Introduction

Cancer represents a major global health issue, linked to considerable illness and death rates across the world. A recent study found that approximately 10 million people died from cancer across the globe in 2020, while the number of new cases was around twenty million1. Oral, ovarian, and breast cancers are significant contributors to global cancer burden, with high incidence and mortality rates2. Oral cancer affects millions of people worldwide, while breast cancer ranks as the most prevalent cancer among women, while ovarian cancer stands as a primary cause of death from gynecological cancers2. Understanding and improving early detection and treatment of these cancers can save many lives.

While the current manuscript highlights the potential of SA as a biomarker in cancer screening, it is crucial to acknowledge that increased screening does not always directly translate to reduced cancer morbidity and mortality. In some cases, as evidenced by studies like in3,4, heightened screening activities have led to increased burdens on healthcare systems without proportionate benefits in patient outcomes. Therefore, it is important to view the development of new screening tools, such as those based on salivary SA, as supplementary options available to clinicians rather than definitive solutions5. These tools should aim to enhance the existing diagnostic framework, providing additional, non-invasive methods for cancer screening and monitoring. Their integration into clinical practice should be approached with careful consideration of their impact on healthcare resources and patient well-being6.

Because cancer often has no symptoms in the early stages,2 a cancer screening procedure is critical for early diagnosis and access to therapy. A review estimated that the relative reduction in deaths from, for example, breast cancer due to early screening was about 20%7. The main method of screening for breast cancer is mammography. However, many women experience barriers to breast cancer screening. For example, women who live in remote rural regions are disproportionately impacted by inequitable access to screening8. One study found that they were less likely to get a mammogram compared to their non-rural counterparts8.

Although early detection via mammography saves lives, the regular use of mammograms has disadvantages, especially exposure to radiation7. False positives from mammograms and the need for invasive tissue biopsies following a problematic mammogram are other disadvantages7. While the benefits often outweigh the risks, it might not be feasible for some women to have mammograms regularly. Younger women (i.e., below the age of 40) are also often excluded from screening, as for them, the risks outweigh the benefits at the population level9. However, this practice may lead to missed cases at the individual level. Although rarer, younger women with breast cancer tend to have more aggressive forms of the disease, with a higher mortality10. Finding less invasive screening methods may thus be advantageous. Furthermore, some women experience delays in getting a mammogram, particularly in regions with lower availability11.

Patients with other solid tumors, such as oral cancer, may also experience barriers to screening. Dentists are the first point of contact for screening for oral cancer, and not all patients receive regular checks, including those designated as “high risk” in one study12. Barriers include traveling for remote patients13 and the costs associated with dental care14.

Cancer biomarker sialic acid

Several studies have evaluated the relationship between salivary sialic acid (SA) levels and breast cancer. These studies found significantly higher SA levels in patients with breast cancer compared to cancer-free individuals. A study by Artega et al. found that SA levels were more than twice as high in cancer patients, with a mean concentration of 14.9 mg/dL versus 6.7 mg/dL (p < 0.01)15. This study validated salivary SA as a biomarker, finding a sensitivity of 80% and specificity of 93%15. Another study by the authors, which compared levels in 100 breast cancer patients versus 164 healthy individuals, found that the mean salivary SA levels were 18.5 mg/dL in cancer patients versus 3.5 mg/dL in healthy subjects16. A study conducted in Turkey by Ozturk et al.17 found that the mean salivary SA level was 114 mg/dL in cancer patients compared to 47 mg/dL in healthy individuals (p < 0.01). It should be noted that this study was undertaken in patients with established and pre-diagnosed breast cancer at different stages, which affected the mean level, rather than in participants who were screened for cancer only during the early stages of the disease.

Most studies on salivary SA have been undertaken in patients with oral cancer. These studies all found a significant and positive association between enhanced salivary SA levels and oral cancer. A dose–response relationship was also identified, with higher SA levels linked to more advanced stages of cancer and progression. For example, a study by Dadhich et al.18 in India found that salivary SA levels were significantly enhanced in a group of 30 oral cancer patients when compared to a group of 30 gender and age matched healthy individuals (9 mg/dL versus 1.5 mg/dL). A similar result was obtained in an independent study with 50 oral cancer patients that were compared to 50 healthy control persons19. Furthermore, there was a positive relationship between mean salivary SA levels and cancer staging, including marked differences in mean levels in patients with precancerous tumors (57.5 mg/dL) compared to healthy controls (40.3 mg/dL) and patients with established oral cancer (80.4 mg/dL) (p < 0.01)20.

