These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

The need for novel biomarkers that could aid in non-small cell lung cancer (NSCLC) detection, together with the relevance of Matrix Metalloproteases (MMPs) -1, -2, -7, -9 and -10 in lung tumorigenesis, prompted us to assess the diagnostic usefulness of these MMPs and the Tissue Inhibitor of Metalloproteinase (TIMP) -1 in NSCLC patients. Markers were evaluated in an initial study cohort (19 NSCLC cases and 19 healthy controls). Those that better performed were analyzed in a larger sample including patients with benign lung diseases. Serum MMPs and TIMP-1 were determined by multiplexed immunoassays. Logistic regression was employed for multivariate analysis of biomarker combinations. MMPs and TIMP-1 were elevated in the serum of NSCLC patients compared to healthy controls. MMP-1, -7 and -9 performed at best and were further evaluated in the sample including benign pathologies, corroborating the superiority of MMP-9 in NSCLC discrimination, also at early-stage NSCLC. The optimal diagnostic value was obtained with the model including MMP-9, gender, age and smoking history, that demonstrated an AUC of 0.787, 85.54% sensitivity and 64.89% specificity. Our results suggest that MMP-9 is a potential biomarker for NSCLC diagnosis and its combined measurement with other biomarkers could improve NSCLC detection.

Taking advantage of eight established cell lines from colorectal cancer patients at different stages of the disease and the fact that all of them could form spheres, cell surface biomarkers of cancer stem cells and epithelial-mesenchymal transition were tested. The aim was to investigate cancer stem cells and metastatic stem cells in order to provide functional characterization of circulating tumor cells and promote the development of new anti-metastatic therapies. Our model showed an important heterogeneity in EpCAM, CD133, CD44, LGR5, CD26 and E-cadherin expression. We showed the presence of a subset of E-cadherin⁺ (some cells being E-cadherin^high) expressing CD26⁺ (or CD26^high) together with the well-known CSC markers LGR5 and EpCAM^high, sometimes in the absence of CD44 or CD133. The already described CD26⁺/E-cadherin^low or ^negative and CD26⁺/EpCAM⁻/CD133⁻ subsets were also present. Cell division drastically affected the expression of all markers, in particular E-cadherin, so new-born cells resembled mesenchymal cells in surface staining. CD26 and/or dipeptidyl peptidase 4 inhibitors have already shown anti-metastatic effects in pre-clinical models, and the existence of these CD26⁺ subsets may help further research against cancer metastasis.

Lateral flow immunoassays (LFIAs) are easy‐to‐use antigen tests that provide different signal readouts, with colorimetric readouts being the most commonly used. However, these analytical devices have relatively low sensitivity and produce semiquantitative results, limiting their diagnostic applications. Herein, we address these challenges by implementing a digital surface‐enhanced Raman spectroscopy (SERS)‐based LFIA for the accurate and ultrasensitive quantitative detection of SARS‐CoV‐2 nucleocapsid (N) protein. Compared with average SERS intensity measurements, the digital approach allowed to overcome fluctuations in Raman scattering signals, thereby increasing the analytical sensitivity of the assay. Our method exhibited a quantification range of the viral protein in nasal swabs from 0.001 to 10 pg mL⁻¹, and a limit of detection down to 1.9 aM (0.9 fg mL⁻¹), improving colorimetric LFIAs and conventional‐SERS‐based LFIAs by several orders of magnitude. Importantly, this approach shows an analytical sensitivity of 0.03 TCID₅₀ mL⁻¹, which is greater than that reported by other immunoassays. In conclusion, we successfully demonstrate the robust detection and quantification of SARS‐CoV‐2N protein in nasal swabs at ultralow concentrations. The improvement in the sensitivity of LFIA by digital SERS may pave the way to translate this technology into the diagnostic arena for the ultrasensitive detection of microbes and disease biomarkers.