Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Machine learning assisted raman spectroscopy for the classification of ovarian cancer cells

Ovarian cancer is one of the most lethal gynecological malignancies, asymptomatic early progression, ineffective screening, and high histological heterogeneity. Accurate subtype classification and detection of chemotherapy resistance are critical for guiding personalized treatment strategies. Raman spectroscopy offers a label-free, non-destructive means of capturing biochemical fingerprints of cells, but its clinical potential is hindered by high spectral complexity and subtle inter-class variations. This study presents a machine learning-assisted Raman spectroscopy framework for the classification of ovarian cancer cell subtypes and their cisplatin resistance phenotypes. Raman spectra were acquired from normal ovarian epithelial cells (IOSE-80), four ovarian cancer cell lines (A2780, SKOV3, OVCAR-3, ES-2), and cisplatin-resistant variants (A2780-DDP, SKOV3-DDP). Three computational models were developed and systematically compared: a principal component analysis-support vector machine (PCA-SVM) algorithm and two convolutional neural network (CNN-Enhance and CNN-BiLSTM). Classification performance was assessed across three tasks: (i) discrimination of normal versus malignant cells, (ii) differentiation of cancer cells from their cisplatin-resistant variants, and (iii) classification of distinct cancer subtypes. Results show that Raman spectra reveal distinctive biochemical differences between normal and malignant cells, particularly in protein-, lipid-, and nucleic acid-related peaks. Both PCA-SVM and CNN achieved high classification accuracy (>90%) in most tasks, with PCA-SVM demonstrating greater stability and superior performance in subtype classification, while CNN showed advantages in specific cell-type detection. Notably, PCA-SVM achieved up to 100% accuracy in differentiating cisplatin-resistant phenotypes. These findings demonstrate that integrating Raman spectroscopy with machine learning enables label-free, and accurate classification of ovarian cancer subtypes and drug resistance, offering a promising pathway toward minimally invasive precision diagnostics and personalized cancer treatment planning.

Discovery of an acridine-based fluorescent ligand for selectively targeting mitochondrial G-quadruplexes DNA and triggering cervical cancer cell death

Mitochondrial G-quadruplexes (mtG4s) have recently emerged as a highly promising therapeutic target in oncology. This is because the frequency of their formation in the mitochondrial DNA (mtDNA) of cancer cells is significantly higher than that in normal cells. The stabilization of mtG4s induces instability in mtDNA, which subsequently triggering mitochondrial dysfunction and programmed cell death-a mechanistic paradigm that unveils novel avenues for cancer therapy. Despite promising prospects, the development of specific ligands targeting mtG4s remains a substantial unaddressed challenge. In this study, we designed and synthesized an acridine derivative, YWB-6. YWB-6 demonstrated remarkable mitochondrial targeting specificity and inherent fluorescence properties. Biophysical characterization confirmed that YWB-6 selectively recognizes and stabilizes mtG4 structures with high affinity. In SiHa cervical cancer cell models, YWB-6 exhibited precise binding to mtG4, leading to mitochondrial membrane integrity disruption, ATP depletion, ROS imbalance, and ultimately cell cycle arrest and apoptosis. Mechanistic investigations revealed that YWB-6 exerts its antitumor effects primarily through mtG4-mediated mitochondrial dysfunction. Cellular assays further verified its potent antitumor activity, with IC

Data science meets FTIR Imaging: a promising probe to improve the diagnosis of human uterine muscle lesions

