A study of structural differences between liver cancer cells and normal liver cells using FTIR spectroscopy

Saliva Based Diagnostic Prediction of Oral Squamous Cell Carcinoma using FTIR Spectroscopy

Oral cancer ranks as the sixth most prevalent form of cancer worldwide, presenting a significant public health concern. According to the World Health Organization (WHO), within a 5-year period following diagnosis, the mortality rate among oral cancer patients of all stages stands at 45%. In this study, a total of 60 patients divided into 2 groups were recruited. Group A included 30 histo-pathologically confirmed OSCC patients and Group B included 30 healthy controls. A standardized procedure was followed to collect saliva samples. FTIR spectroscopy was done for all the saliva samples collected from both Group A and B. An IR Prestige-21 (Shimadzu Corp, Japan) spectrometer was used to record IR spectra in the 40-4000 cm-1 range SVM classifier was applied in the classification of disease state from normal subjects using FTIR data. The peaks were identified at wave no 1180 cm-1, 1230 cm-1, 1340 cm-1, 1360 cm-1, 1420 cm-1, 1460 cm-1, 1500 cm-1, 1540 cm-1, 1560 cm-1, and 1637 cm-1. The observed results of SVM demonstrated the accuracy of 91.66% in the classification of Cancer tissues from the normal subjects with sensitivity of 83.33% while specificity and precision of 100.0%. The development of oral cancer leads to noticeable alterations in the secondary structure of proteins. These findings emphasize the promising use of ATR-FTIR platforms in conjunction with machine learning as a reliable, non-invasive, reagent-free, and highly sensitive method for screening and monitoring individuals with oral cancer.

Advances in far-infrared research: therapeutic mechanisms of disease and application in cancer detection

Far infrared (FIR) irradiation is commonly used as a convenient, non-contact, non-invasive treatment for diseases such as myocardial ischemia, diabetes, and chronic kidney disease. In this review, we focus on reviewing the potential therapeutic mechanisms of FIR and its cutting-edge applications in cancer detection. Firstly, we searched the relevant literature in the last decade for systematic screening and briefly summarized the biophysical properties of FIR. We then focused on the possible mechanisms of FIR in wound healing, cardiovascular diseases, and other chronic diseases. In addition, we review recent applications of FIR in cancer detection, where Fourier transform infrared spectroscopy and infrared thermography provide additional diagnostic methods for the medical diagnosis of cancer. Finally, we conclude and look into the future development of FIR for disease treatment and cancer detection. As a high-frequency non-ionizing wave, FIR has the advantages of safety, convenience, and low cost. We hope that this review can provide biological information reference and relevant data support for those who are interested in FIR and related high-frequency non-ionizing waves, to promote the further application of FIR in the biomedical field.

Diagnosis and monitoring of hepatocellular carcinoma in Hepatitis C virus patients using attenuated total reflection Fourier transform infrared spectroscopy

BACKGROUND

Current diagnostic methods for assessment of hepatitis C virus related hepatocellular carcinoma and subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, to establish appropriate treatment strategies, are costly, invasive and requires multiple screening steps. This demands alternative diagnostic approaches that are cost-effective, time-efficient, and minimally invasive, while maintaining their efficacy for screening of hepatitis c virus related hepatocellular carcinoma. In this study, we propose that attenuated total reflection Fourier transform infrared in conjunction with principal component analysis - linear discriminant analysis and support vector machine multivariate algorithms holds a potential as a sensitive tool for the detection of hepatitis C virus-related hepatocellular carcinoma and the subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma.

METHODS

Freeze-dried sera samples collected from 31 hepatitis c virus related hepatocellular carcinoma patients and 30 healthy individuals, were used to acquire mid-infrared absorbance spectra (3500-900 cm-1) using attenuated total reflection Fourier transform infrared. Chemometric machine learning techniques were utilized to build principal component analysis - linear discriminant analysis and support vector machine discriminant models for the spectral data of hepatocellular carcinoma patients and healthy individuals. Sensitivity, specificity, and external validation on blind samples were calculated.

RESULTS

Major variations were observed in the two spectral regions i.e., 3500-2800 and 1800-900 cm-1. IR spectral signatures of hepatocellular carcinoma were reliably different from healthy individuals. Principal component analysis - linear discriminant analysis and support vector machine models computed 100% accuracy for diagnosing hepatocellular carcinoma. To classify the non-angio-invasive hepatocellular carcinoma/ angio-invasive hepatocellular carcinoma status, diagnostic accuracy of 86.21% was achieved for principal component analysis - linear discriminant analysis. While the support vector machine showed a training accuracy of 98.28% and a cross-validation accuracy of 82.75%. External validation for support vector machine based classification observed 100% sensitivity and specificity for accurately classifying the freeze-dried sera samples for all categories.

