Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Background: Raman and �uorescence spectra techniques are potential tools for disease diagnosis. In recent years, the application of Raman and �uorescence spectra techniques in biological studies has increased a great deal, and clinical investigations relevant to cancer detection by spectroscopic means have attracted particularly attention from both clinical and non-clinical researchers. Methods: In this article, Raman and �uorescence spectra were employed for the detection of liver cancer and healthy individuals using their serum samples. These serum samples were compared with their spectral features acquired by Raman and �uorescence spectroscopy to initially establish spectral features that can be considered spectral markers of liver cancer diagnosis. Resuits: The intensity differences from characteristic peaks of carotene, protein and lipid associated Raman spectra were clearly observed in liver cancer patient serum samples versus normal human serum. The changes in the serum �uorescence pro�les of liver cancer patients were also analyzed. To probe the capacity and contrast of Raman spectroscopy as an analytical implement for the early diagnosis of liver cancer, principal component analysis (PCA) was used to analyze the Raman spectra of controls , liver cancer patients and healthy individuals. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of Raman spectroscopy for the classi�cation of disease samples and healthy samples. Conclusion: Compare with the existing diagnostic techniques, the Raman spectroscopy technique has an excellent advantage in extremely low sample requirements, ease of use and ideal screening procedures. Thus, Raman spectroscopy has great potential to be developed as a powerful tool for distinguishing between healthy and liver cancer serum samples.


Introduction
Cancer is a major public health problem worldwide [1,2] .The latest data from the International Agency for Research on Cancer (IARC)'s World Cancer Report 2020 show that in 2020, there were 19.29 million new cancer cases worldwide, including 10.06 million males and 9.23 million females, and 905,677 new cases of liver cancer accounted for 4.7% of the new cancer, ranking fth.There were 9.96 million cancer deaths worldwide in 2020, including 5.53 million males and 4.43 million females; of these, 830,180 liver cancer deaths, representing 8.3% of cancer deaths, were ranked third [2][3][4][5] .At present, the total treatment condition of liver cancer is still a low radical cure rate, high recurrence rate and poor prognosis.The main reason for the unsatisfactory e cacy of liver cancer is that the diagnosis is late.Approximately 70%~80% of patients with liver cancer have reached the late stage and effective radical treatment cannot be performed.As a result, the early diagnosis of liver cancer is extremely important [4][5][6][7][8] .At present, the monitoring and screening of high-risk groups are the main methods of early diagnosis of liver cancer.Due to the genetic susceptibility of liver cancer, the great differences in morphological diversity and microenvironment and other factors and the rapid development of disease cause the di cult early diagnosis of liver cancer.For patients with liver cancer, most of them are diagnosed late and unable to cure [5][6][7] .Using various tests to improve the detection rate has great signi cance in improving the treatment effect of liver cancer, prolonging patient life and ensuring patient quality of life [6][7][8][9] .
Currently, the traditional methods for diagnosing liver cancer include ultrasound imaging (US), computed tomography (CT), magnetic resonance imaging (MRI), and detection of serum alpha-fetoprotein (AFP) levels.However, the use of imaging to examine liver cancer is highly dependent on the experience of the operator, and has limited ability to distinguish liver cancer cells.It is a commonly used detection method to diagnose liver cancer by detecting serum AFP content, but the sensitivity of AFP content is very low and cannot be the most effective means for early diagnosis [6][7][8][9] .Therefore, it is particularly important to design an economical and simple test method that can quickly and accurately detect and distinguish between early liver cancer patients and normal people.Raman scattering detects the vibrational frequency of molecular chemical bonds, and this intrinsic property makes Raman scattering have ultrahigh chemical resolution ability.It is also important in this way, that is, it does not require the addition of external labels to distinguish different components and is a non-labeling technique [10][11][12] .In the medical eld, the occurrence of diseases often starts from subtle variations inside the molecules, which are di cult to detect by routine clinical means, such as changes in the structure of proteins, fats, sugars and nucleic acids [13][14][15] .However, subtle changes in biological internal molecules can be well detected by Raman spectroscopy, thus providing great guidance and help for the early diagnosis of diseases.
In this paper, the Raman spectra of normal human serum (75 cases) and liver cancer serum (69 cases) were collected.The differences between normal human serum spectra and liver cancer serum spectra were analyzed, and the molecular structure changes of the main components were discussed.The effect of the uorescence spectrum on Raman spectra was analyzed.The Raman spectra were identi ed using PCA and PLS-DA, to facilitate the application of Raman spectrum for clinical tumor diagnosis.