Uterine smooth muscle tumors include a broad range of neoplasms, from benign leiomyomas (LMs) to malignant leiomyosarcomas (LMS), as well as intermediate forms classified as Smooth Muscle Tumors of Uncertain Malignant Potential (STUMP). An accurate diagnosis of these tumor types is essential for their appropriate clinical management; however, it remains challenging due to possible overlapping of histological features. In this study, a multidisciplinary approach combining Fourier Transform Infrared Imaging (FTIRI) spectroscopy, a label-free and non-destructive analytical technique, with histology and statistical analyses have been exploited for investigating the morpho-chemical characteristics of these uterine smooth muscle tumors. The analysis aimed to identify new reliable and diagnostic spectral markers, complementary to traditional histology, and thus useful for improving accuracy in cases with uncertain morphological features. Tissue samples including different leiomyoma histological subtypes, such as usual, cellular, apoplectic, and bizarre, were analyzed and compared with LMS and healthy myometrium. The analysis of IR data, submitted to univariate and multivariate statistical approaches, such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), revealed distinctive spectral profiles associated with each tumor type and indicated changes in collagen content and organization as key features for a reliable discrimination not only between benign and malignant tissues but also among different LM histotypes.

A high biocompatible near-infrared fluorescent probe for tracking cysteine in multi-biosystem and its application in cervical cancer imaging

Cysteine (Cys) plays a crucial role in the biological system and many related diseases. However, the detection of Cys in living organisms are still hindered by shortage of small molecule fluorophores that exhibit excitation and emission in the near-infrared region. Herein, we designed and synthesized a high water-soluble Cys probe (Cy7-SS) based on heptamethine cyanine scaffold. The prepared Cy7-SS displayed an enhanced emission at near-infrared region (NIR) after the recognition of Cys. Moreover, Cy7-SS not only exhibited high selectivity and sensitivity on the detection of in vitro Cys, but also could be used for endogenous Cys in living cells and C.elegans. The prepared strategy for the design of fluorophores expands the in vivo sensing toolkit for the precise analysis in clinical diagnosis.

Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm

Spectroscopic investigations, quantum chemical, drug likeness and molecular docking studies of methyl 1-methyl-4-nitro-pyrrole-2-carboxylate: A novel ovarian cancer drug

Density functional theory (DFT) calculation was used to analyse the structural and vibrational properties of Methyl 1-Methyl-4-nitro-pyrrole-2-carboxylate (MMNPC) using the cc-pVTZ basis set. The potential energy surface scan and the most stable molecular structure were optimized using Gaussian 09 program. A potential energy distribution calculation was used to calculate and assign vibrational frequencies using the VEDA 4.0 program package. The Frontier Molecular Orbitals (FMOs) were analysed to determine their related molecular properties. Ab initio density functional theory (B3LYP/cc-pVTZ) method with basis set was used to calculate

SPRi/SERS dual-mode biosensor based on ployA-DNA/ miRNA/AuNPs-enhanced probe sandwich structure for the detection of multiple miRNA biomarkers

MicroRNA (miRNA) has broad application prospects in the early detection of various cancers. In this work, a SPRi/SERS dual-mode biosensor was developed on the same gold chip by AuNPs as the reinforcing medium. High throughput and sensitivity detection of three typical cervical cancer markers miRNA21, miRNA124 and miRNA143 were achieved based on the sandwich structure of polyA blocks-DNA capture probe/target miRNA/AuNPs-assistant probe or SERS nanoprobes. AuNPs greatly improved the SPR response due to mass increase and more sensitive refractive index changes. Meanwhile, due to the LSPR effect of AuNPs, the signal of SERS nanoprobe can be amplified. The miRNAs were detected in serum to verify its practicality. SPRi achieved detection of three miRNAs simultaneously. LODs were 6.3 fM, 5.3 fM and 4.6 fM, respectively, and wide dynamic response range of 500 pM-10 nM. While SERS assay ensured high sensitivity with LODs as low as 1 fM, 0.8 fM and 1.2 fM, respectively, and with the recoveries in the range of 90.0 %-100.2 %. The redundant detection signals of the two modes can provide more reliable data to prevent false positive or false negative detection, and have great application prospects in detection of cancer-related nucleic acids in early stage of disease.