CONCLUSIONS

We present the specific spectral signatures for non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, which were prominently differentiated from healthy individuals. This study provides an initial insight into the potential of attenuated total reflection Fourier transform infrared to diagnose hepatitis C virus related hepatocellular carcinoma but also to further categorize into non-angio-invasive and angio-invasive hepatocellular carcinoma.

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

PURPOSE

Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious.

METHODS

Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way.

RESULTS

Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images.

CONCLUSIONS

This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

Diagnosis of liver cancer by FTIR spectra of serum

Liver cancer is the most common fatal malignant tumor in the world. Early diagnosis of liver cancer can improve the survival rate of the patients with liver disease. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with curve fitting and chemometrics was used to distinguish the serum from patients from that of healthy people. The curve fitting results in protein range of 1700-1600 cm^-1 showed that there were differences in the secondary structure of protein in serum between the patients with liver cancer and healthy people. Principal component analysis (PCA) in lipid range of 2900-2800 cm^-1 could distinguish the serum of patients with liver cancer from that of healthy people. The first two principal components PC1 and PC2 explained 95% of the total data variance. The sensitivity and specificity of partial least squares discriminant analysis (PLS-DA) in lipid range of 2900-2800 cm^-1 reached 92.85% and 95.23% respectively. It is shown that FTIR spectroscopy might be developed as an effective method for the diagnosis of liver cancer.

Discrimination of Different Breast Cell Lines on Glass Substrate by Means of Fourier Transform Infrared Spectroscopy

Fourier transform infrared (FTIR) micro-spectroscopy has been attracting the interest of many cytologists and histopathologists for several years. This is related to the possibility of FTIR translation in the clinical diagnostic field. In fact, FTIR spectra are able to detect changes in biochemical cellular components occurring when the cells pass to a pathological state. Recently, this interest has increased because it has been shown that FTIR spectra carried out just in the high wavenumber spectral range (2500-4000 cm^-1), where information mainly relating to lipids and proteins can be obtained, are able to discriminate cell lines related to different tissues. This possibility allows to perform IR absorption measurements of cellular samples deposited onto microscopy glass slides (widely used in the medical environment) which are transparent to IR radiation only for wavenumber values larger than 2000 cm^-1. For these reasons, we show that FTIR spectra in the 2800-3000 cm^-1 spectral range can discriminate three different cell lines from breast tissue: a non-malignant cell line (MCF10A), a non-metastatic adenocarcinoma cell line (MCF7) and a metastatic adenocarcinoma cell line (MDA). All the cells were grown onto glass slides. The spectra were discriminated by means of a principal component analysis, according to the PC1 component, whose values have the opposite sign in the pairwise score plots. This result supports the wide studies that are being carried out to promote the translation of the FTIR technique in medical practice, as a complementary diagnostic tool.

Analysis of Saliva Lipids in Breast and Prostate Cancer by IR Spectroscopy

We have developed a method for studying the lipid profile of saliva, combining preliminary extraction and IR spectroscopic detection. The case-control study involved patients with a histologically verified diagnosis of breast and prostate cancer and healthy volunteers. The comparison group included patients with non-malignant pathologies of the breast (fibroadenomas) and prostate gland (prostatic intraepithelial neoplasia). Saliva was used as a material for biochemical studies. It has been shown that the lipid profile of saliva depends on gender, and for males it also depends on the age group. In cancer pathologies, the lipid profile changes significantly and also depends on gender and age characteristics. The ratio of 1458/1396 cm-1 for both breast and prostate cancer has a potential diagnostic value. In both cases, this ratio decreases compared to healthy controls. For prostate cancer, the ratio of 2923/2957 cm-1 is also potentially informative, which grows against the background of prostate pathologies. It is noted that, in all cases, changes in the proposed ratios are more pronounced in the early stages of diseases, which increases the relevance of their study in biomedical applications.