Trivedi et al. investigated the effects of chemotherapy on salivary SA levels in patients with oral cancer and found that patients who had undergone chemotherapy had significantly lower levels compared to cancer patients who had not been treated21. Furthermore, Poudel et al. found a significant negative relationship between salivary SA levels and treatment duration19.

Finding noninvasive ways to prescreen or adjunct methods of screening that are simple, affordable, and readily accessible is thus important to reduce the barriers to cancer screening. The collection of saliva provides advantages because it is relatively simple and noninvasive and may be widely used in labs. Home collection kits can be sent to patients living in remote areas. Thus, there is a need to evaluate whether this method is accurate in detecting breast cancer and other solid tumors. Therefore, the rationale for this perspective arises from the imperative to identify noninvasive and easily accessible modalities for cancer screening, specifically those that can serve as prescreening or supplementary tools alongside existing methods.

Sialic acid quantification methods

Due to its potential to serve as a biomarker in various diseases, a plethora of methods have been developed to quantify free SAs22. Common to all methods is the fact that SA is first released from the cell surface22. On one hand, this can be done using various acidic compounds. The released SA is subsequently derivatized with a colorimetric agent or with a fluorescent molecule to be detected and quantified. The disadvantage of these methods is that derivatization is not specific to SAs; as such, the measurements are overrated with respect to SA levels22.

In contrast to these traditional methods, recent advancements have introduced more sophisticated techniques like Raman spectroscopy and biosensors.

Raman spectroscopy, particularly in its Surface-Enhanced Raman Scattering (SERS) form, offers a label-free, highly sensitive approach to sialic acid detection23. This technique relies on the inelastic scattering of photons, providing a molecular fingerprint of the sample. On the other hand, biosensors typically involve labeled detection strategies, incorporating specific receptors or antibodies for sialic acid24. While Raman spectroscopy excels in its minimal sample preparation and rapid analysis capabilities, biosensors are noted for their specificity and potential for quantification in complex biological matrices. The choice between these methodologies depends on factors such as the required sensitivity, the complexity of the sample matrix, and the need for label-free detection25.

To overcome this limitation, chromatographic methods, such as high-performance liquid chromatography, can be applied to separate labeled SAs from interfering molecules26. On the other hand, the release of SAs can be triggered by enzymatic digestion27. Again, the SA is then derivatized to be labeled by various methods and quantified. The enzymatic method has the advantage of resulting in measurements with high specificity; however, the process is slow and cumbersome.

More recent developments include SA biosensors with the assistance of multiwalled carbon nanotubes and ferrocene28 or the biosensor enhanced by near-infrared light excitation and localized surface plasmon resonance29. Most promising is the development of Raman-spectroscopic methods, such as the one developed by Hernández-Arteaga15 as it provides highly sensitive and specific results. However, all these methods require sophisticated and large machines and trained personnel in a laboratory setting and are, as such, not suited for an easy-access screening program. It is therefore of paramount importance that portable devices be engineered that would allow for individual sample collection and processing while maintaining the result interpretation for a trained professional in a medical research laboratory.

Notably, most clinical research on SA levels in saliva and their connection to various types of cancer has been carried out using classical lab-based approaches, such as Yao’s method30. These methods are well developed and produce excellent results. However, they require the presence of medical professionals and lab-trained personnel to assess SA levels. Preferably, screening for cancer should be done as early as possible, and at-home solutions would be perfect for that goal. Translation of classical lab-based techniques to mobile, point-of-care, and personal solutions does not seem to be feasible, mainly because they involve dangerous chemicals, are bulky, and require expensive equipment for analysis.

State-of-the-art research on sialic acid analysis has produced intriguing solutions, such as wearable sensors for SA31 and miniaturized electrochemical sensors32. While these exciting developments show excellent performance in SA detection, they are rarely employed in real samples of actual cancer patients.

On the other hand, some developments go hand-in-hand with human studies. For example, a novel SERS-based method to detect SA levels in saliva was developed15,33, which was validated with patients with cancer or other diseases. It was used to uncover new links between SA and various diseases, such as breast cancer16. Raman spectroscopy is already widely used in various handheld mobile applications34, and one can easily imagine that the Raman method for SA quantification can also become a mobile device.