Advanced artificial intelligence combined with SERS platforms for diagnosis and therapeutic effects of cancer in clinical applications

Cervical cancer diagnosis model using spontaneous Raman and Coherent anti-Stokes Raman spectroscopy with artificial intelligence

Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm

Lysophosphatidic acid responsive photosensitive supramolecular organic frameworks for tumor imaging, drug loading, and photodynamic therapy

Supramolecular organic frameworks have been widely applied for biological detection and drug delivery. In this study, a supramolecular organic framework (SOF) is constructed through the self-assembly of a highly photosensitive triarylphosphine oxide guest molecule, OTPP-6-Methyl, with cucurbit [8] uril (CB [8]). The formation of the SOF gradually enhances the weak fluorescence of OTPP-6-Methyl owing to the restriction of the molecular folding motion. Although the high positive charge of OTPP-6-Methyl facilitates binding to various negatively charged substances, the SOF system only demonstrated an obvious fluorescence response to LPA, a biomarker of ovarian cancer, via the disassembly of SOF and subsequent binding of OTPP-6-Methyl with LPA. The fluorescence changes during the entire process are insufficient to allow the sensitive detection of LPA; thus, we further designed a FRET system by introducing Cy5, which can act as an energy receptor to achieve a ratiometric readout for LPA. The tumor-targeting cRGD group was introduced into the SOF system as part of another guest molecule, OTPP-5-M-1-cRGD, to improve the tumor-targeting ability of the SOF system. The SOF system further improves the photosensitivity of guest molecules, and is therefore used in the in vivo imaging of ovarian cancer subcutaneous tumors and as a DDS for loading DOX for the combined in vivo chemotherapy and photodynamic treatment of tumors.

A novel NIR fluorescent probe for enhanced β-galactosidase detection and tumor imaging in ovarian cancer models

The advancement of biological imaging techniques critically depends on the development of novel near-infrared (NIR) fluorescent probes. In this study, we introduce a designed NIR fluorescent probe, NRO-βgal, which exhibits a unique off-on response mechanism to β-galactosidase (β-gal). Emitting a fluorescence peak at a wavelength of 670 nm, NRO-βgal showcases a significant Stokes shift of 85 nm, which is indicative of its efficient energy transfer and minimized background interference. The probe achieves a remarkably low in vitro detection limit of 0.2 U/L and demonstrates a rapid response within 10 min, thereby underscoring its exceptional sensitivity, selectivity, and operational swiftness. Such superior analytical performance broadens the horizon for its application in intricate biological imaging studies. To validate the practical utility of NRO-βgal in bio-imaging, we employed ovarian cancer cell and mouse models, where the probe's efficacy in accurately delineating tumor cells was examined. The results affirm NRO-βgal's capability to provide sharp, high-contrast images of tumor regions, thereby significantly enhancing the precision of surgical tumor resection. Furthermore, the probe's potential for real-time monitoring of enzymatic activity in living tissues underscores its utility as a powerful tool for diagnostics in oncology and beyond.

Highly sensitive detection of cervical cancer biomarker miR-21 using surface-enhanced Raman scattering (SERS)

Cervical cancer's frequent late-stage diagnosis due to asymptomatic progression necessitates advanced early detection tools. This study pioneers a SERS platform for ultrasensitive detection of miR-21-a key cervical cancer biomarker-combining thiol-modified DNA probes for high-affinity miR-21 capture with AgNP-enhanced SERS substrates that convert hybridization events into amplified Raman signals. The platform achieves sensitivity in serum and demonstrates exceptional stability through DNA-RNA duplex formation, enabling robust reproducibility in complex biofluids. Unique spectral fingerprints enable precise target quantification while exhibiting promising potential for non-invasive lesion severity prediction. By overcoming critical limitations of conventional methods-including Pap smear's low sensitivity and HPV testing's inability to detect active cancer-this approach offers a rapid, specific liquid biopsy solution to enhance early diagnosis, dynamic monitoring, and clinical outcomes in cervical cancer management.