A Comparison between FTIR Spectra from HUKE and SH-SY5Y Cell Lines Grown on Different Substrates

In recent years, Fourier Transform Infrared (FTIR) micro-spectroscopy has shown promising potential in medical diagnostics at the cellular level. In fact, FTIR spectra can provide information related to DNA, protein, and lipid content and how such a content changes when a pathological state arises. Most of these information is included in the so-called fingerprint region (1000–1800 cm−1), consisting of several spectral peaks related to vibrational modes occurring inside cellular components. Unfortunately, the slides commonly used in cytology (as the glass microscopy slides and coverslips) are opaque to IR radiation in the fingerprint region, whereas they are transparent for wavenumber values larger than 2000 cm−1, where few and broad spectral absorption bands, mainly due to lipids and proteins, are present. Nonetheless, here we show that FTIR spectra performed in the high wavenumber range 2750–3000 cm−1 can be used to discriminate two different types of cells, one from a normal cell line (Human Keratinocyte, HUKE) and the other from a cancer one (SH-SY5Y). The spectra are discriminated by means of their Principal Component Analysis, according to the PC1 component, and by means of ratiometric analysis, according to the ratio of the intensity of the peak at 2956 cm−1 and that of the peak at 2924 cm−1. The PC1 score values of the HUKE are statistically different from the PC1 score values of SH-SY5Y, whereas the intensity ratio results larger for SH-SY5Y than for HUKE cells. Such results occur for different substrates over which the cells have been grown, including the thick glass slides used for cytological analysis. This result is a further step toward the application of FTIR microspectroscopy in the cytological routine diagnosis.

Remodeling Chromatin Induces Z-DNA Conformation Detected through Fourier Transform Infrared Spectroscopy

The SWI/SNF complex is a highly conserved chromatin remodeling complex and can hydrolyze ATP by its catalytic subunit BRG1 or BRM to reconstruct the chromatin. To investigate whether this ATP-dependent chromatin remodeling could affect the DNA conformation, we therefore regulated (knocked down or overexpressed) BRG1/BRM in the cells and applied Fourier transform infrared (FTIR) spectroscopy to probe DNA conformational changes. As a result, we found that BRG1/BRM was indeed associated with the DNA conformational changes, in which knockdown of BRG1/BRM reduced Z-DNA conformation, while overexpression of BRG1/BRM enhanced Z-DNA conformation. This Z-DNA conformational transformation was also verified using the Z-DNA-binding proteins. Therefore, this work has provided a direct analytical tool to probe Z-DNA transformation upon ATP-dependent chromatin remodeling.

Detection of macromolecular inversion-induced structural changes in osteosarcoma cells by FTIR microspectroscopy

Fourier transform infrared (FTIR) microspectroscopy provides a biochemical fingerprint of the cells. In this study, chemical changes in 143B osteosarcoma cells were investigated using FTIR analysis of cancer cells after their treatment with polymeric invertible micellar assemblies (IMAs) and curcumin-loaded IMAs and compared with untreated osteosarcoma cells. A comprehensive principal component analysis (PCA) was applied to analyze the FTIR results and confirm noticeable changes in cell surface chemical structures in the fingerprint regions of 1480-900 cm^-1. The performed clustering shows visible differences for all investigated groups of cancer cells. It is demonstrated that a combination of FTIR microspectroscopy with PCA can be an efficient approach in determining interactions of osteosarcoma cells and drug-loaded polymer micellar assemblies. Graphical abstract.

Differentiating Between Malignant Mesothelioma and Other Pleural Lesions Using Fourier Transform Infrared Spectroscopy

Histopathology, despite being the gold standard as a diagnostic tool, does not always provide a correct diagnosis for different pleural lesions. Although great progress was made in this field, the problem to differentiate between reactive and malignant pleural lesions still stimulates the search for additional diagnostic tools. Our research using vibrational spectroscopy and principal component analysis (PCA) statistical modeling represents a potentially useful tool to approach the problem. The objective method this paper explores is based on the correlation between different types of pleural lesions and their vibrational spectra. Obtained tissue spectra recorded by infrared spectroscopy allowed us to categorize spectra in different groups using a created PCA statistical model. The PCA model was built using tissues of known pathology as the model group. The validation samples were then used to confirm the functionality of our PCA model. Student's t-test was also used for comparing samples in paired groups. The PCA model was able to clearly differentiate the spectra of mesothelioma, metastasis and reactive changes (inflammation), and place them in discrete groups. Thus, we showed that Fourier transform infrared spectroscopy combined with PCA can differentiate pleural lesions with high sensitivity and specificity. This new approach could contribute in objectively differentiating specific pleural lesions, thus helping pathologists to better diagnose difficult pleural samples but also could shed additional light into the biology of malignant pleural mesothelioma.

Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics

INTRODUCTION

Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These techniques can be used not only for the early diagnosis of cancer but also for monitoring the progression of the disease. Furthermore, chemical pathways to the progression of the disease process can be understood and followed.