Discussion

There is strong evidence to support the use of salivary SA to prescreen individuals for cancer, including oral and breast cancers. While the ‘Sialic Acid Quantification Methods’ section detailed the technical aspects of Raman spectroscopy and biosensors, we now turn our attention to their broader implications in cancer detection. Raman spectroscopy, particularly SERS, offers a promising, rapid, and non-invasive approach for cancer diagnostics, potentially revolutionizing point-of-care testing35. Its label-free nature reduces preparation time and preserves the integrity of the sample. However, challenges exist in its application in complex biological systems and ensuring specificity. Biosensors, known for their high specificity, could complement Raman spectroscopy by providing targeted detection, especially in heterogeneous samples36. Integrating these technologies could pave the way for more comprehensive diagnostic platforms. Future research should focus on overcoming the limitations of each method and exploring their combined potential in clinical applications.

The reviewed studies reinforce a strong positive relationship between cancer stage (oral and breast) and salivary SA levels. Salivary SA testing is not only useful for detecting cancer and identifying early asymptomatic or precancerous stages but also for monitoring treatment responses. As levels decline in treated patients, salivary SA could serve as a prognostic marker and a tool for monitoring treatment effectiveness and disease progression37. Additionally, salivary SA levels might be valuable as prescreeners for cancer in asymptomatic patients, leading to more diagnostic measures like mammograms and tissue biopsies.

By increasing accessibility to timely prescreening, salivary SA testing could save many lives. Furthermore, mammograms use radiation, which is harmful and can slightly increase the risk of developing cancer in the long run14,38. Thus, women who would otherwise not have undertaken a mammogram (because they belong to a lower-risk group) may find it easier to simply test their saliva for SA levels and then make informed decisions about follow-up imaging and biopsy procedures.

Individuals at risk of oral cancer, including those who chew tobacco39 or have human papillomavirus (which slightly increases the risk of oral tumors)40, may benefit from simple and noninvasive salivary testing. Dentists are usually the first point of contact for diagnosing oral cancer, but not all patients have equitable access to dental care12,14. By undertaking a simple, noninvasive test, individuals may gain priority access to oral cancer care, including those who would have avoided visiting the dentist. It is also important to further establish whether both free and bound salivary SA levels should be evaluated, given the potential for false positives in tobacco chewers without cancer found in this review. One study found that bound levels were significantly different in those with cancer versus those without, suggesting that this may be the preferred measure41.

More research is needed to better understand whether salivary SA testing may be used to detect ovarian cancer, as one study supported its potential applicability33. Further evaluation of salivary SA in relation to other solid tumors is also of interest. After hypothetically using this form of testing, patients can make informed decisions based on their family history, risk factors, and symptoms regarding whether to undertake further confirmatory and specification tests.

As shown in Table 1, different ranges of salivary SA levels and supported evidence that higher levels are found in patients with cancer, especially at later stages. However, it is important to determine reliable cut-off levels for the diagnostic criteria and to establish whether the levels vary across populations according to race, ethnicity, and sex. Given the promising results discussed above, it would be beneficial to make this form of testing more available to patients at medical clinics.

Table 1 Scientific literature showcases instances of salivary sialic acid as a biomarker for the detection of solid tumors in the breasts, ovaries, and oral cavity
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For the assay of free, protein-bound, and/or total salivary SA, most scoped studies use variations of spectrophotometric assays employing either Gaitonde’s acid ninhydrin reagent reaction developed by Yao and colleagues30 or Skoza and Mohos’ protocol, which uses periodate, sodium arsenite, and thiobarbituric acid42. These methods were developed in the 1970s–1980s and are well-known for SA analysis, and some are still considered to be reference techniques for detection of this biomarker in saliva.

While quite precise and chemically simple, these methods have certain disadvantages. The number and complexity of manipulations, special chemical reagents, and the need for a spectrophotometer to read the final results make these protocols useable only by trained professionals in a clinical lab setting43.

New and portable SA quantification methods

All the above-mentioned SA quantification methods are qualified to be designed into a portable device. Chemical reactions can be performed in microvessels, and the addition of substances can be controlled and driven within microfluidic channels and microvalves. Photometers and fluorescence readers for detection have already been miniaturized and can be combined with the necessary chemical reaction chambers44. Nevertheless, portable devices that measure SA should encompass a quantification method that is reasonably fast, specific, and sensitive. Such methods include surface-enhanced Raman spectroscopy, for which various approaches have been studied15,33,45,46. One advantage of this methodology lies in its minimized sample preparation steps. Furthermore, various types of biosensors28,29,47 would also lend themselves to portable devices.