Aggressiveness evaluation of borderline serous ovarian tumors by analysis of Psammoma bodies present in cancer tissues using micro-FTIR spectroscopy

Borderline ovarian tumor is a type of tumor with generally low malignant potential. However, these tumors pose diagnostic challenges with benign and malignant epithelial ovarian tumors because the clinical symptoms are similar and investigation procedures for specific diagnosis are still debated. In addition, a small number of borderlines transform into high-grade serous ovarian carcinoma with a poor prognosis. Therefore, tools improving a better characterization of high-risk subtypes of borderline tumors to enable understanding of possible unfavorable evolution are essential for patients' management. Psammoma bodies (PBs) are microcalcifications found both in serous epithelial ovarian cancer and serous borderline tumors with possible correlation with disease progression. In this work, the chemical composition of PBs found in the tissues of borderline, high-grade and low-grade ovarian tumors was evaluated using micro-FTIR spectroscopy. Applying principal component analysis to spectral data, it was observed that among the borderline tumors analyzed (1-bl,2-bland3-bl), the PBs of3-blshowed a different chemical content from that of the PBs of the high-grade and low-grade tumors, while the PBs of1-bland2-blappeared to have similar chemical content to the PBs of high- and low-grade tumors. The discriminating wavenumbers were found to be those related to carbonate CO

Fourier transform infrared microspectroscopy analysis of ovarian cancerous tissues in paraffin and deparaffinized tissue samples

Ovarian cancer is one of the deadliest cancers occurring in women. This is typically due to late diagnosis of the disease and difficult treatment. Infrared microspectroscopy is a complementary research method that can be helpful in the diagnosis of this disease, because it allows for the analysis of the tissues biomolecular composition. In this study, archival paraffin-embedded preparations of ovarian tissues, tumours and control, were used. However, the paraffin present in such specimens is a strong absorber of infrared radiation, which makes it impossible to reliably analyse the biomolecular composition of the sample. The solution to this problem is to deparaffinize the tissue before the analysis. However, the extend to which the paraffinization and deparaffinization processes influence the biomolecular composition of the tissues is unclear. Analysed tissues in the form of cores were placed in a paraffin micromatrix and FTIR measurements were performed. Then the samples were deparaffinized and the measurements were taken again. For both sets of samples (embedded in paraffin and deparaffinized) ratios of integrated peaks and massifs within the obtained spectra were calculated. The obtained ratios were compared for different types of diseased and healthy, control tissues. The Kruskal-Wallis test revealed statistically significant differences of the calculated ratios between most of the types of tissues. Random Forest models clearly showed that both samples in paraffin and deparaffinized retain enough information to classify the tissues reliably. The feature analysis revealed the most important feature for distinguishing between different types of samples, i.e. 1080 cm

Diagnostic and prognostic application of Raman spectroscopy in carcinoma cervix: A biomolecular approach

Blood serum samples from 63 cervical cancer patients and 30 controls were collected at three different phases of the treatment (i.e. before, during, and at follow up). The spectra of serum samples from control as well as patients were classified into different groups using principal component analysis (PCA) and linear discriminant analysis (LDA) based on different phases of treatment using R software. The spectra of blood serum samples have shown the distinct changes and differences compared with each other in the profile of various biochemical parameters. The sensitivity (92.5%) and specificity (85%) were observed maximum between control and cervical cancer patients (before treatment). Between different phases of treatment, the sensitivity and specificity were less but, all accuracies of detection and classification reached above 50%. This method can be considered as a screening method for detection and treatment monitoring.

Rhodamine hydrazide-linked naphthalimide derivative: Selective naked eye detection of Cu2+, S2− and understanding the therapeutic potential of the copper complex as an anti-cervical cancer agent

A naphthalimide-labeled rhodamine hydrazone derivative HL has been synthesized, characterized and examined in metal ion recognition. It shows selective colorimetric detection of Cu

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing

The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.

Rational design of near-infrared ratiometric fluorescent probes for real-time tracking of β-galactosidase in vivo

β-Galactosidase (β-gal) is a hydrolytic enzyme in lysosomes and is also an important biomarker of cellular senescence and primary ovarian cancer. Therefore, real-time non-invasive detection of β-gal activity in vivo is of great significance for the prevention of cell senescence and early diagnosis of ovarian cancer. We designed an enzyme-activated proportional near-infrared (NIR) probe (Gal-Br-NO