosensor design or configuration. Furthermore, this approach enables a higher density of recognition elements on the biosensor surface, thereby improving sensor performance. In line with this approach, affibodies have emerged as a new class of engineered bioreceptors with superior target recognition characteristics [20].

Affibody molecules represent a new class of engineered affinity proteins with high affinity and specificity toward target proteins or peptides once isolated. These molecules are composed of α-helical structures and lack disulfide bonds, allowing their use in intracellular applications [21]. Unlike other bioreceptors, such as antibodies and aptamers, affibody molecules can be produced more rapidly via solid phase peptide synthesis (SPPS)due to their small size and rapid folding kinetics. Furthermore, these molecules can be produced recombinantly in bacteria such as E. coli [22]. Consequently, affibodies are considered a promising new alternative as affinity proteins, belonging to the class of scaffold proteins that mimic monoclonal antibodies but with enhanced properties [23].

Several previous review articles have discussed the use of affibody molecules in various application domains. Justino et al. [23] focused on the use of affibody in cancer cell targeting, affibody purification and medical imaging, without emphasizing early cancer detection. Meanwhile, Ståhl et al. [22] focused on the use of affibody for broader medical applications, including imaging and therapy for neurodegenerative diseases and inflammatory disorders. Then, Löfblom et al. [21] reviewed various affibody molecules that specifically target human epidermal growth factor receptor 2 (HER2) with their main focus on therapeutic applications, in vivo imaging and biotechnology. In contrast to the reviews above, this review offers a broader perspective, covering the detection of various types of cancer with affibody-integrated biosensor devices. This review explores the development of affibody-based biosensors for detecting various cancer biomarkers, emphasizing their applications in early diagnostics. Furthermore, this review examines affibody synthesis methods, such as solid-phase peptide synthesis (SPPS) and recombinant synthesis in E. coli. In addition, various signal amplification strategies, such as those used to improve detection performance, are discussed. Finally, this review provides a comprehensive overview of current applications and prospects in cancer biomarker diagnostics.