While salivary SA analysis has great potential for the pre-diagnostic detection of cancers, methodological complexity may preclude the introduction of such screening in everyday clinical analysis, and even more so in patient-side and/or personalized use. For example, a molecularly imprinted polymers based sensor was reported to have high selectivity, stability, sensitivity and reproducibility47. Such a method for SA detection can be portable and adapted for personalized use, as no sample processing is required.

A sensor for SA based on optic fibers modified by magnetic nanoparticles was also developed48, with a response time of a few seconds, which is significantly faster than gold standard techniques, which can take up to 40 min per sample. Another state-of-the art device, a capillary sensor based on UiO-66-NH2 metal-organic framework, has shown operation in microvolume (15 μl) analysis of SA in a rapid, reliable fashion49. These and many other recent developments in novel SA sensing techniques have yet to find applications in clinical validation studies. However, these exciting developments show great promise for the translation of salivary SA assays to more portable solutions, which would not require trained professional attention, and ultimately may be used by patients themselves in home settings for potentially real-time analysis of SA levels in saliva for early cancer detection.

Raman spectroscopy is a technique that measures the scattering of laser light by molecules within a sample50. It provides valuable information about the chemical composition and molecular structure of the analyzed substance50. It can identify specific chemical bonds and functional groups and even detect trace amounts of substances. Raman spectroscopy is widely used for qualitative and quantitative analyses in various fields, including pharmaceuticals, forensics, materials science, and environmental monitoring51.

In terms of portability, both portable spectrophotometers and portable Raman spectrometers are designed for on-site or field applications. However, portable Raman spectrometers are typically more compact and handheld52,43, allowing for easy mobility and measurements in real time at the point of analysis.

A portable Raman spectrometer is particularly suitable for SA analysis, providing molecular-specific information for the identification and analysis of specific compounds, including SA. Significant advancements have been made in the precise detection of SA in untreated saliva using Raman spectroscopy, specifically SERS15,16,53. Moreover, studies involving cancer patients have successfully utilized this technology15,16. Given the ease of use and widespread adoption of handheld Raman spectrometers, they have promising potential for portable SA detection in the future.

Nevertheless, there are a few things to consider when aiming at using Raman spectroscopy to determine SA levels to predict cancer disease status. Raman spectroscopy is mainly used as a qualitative analytical method whereby the Raman shifts are specific to a given analyte;50 an example of Raman shifts obtained via a portable SERS device is schematically shown in Fig. 1. Quantitative statements based on the area under the curve are more difficult to make, as these statements are dependent on the amount of incoming electromagnetic waves and the medium in which the analyte resides. The composition of saliva is different from person to person, and thus poses a problem in interpreting the results quantitatively5. To correct for these differences, a specific, yet to be determined, internal reference of known amount must be added to the saliva samples5. Also, the analytical volume must be normalized to obtain reproducible results. We envisage a device in which the patient’s saliva is collected. By closing the device, pressure would be applied to the saliva sample such that it passes through a rough filter that would retain particulate matter into a well with a defined volume. The well would contain the internal standard substance in dried form (like a tablet) and would be solubilized when in contact with the saliva sample (Fig. 1).

Fig. 1: Illustration of a portable Raman spectrometer for cancer detection.
figure 1

The figure highlights the entire process, commencing with saliva collection through a portable Raman spectrometer and smartphone, ending with a personalized feedback for the user. Notably, the proposed portable device will be web-connected to transmit Raman shifts for more accurate analysis, updating the machine learning algorithm online. Subsequently, a probability of potential cancer disease will be conveyed, along with a comprehensive report, to the hospital/clinic. A clinician will then directly contact the patient to inform them of the results and discuss next steps. In cases where the results are favorable and no urgent outcome is indicated, a message will be directly sent to the user.

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Figure 1 outlines the process of using a portable Raman spectrometer for cancer detection. It starts with the collection of saliva samples, and then shows how the portable Raman spectrometer is employed, highlighting the application of a laser to SA, which leads to the generation of spectrum plots containing Raman shift curves. Lastly, the diagram demonstrates the integration of cloud computing, enabling the provision of personalized health advice tailored to medication intake or the necessity of consulting a clinician.