Raman measurement
Raman spectra were determined by a laser microscopic confocal Raman spectrometer (ANDOR SR-500, UK).The focal length is 500mm, and 1200l/mm grating (Blaze 500) was used in the experiment, and the spectral resolution is 1cm −1 .Laser is a 532nm green solid-state laser (Cobolt Samba 532 nm, Cobolt AB Solna, Sweden).A thermoelectric cooled charge-coupled device (CCD) camera is equipped with a back illuminated, deep depletion CDD chip (Andor iDus 416, DU416A LDC-DD, Andor Technology Ltd., Belfast, UK) to collect the sample surface and scattered signals, and cooled the camera to -70°C to reduce noise.Microscope (Leica DM 2700m, Leica microsystems Wetzlar GmbH), 50x (NA=0.5)objective.The edge lter is used to lter stray light.Spectral data were collected using Andor Solis software (Andor Technology).

Samples preparation
Serum samples were provided by the Department of Thoracic Surgery, the First People's Hospital of Yunnan Province.All participants were informed and signed their consent form for this study.Ethical approval was approved by Biomedical Research Ethics Committee of Yunnan Normal University (No.ynnuethic 2021-14).Serum samples from 69 liver cancer patients and 75 healthy subjects were collected.Sample information is listed in Table 1.Three milliliters of venous blood was drawn from each participant before breakfast and centrifuged at 3000 r/min for 20 min.Then 1.5 mL of upper serum was taken and sealed in an Eppendorf tube and placed in a refrigerator (temperature 4°C) for use.For Raman spectroscopy tests, we used a pipette gun to suck 30 µ l of sample and drop it on a clean glass slide (soak it in aqua regia for 1 h, then wash it with a large amount of ultrapure water, soak it in acetone solution for 1 h, clean it with a large amount of ultrapure water and then blow dry it), and then dry it in the M3 ultraclean chamber.

Raman spectral data acquisition and pretreatment
The ANDOR SR-500-type Raman spectrometer laser light path was adjust, a 532 nm excitation wavelength laser was used, and the entire experimental process was performed in the M3 ultraclean chamber laboratory.The spectra were collected by scanning for 15 s and superimposing three times, with a spectral measurement range of 800-1800cm −1 , and the spectra in this range covered most of the characteristic Raman peaks of the analytes studied, with a slit width set at 100 microns, to a laser power of approximately 5 mW on the sample.In the acquisition of Raman spectra containing uorescent substances, uorescence is an important interference factor, and Raman scattering of serum also has a certain degree of uorescence interference.Interference caused by uorescence exists in the acquisition of Raman spectra of serum, so later, we performed uorescence spectroscopic analysis.To eliminate the spiking effects introduced by cosmic radiation, a running median lter was applied.The entire Raman study owchart is shown in Figure 1.

Data analysis
Principal component analysis(PCA) is a widely used multivariate analysis technique that can discriminate Raman spectra originating from biological systems [7,16−22] .All two groups of spectral were simultaneously analyzed using PCA, in order to reduce the spectral dataset to a smaller number of variables (principal components (PCs)) that describe the majority of the variance in the spectral dataset [16] .Partial least squares discriminant analysis (PLS-DA) is a supervised classi cation model that is performed on the spectral data of liver cancer and healthy individual samples as X-variables (predictors) and their class information as Y-variables [22][23][24][25][26] .The second derivative spectrum can improve spectral resolution by amplifying small differences [6] .Second derivative Raman spectra were obtained by the Savitzky-Golay algorithm in OMNIC 8.2 software (Thermo Scienti c).PCA and PLS-DA analysis of second derivative Raman spectra were performed using Unscrambler X 10.4 software (Camo Software AS, Oslo, Norway).