AREAS COVERED

More recently, Artificial Intelligence (AI), Neural Network (NN), and Machine Learning are being combined with spectroscopy, which is making it easier to understand the chemical structural details of cancers and biological molecules more precisely and accurately. In this report, these aspects are being outlined by using breast cancer as a specific example.

EXPERT OPINION

A pathway showing to combine vibrational spectroscopy with AI and ML has immense potential in predicting various stages of different disease processes, in particular, in cancer diagnosis, staging, and designing treatment. This will result in improved patient care pathways.

A bio-responsive 6-mercaptopurine/doxorubicin based "Click Chemistry" polymeric prodrug for cancer therapy

A novel bio-responsive co-delivery system based on Poly(DEA)-b-Poly(ABMA-co-OEGMA) (PDPAO, prepared by reversible addition-fragmentation chain transfer (RAFT) polymerization) copolymers was constructed for enhanced cellular internalization and effective combination therapy. Reduction-sensitive 6-mercaptopurine (6MP) based prodrug and pH-sensitive doxorubicin (DOX) based prodrug were grafted onto PDPAO by an azide-alkyne "Click Chemistry" reaction to acquire a pH/reduction-sensitive polymeric prodrug (PDPAO@imine-DOX/cis-6MP), which was able to self-aggregate to form polymeric micelles (M(DOX/6MP)) with an average particle size of 116 ± 2 nm in the water. The resultant micelles could maintain a stable sphere structure and show stability with a small particles' dispersion index in the blood. Importantly, it has been observed that the pH-sensitive surface charge-conversion accompanied pH-triggered DOX release in the biomimetic extracellular acidic environment of tumor tissue and a rapid dual-drug release triggered by pH and GSH in the intracellular environment. The in vitro evaluation of micelles on human cervical cancer (HeLa) and human promyelocytic leukemia (HL-60) cells showed an enhanced cellular uptake because of charge-conversion and exhibited a higher cell-killing performance. Moreover, the graft ratio of DOX and 6MP showed the ability to adjust the cytotoxicity; the micelles with a graft ratio of 2: 1 (M(DOX₂/6MP)) displayed the higher cellular inhibition on either HeLa (combination index (CI) = 0.62) or HL-60 (CI = 0.35) cells. Overall, this novel dual-drug-conjugated delivery system might have important potential applications for combination therapy of cancer.

Ellagic acid ameliorates cisplatin induced hepatotoxicity in colon carcinogenesis

The clinical application of cisplatin (CP), one of the most extensively used antineoplastic drug, is restricted by its numerous side effects. CP's antitumor potential resides in the free generation of reactive oxygen species leading to oxidative stress. This stress is a source of the side effects associated with its use. Ellagic acid (EA), a polyphenol is known to possess multiple health benefits owing to its antioxidant properties. EA is largely metabolized by the colon microbiota of different mammals and therefore was a polyphenol of choice in the present study. The present study was thus carried out to explore the protective potential of EA on CP induced hepatotoxicity in colon tumor bearing mice. The administration of EA (10 mg/kg bwt po daily for 6 weeks) significantly ameliorated the toxicity caused by CP (5 mg/kg bwt ip once a week for 4 weeks). Activities of liver marker enzymes and lactate dehydrogenase were brought back to normal. EA cotreatment also led to a marked reduction in the extent of peroxidative damage to liver tissue as was evident from the improvement in the histopathological changes observed and FT-IR analysis. The present study, therefore, suggests that the administration of EA reduces the CP-induced hepatotoxicity, thereby emerging out as a potential candidate for chemopreventive action.

A novel experimental approach for liver analysis in rats exposed to Bisphenol A by means of LC-mass spectrometry and infrared spectroscopy

An innovative complementary approach using a liquid chromatographic-mass spectrometer method and infrared spectroscopy is proposed for measuring internal biological exposure to dangerous chemical contaminants and for monitoring biochemical changes in target organs. The proposed methodologies were validated and applied in the case of rats exposed to low-doses of Bisphenol A (BPA). A liquid chromatographic method coupled to a tandem mass spectrometer was used in order to measure BPA concentration in rat livers. BPA was detected at different levels in all liver samples from BPA-treated rats, although the exposure dose was the same in all treated animals, and also from control rats, highlighting the difficulties in eliminating external uncontrolled exposure and the need for internal biological monitoring. Fourier Transform Infrared analysis was applied to detect structural changes occurring in several molecules (lipids, proteins, carbohydrates and nucleic acids) as well as the presence of specific metabolic processes. The spectroscopic analyses clearly demonstrated a different lipid composition more than an evident lipid accumulation and a glycogen accumulation decrease, revealing a metabolic disturbance in livers with a normal histological aspect. These results demonstrated the potential of an integrated approach based on mass spectrometry and infrared spectroscopy to evaluate at an early stage the hepatotoxic effect of BPA exposure in an animal model. This approach can be usefully exploited in all the investigations aimed to provide better information concerning the interrelationships between contaminant exposure, dose, and health effects.