Raman spectrometry has the potential to yield precise results in saliva analysis, although the selection between the two methods relies on the particular application and technique employed. Raman spectrometry stands out for its exceptional accuracy and specificity in conducting molecular analyses, making it an ideal choice for in-depth molecular characterization. However, this proposal comes with the following challenges and opportunities.

Challenge 1: instrumentation size and complexity of internal reference

The current state of traditional SERS systems poses several challenges that hinder their widespread adoption and usability23. These challenges include their bulky and complex nature, as they often require sophisticated laser sources and spectrometers, making them less suitable for portable and point-of-care applications35.

Another challenge in performing SERS measurements arises from the need for the selection and quantity of one or more reference substances. This consideration is essential for the standardization of quantitative analysis35.

Additionally, sample preparation methods play a critical role in SERS applications,35 especially in point-of-care settings. Simplifying and standardizing sample preparation methods, such as using a Y-shaped cup or, as proposed in Fig. 1, placement at the bottom of the test tube, is essential for enhancing the practicality and reproducibility of SERS measurements.

To address these challenges and enable more user-friendly and accessible SERS technology, efforts36 are being made to connect biosensors to smartphones. This offers the potential to create portable and user-friendly platforms for real-time analysis and diagnostics with the ability to capture and analyze Raman spectra conveniently on mobile devices.

Challenge 2: sensitivity and specificity

In the realm of cancer detection, achieving high sensitivity and specificity in a portable SERS device is of paramount importance. Accurate and reliable cancer detection relies on the system’s ability to discern subtle changes in SA levels associated with cancer while avoiding false positives, which poses a significant challenge.

To address this challenge and enhance the capabilities of portable SERS devices for cancer detection, advanced machine learning algorithms and data analysis techniques have come into play. These powerful tools can be applied to develop robust and accurate cancer classification models based on Raman spectra; a visualization of this process is shown in Fig. 1.

One approach to explore is the utilization of deep learning methods54, which can extract complex patterns and relationships from Raman spectral data, potentially leading to improved detection accuracy. Another avenue worth investigating is feature selection, in which specific relevant features are identified and utilized to enhance the discriminative power of classification models. By integrating cutting-edge machine learning and data analysis techniques into portable SERS devices, we can unlock their full potential as powerful tools in the fight against cancer.

Challenge 3: costs

Foremost among these challenges is the cost factor, with the current price of handheld SERS systems exceeding $20,000 (https://optosky.com/portable-raman.html). Developing a portable version of the SERS system without compromising its sensitivity and accuracy requires substantial investment in research and development. Engineers and scientists need to find innovative solutions to streamline the manufacturing process and utilize cost-effective materials and analysis software without compromising the system’s performance. By successfully reducing the cost of the SERS system, its adoption in clinical settings can be significantly enhanced, bringing the promise of early cancer detection closer to reality for a broader population of patients.

Challenge 4: integration and adoption

While the scientific merits of SERS based testing are evident, the successful integration into existing healthcare infrastructure demands a multifaceted approach, as shown in Fig. 1. The introduction of a new diagnostic to take the place of existing methods has been historically difficult48, however COVID-19 has expanded telehealth and diagnostics (both point of care and at home testing) creating opportunities for other tests to be introduced49. Clinical evidence is often not enough for widespread adoption into clinical practice, but also requires stakeholder input, education and training, familiarity and cost effectiveness50. Ensuring that stakeholders and users understand the long-term benefits of SA testing will be paramount to its adoption51. Adoption of a new cancer diagnostic will require industry engagement and partnership with federal programs such as cancer screening and prevention paradigms to promote implementation. Endorsements from organizations such as national cancer groups and advocacy within the medical community in tandem with commercial availablility and cost effectiveness have been shown to have population-level impact43.

Opportunity 1: personalized medicine

The proposed SERS-based cancer detection portable device has immense potential for revolutionizing the landscape of personalized cancer treatment. It is very likely that additional cancer biomarker(s) will be identified in saliva and possibly in other easily accessible body fluids such as urine in the near future. The combinations of all biomarker measurements can make cancer diagnostics much more discriminative and as such will support personalized patient treatment. Alternatively, with the use of advanced machine learning capabilities, Raman shift patterns of saliva may prove unique for a cancer type, grade and stage and can also indicate patient specific properties important to the therapeutic treatment similar to its use as surgery guidance tool to distinguish cancer cell from healthy cells.