Raman spectra of serum samples
In Fig. 2 shows the Raman spectra of serum samples from 75 healthy individuals.It can be seen from the spectra that the peak positions of the Raman spectra and the Raman spectrum are the same, and the intensity of each Raman peak changes slightly.Considering that the experimental conditions cannot be exactly the same during the test, for example, the laser power of the sample uctuates slightly due to inconsistent focusing each time, which affects the intensity of the detection signal and moves the spectrum line up and down.
Figure 3 shows the Raman spectra of serum samples from 69 patients with liver cancer.The spectra showed that the Raman spectra of serum samples from patients with liver cancer had the same peak position.The Raman peak intensity of serum from patients with liver cancer was signi cantly different from the characteristic peak intensity of serum raman spectra from normal subjects.In order to compare the serum Raman spectra of patients with liver cancer and normal subjects, we averaged the spectra shown in Fig. 4(a).
It can be seen from Fig. 4 and porphyrin content variation [28] .Two high intensity Raman peaks at 1156 and 1519 cm −1 due to the resonance raman effect belonging to β-carotene are strongly enhanced under excitation at 532 nm [28,29] .
The decrease in β-carotene in the diseased serum samples is consistent with previous research [29] .In addition, some weak difference Raman peaks appear at 962, 1127, 1297, 1335, 1447, 1584, and 1653cm −1 can also be found.The weak differential peaks at 962 cm −1 belong to ribose C-O stretching of ribose [22] , 1127 cm −1 (C-N stretching Protein), 1297 cm −1 (CH 2 deformation Fatty acids), 1335 cm −1 (CH 3 CH 2 wagging, collagen (protein assignment), nucleic acid), 1447 cm −1 (CH 2 CH 3 bending mode, CH 2 deformation of proteins & lipids), 1584 cm −1 (C=C bending mode of phenylalanine) [10] , 1653 cm −1 (Carbonyl stretch (C=O), C=C stretch Protein amide I absorption) [39,40] .Patients with malignant tumors are mostly in a high metabolic state, protein synthesis and catabolism in the body are increased, and the metabolites produced and various material components in the blood are also changed.Amino acids are involved in protein synthesis and catabolite, whose composition and concentration can re ect the metabolic state.Hyperproliferation of tumor cells causes changes in protein, amino acids and other components in body uid.Rapid growth and unlimited proliferation of cancer cells require a large amount of nutritional substrates, especially amino acids, to be consumed, which will inevitably lead to changes in the amino acid metabolic database of cancer tissue.
Table 2 The spectral peaks and their assignments.

Fluorescence Spectra analysis
Endogenous uorescent substances are present in the serum, such as proteins, porphyrins, carotenoids, and ribo avin, which can produce uorescence after excitation by a certain wavelength of light [41][42][43][44][45] .From Fig. 4, we can nd that the Raman spectral uorescence background of liver cancer patients is relatively strong, so we performed uorescence spectroscopic analysis of the serum of healthy individuals and liver cancer patients.During the experiment, 50 microliters of serum samples were added to 2 ml of saline, diluted and poured into quartz uorescent colorimetric dishes, put in a uorescent spectrophotometer (Edinburgh Instruments, FS5 type, UK, with a 150 W xenon lamp as the excitation source, scanning speed of 60nm/min) to obtain physiological saline (background spectroscopy), liver cancer and healthy individual serum uorescence spectra.The results are shown in Fig. 5. From Fig. 5A, we can see that 462 nm belongs to the uorescence characteristic peak of physiological saline, with porphyrin luminescence mainly present in the 600-700 nm spectral region [42] .In the spectral region where the largest difference in peak intensity between healthy individuals and liver cancer patients, the molecule playing the main luminescence role is protein [42][43][44][45][46] .Proteins are formed by a peptide chain composed of multiple amino acids repeatedly folding in space, where the amino acids capable of uorescing are tryptophan, tyrosine, and phenylalanine [43][44] .The growth and division of cancer cells will not be regulated by genes, and their uptake of amino acids is too fast, which disturbs amino acid metabolism in cancer patients, and eventually leads to changes in the content of amino acids in serum [41,45] .Compared with healthy individuals, liver cancer patients have a reduced ability to degrade aromatic amino acids, and the contents of tryptophan, tyrosine and phenylalanine in serum are signi cantly increased, with increased concentrations of these three amino acids, leading to enhanced hydrogen bonding energy between light emitting molecules [46][47][48] .Fluorescence spectra from healthy individual serum and liver cancer patients were used for baseline calibration, and multipeak Gaussian tting was performed on liver cancer serum (Fig. 5B).We found that the tted three peaks in the serum of liver cancer patients, 490 nm, 513 nm and 544 nm compared to the three peaks of healthy individuals, 490 nm, 512nm and 580 nm were signi cantly different in peak position and peak strength.In particular, the peak of serum 544 nm in liver cancer patients was blue-shifted by approximately 36 nm compared with that of healthy individuals 580 nm.This may be due to impaired tissue and organ function in patients with malignant tumors, disrupting amino acid metabolism [45][46][47] .The content of luminescent amino acids in the free state is increased, and the concentration of amino acids that can emit uorescence increases, resulting in enhanced hydrogen bond energy and elongation of the two interatomic chemical bonds that form hydrogen bonds [45][46][47] .