A Plasma Biochemical Analysis of Acute Lead Poisoning in a Rat Model by Chemometrics-Based Fourier Transform Infrared Spectroscopy: An Exploratory Study

In this work, we explored to use chemometrics-based Fourier transform infrared (FTIR) spectroscopy to investigate the plasma biochemical changes due to acute lead poisoning (ALP) in a rat model. We first collected the FTIR spectra of the plasma samples from the rats with and without suffering from ALP. We then performed the chemometric analysis of these FTIR spectra using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). We found that the chemometrics-based FTIR spectroscopy can discriminate the rats with and without ALP. Further analysis on the PLS-DA regression coefficient revealed that the spectral changes, in particular, corresponding to the biochemical changes of proteins in the plasma may be used as potential spectral biomarkers for the diagnostics of lead poisoning. Our work demonstrates the potential of chemometrics-based FTIR spectroscopy as a promising tool for the biochemical analysis of plasma that could consequently enable an objective, convenient and non-destructive diagnostics of lead poisoning. To the best of our knowledge, this work is the first application of chemometrics-based FTIR spectroscopy in the diagnostics of lead poisoning.

Tumor-targeting and pH-responsive nanoparticles from hyaluronic acid for the enhanced delivery of doxorubicin

In this study, intracellular pH-responsive nanoparticles (NPs) of hyaluronic acid-hydrazone linkage-doxorubicin (HA-hyd-DOX) were designed and prepared for acid-triggered release of doxorubicin through a hydrazone linkage. A series of amphiphilic polymeric prodrugs were obtained, which can be self-assembled in aqueous media, the formed NPs exhibited a spherical core-shell type and the uniform size was ranging from 167 to 220nm. Moreover, the HA-hyd-DOX NPs exhibited a good stability in vitro and the drug release profiles showed that the DOX release was obviously mediated by pH gradient. Additionally, the cell counting assay kit-8 (CCK-8) demonstrated that the drug delivery system in this study performed a lower cytotoxicity on normal cells (Mouse fibroblast cells, L929) and higher inhibition ratio on tumor cells (Human cervical cancer cells, HeLa) in response to drug release with the intracellular pH environment. Furthermore, confocal laser scanning microscopy (CLSM) images and flow cytometric profiles of HeLa cells showed an efficiently cellular uptake due to the receptor-mediated affinity of CD44 for HA with high specificity. These results suggest that this pH dependent drug delivery system based on HA will provide insights into the design of potential prodrugs for the cancer therapy.

Fourier transform infra-red spectroscopic signatures for lung cells' epithelial mesenchymal transition: A preliminary report

Infra red (IR) spectral characterization can provide label-free cellular metabolic signatures of normal and diseased circumstances in a rapid and non-invasive manner. Present study endeavoured to enlist Fourier transform infra red (FTIR) spectroscopic signatures for lung normal and cancer cells during chemically induced epithelial mesenchymal transition (EMT) for which global metabolic dimension is not well reported yet. Occurrence of EMT was validated with morphological and immunocytochemical confirmation. Pre-processed spectral data was analyzed using ANOVA and principal component analysis-linear discriminant analysis (PCA-LDA). Significant differences observed in peak area corresponding to biochemical fingerprint (900-1800cm^-1) and high wave-number (2800-3800cm^-1) regions contributed to adequate PCA-LDA segregation of cells undergoing EMT. The findings were validated by re-analysis of data using another in-house built binary classifier namely vector valued regularized kernel approximation (VVRKFA), in order to understand EMT progression. To improve the classification accuracy, forward feature selection (FFS) tool was employed in extracting potent spectral signatures by eliminating undesirable noise. Gradual increase in classification accuracy with EMT progression of both cell types indicated prominence of the biochemical alterations. Rapid changes in cellular metabolome noted in cancer cells within first 24h of EMT induction along with higher classification accuracy for cancer cell groups in comparison to normal cells might be attributed to inherent differences between them. Spectral features were suggestive of EMT triggered changes in nucleic acid, protein, lipid and bound water contents which can emerge as the useful markers to capture EMT related cellular characteristics.