By leveraging such point-of-care SERS-based cancer detection devices, medical professionals can make more informed and precise treatment decisions tailored to individual patient’s needs. Personalized treatment strategies have been shown to improve treatment outcomes, minimize adverse effects, and enhance the overall quality of life for cancer patients. These benefits have been observed across various ethnicities and age-matched populations, highlighting the universal applicability of personalized approaches (shown in Fig. 2).

Fig. 2: Conceptualizing the network of portable Raman devices to enable remote collaboration and clinical translation for cancer detection.
figure 2

Creating a network integrated with a comprehensive database will enable the collection of Raman shifts and demographic information from multiple users with diverse age, sex, and ethnicity. This collected data can be made accessible to researchers, hospitals, and the machine learning model. Regular updates to the machine learning model will encompass various cancer types and consider different age groups, sexes, and ethnicities, enhancing the reliability and scalability of automated cancer prescreening.

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Opportunity 2: multiplexing capabilities

The possibility of portable SERS technology with multiplexing capabilities presents a groundbreaking advancement in cancer diagnosis, with the potential to transform the field of oncology. By enabling the simultaneous detection of multiple biomarkers in saliva, such as those associated with breast cancer, oral cancer, and ovarian cancer, the diagnostic power of the device is significantly enhanced. A visualization of this process is shown in Fig. 1.

Portable SERS devices equipped with multiplexing capabilities offer a minimally invasive and rapid means of obtaining a comprehensive molecular profile of a patient’s tumor, aiding in the early detection and precise characterization of the cancer. This multifaceted approach not only streamlines the diagnostic process, but also provides crucial insights into the tumor’s molecular composition, enabling oncologists to tailor personalized treatment plans based on the specific cancer subtype and individual patient characteristics. Moreover, the portability of the SERS system empowers healthcare professionals to conduct on-site diagnostics, even in resource-limited or remote settings, thereby improving access to accurate and timely cancer screening.

Opportunity 3: remote collaboration via a network

Portable SERS devices offer the potential to bridge geographical gaps and facilitate remote collaboration between healthcare providers and specialists, revolutionizing the way expert opinions and second consultations are sought. With these devices in hand, healthcare professionals can capture detailed molecular information from a patient’s tumor sample and transmit the data securely to specialists in other locations. This enables remote experts to analyze SERS spectra and provide valuable insights and recommendations for diagnosis and treatment options. Such collaborative efforts can improve diagnostic accuracy and ensure that patients receive the most appropriate and tailored care, regardless of their geographic location.

To fully realize the potential of portable Raman spectroscopy in cancer detection, Fig. 2 visualizes how extensive clinical studies and validation trials are essential. Collaboration with clinical researchers and oncologists can facilitate the evaluation of the device’s performance in large and diverse patient cohorts, encompassing different cancer types and stages. This process, shown in Fig. 2, will provide valuable data on the diagnostic accuracy, sensitivity, and specificity of portable SERS devices, ensuring their reliability and effectiveness in real-world clinical scenarios. Additionally, the inclusion of diverse populations and different ethnicities in these studies will enhance the generalizability of the technology, enabling equitable and effective cancer detection across various patient demographics. Through rigorous clinical translation and validation, portable SERS devices can solidify their role as transformative tools in cancer diagnostics and contribute to improving patient outcomes on a global scale.

Opportunity 4: longitudinal monitoring

The ability to monitor simulatenous detection of SA and potential other biomarkers over an extended period would be a game-changer in understanding disease progression, treatment responses, and potential recurrence. For example, AI can be applied to healthcare in predicting patient hospital readmissions to identifying patterns in complex medical datasets offering a glimpse into AI’s potential in biomarkers testing53,54,55. However, for this to be a viable plan, patients receiving treatment must understand the importance of regular testing while also going through a process they find convenient and non-intrusive. Further areas of opportunity with salivary biomarkers monitoring could also be integrated into digital health platforms.

After discussing the potential of AI in predicting patient hospital readmissions and identifying patterns in complex medical datasets, it’s important to further clarify the specific role of AI in relation to SA and its potential in biomarker discovery. While SA is a known biomarker for oral cancer, and its detection might often require only setting a diagnostic threshold based on the intensity of SA-specific peaks, AI can play a more nuanced role in this context. Particularly, AI and machine learning algorithms can be crucial in correlating spectral data with patient outcomes, which involves a more complex analysis than simply measuring SA levels.

This capability of AI extends beyond mere quantification of SA. By analyzing vast datasets, AI can help in identifying new patterns and correlations, potentially leading to the discovery of additional biomarkers related to oral and other cancers. In this respect, the use of SERS combined with AI analytics should not be viewed solely as a tool for SA quantification or as a ‘black-box’ for cancer screening. Instead, its value lies in the potential to expand the scope of biomarker identification, offering a more comprehensive understanding of cancer pathogenesis and progression.

Therefore, the integration of AI in longitudinal monitoring, particularly when combined with SERS, opens new avenues for the identification and validation of additional biomarkers. This expanded approach could further refine cancer screening and diagnostic methods, moving beyond the current focus on SA alone. Future research and development in this area could greatly enhance the utility of salivary biomarkers in cancer detection, offering more personalized and precise diagnostic tools.

The digital health platforms that are currently available that track fitness, nutrition, or chronic diseases often employ gamification, rewards, or community support to engage users56. These strategies could be adapted for salivary biomarkers testing, specifically with a SERS system. While salivary biomarkers testing can significantly shift the diagnostic and monitoring paradigm, it is just one piece in the larger puzzle of comprehensive cancer care. Patient education, counseling, post-diagnosis care, and continued research into treatment modalities are equally vital57,58. Personality engagement is also relevant in longitudinal monitoring. For example, beyond generic reminders, personalized messages based on a patient’s data might offer more compelling motivation. For instance, visualizing how their salivary biomarkers change over time, correlated with lifestyle choices, could be insightful. The case is similar with community building as establishing patient communities where individuals can share experiences, ask questions, and offer support can bolster long-term engagement.

Salivary biomarkers, particularly sialic acid testing, hold significant promise in the field of cancer detection. Our research emphasizes the potential of this method, especially when combined with advancements in portable Raman spectroscopy and AI analysis. These technologies can enhance the accuracy and convenience of salivary SA testing, potentially revolutionizing early cancer detection. However, the full potential of salivary biomarkers can only be realized through rigorous research and consistent patient engagement. Our focus remains on refining these methods to ensure they are not only scientifically sound but also patient-friendly, facilitating early diagnosis and effective management of cancer.

Outlook

The SERS technique, as well as developments in molecularly imprinted polymers and optic fibers modified by magnetic nanoparticles, show promise for portable and rapid salivary SA detection. These advancements have the potential for future clinical validation studies.

Recommendations for enhancing cancer detection using portable salivary SA testing are as follows:

  1. 1.

    Continue investigating the relationship between Raman shifts and cancer for oral, ovarian, and breast cancers. Explore the potential of salivary SA testing for detecting additional cancer types.

  2. 2.

    Determine specific and reliable Raman shift features using machine learning for SA to establish diagnostic criteria for different cancer grades and stages and different populations based on race, ethnicity, and sex.

  3. 3.

    Validate and compare different measurement techniques such as SERS technique, molecularly imprinted polymers, and optic fibers modified by magnetic nanoparticles for portable salivary SA analysis.

  4. 4.

    Increase awareness and accessibility of portable salivary SA testing.

  5. 5.

    Carry out large-scale clinical validation to determine the effectiveness of portable testing.

  6. 6.

    Develop portable SERS devices with the ability to detect multiple biomarkers in saliva (in addition to sialic acid) simultaneously, such as those associated with different cancer types. Multiplexing can enhance diagnostic power and streamline the screening process.

  7. 7.

    Enable remote collaboration between healthcare providers and specialists by integrating portable SERS devices into a network.

Salivary sialic acid analysis holds great promise as a non-invasive and early diagnostic tool for various cancers, including oral and breast cancers. Classical lab-based approaches have yielded excellent results in research, but their translation to portable, point-of-care solutions poses challenges due to complexity and costly equipment. State-of-the-art research on SA analysis, including wearable sensors and miniaturized electrochemical sensors, requires further validation with real cancer samples. Portable Raman spectroscopy, particularly surface-enhanced Raman spectroscopy, shows remarkable potential for SA detection. Overcoming challenges related to device size, complexity, cost, while ensuring sensitivity and specificity is essential. Portable SA testing offers opportunities for personalized medicine, multiplexing, and remote collaboration among healthcare providers. Large-scale clinical validation is vital for establishing its reliability. Embracing and advancing portable SA testing can significantly enhance cancer screening, early detection, and personalized treatment options.