PCA analysis
PCA analysis was performed on the second derivative Raman spectra in range of 1100-1200 cm −1 (Fig. 6).Fig. 6a shows that the serum of patients with liver cancer was well separated from the serum samples of healthy individuals.The rst three PCs explained 91% of the total variance, with 53% for PC1, 29% for PC2, and 9% for PC3.The loading plot of PCA is used to identify the peaks that make a high contribution to the differentiated samples.As show in Fig. 6b, PC1 and PC2 mainly contributed greatly near 1127cm −1 and 1156 cm −1 , which are related to proteins [28] and carotenoids [34][35][36] .

PLS-DA results
Performed of PLS-DA analysis were make of calibration set (patients with liver cancer provide 52 serum samples and healthy individuals provide 56 serum samples) and validation set (which patients with liver cancer provide 17 serum samples and healthy individuals gave 19 serum samples) according to the ratio of 3:1 for model work in range of 1100-1200 cm −1 (Fig. 7).From Fig. 7a, we can see that the serum samples are distributed into two clusters.The red cluster is mainly composed of serum samples from patients with liver cancer, and the blue cluster is mainly composed of the serum samples that healthy individuals gave.Fig. 7b shows the loading plot of Factor-1 and Factor-2 for identifying the peaks with high weights in classifying samples.There are positively weighted peaks at approximately 1158 cm −1 and passively weighted peaks at approximately 1154 cm −1 .The peak of this region belongs to the Raman peaks of proteins and carotenoids, thus showing that protein and carotenoid changes during liver cancer carcinogenesis dominate in this classi cation model.
Figure 8 shows the prediction results of PLS-DA in the range of 1100-1200 cm −1 .Where predicted Y values greater than zero were considered liver cancer, and less than one was considered healthy.The results showed that the predicted Y values of 17 serum samples from patients with liver cancer and 19 serum samples from healthy individuals were consistent with the actual situation.The effect is very good, and the classi cation accuracy is 100%

4.
Raman and uorescence spectroscopy were used to classify liver cancer and healthy individual serum samples.The difference spectrum clearly shows the changes in the various major components of the serum in the body during liver carcinogenesis.According to the uorescence spectroscopy and Raman date analysis of PCA, the main factor causing the serum Raman spectra difference between liver cancer patients and healthy people is the changes of carotenoid and protein in the serum.In particular, uorescence spectroscopic analysis found that the amino acid content that shines freely under the in uence of malignant tumors increases (including tryptophan, tyrosine, and phenylalanine), which can provide a reference for clinical treatment.Using Raman spectroscopic data, a PLS-DA model was established to accurately classify serum samples from healthy individuals and liver cancer patients.Based on Raman spectroscopy, we currently well distinguish a limited sample of liver patients and healthy individuals.Although further studies are needed to clearly explain the characteristic Raman spectra of various serum biomolecules, the results obtained are very promising to use Raman spectroscopy for clinical diagnosis.Signi cantly, compare with the existing diagnostic techniques, the Raman spectroscopy technique has an excellent advantage in extremely low sample requirements, ease of use and ideal screening procedures.It can provide a clear and objective result at the molecular level and help reduc the human errors on the objective result to the maximum.Thus, Raman spectroscopy has great potential to be developed as a powerful tool for distinguishing between healthy and liver cancer serum samples.

Declarations Figures
(a) that the Raman characteristic peaks of liver cancer serum and normal serum mainly occurred in the range of 600-1653cm −1 .The main Raman peaks are caused by serum proteins, amino acids, lipids, sugars, carbohydrates and other substances, which occurred in the ranges of 745 cm −1 , 1003cm −1 , 1127cm −1 , 1156cm −1 , 1301cm −1 , 1337cm −1 , 1447cm −1 , 1519cm −1 and 1653cm −1 .The peak assignments corresponding to their Raman spectra are shown in Table2.To nd the difference between the serum Raman spectra of liver cancer patients and healthy individuals, differential spectra were found by subtraction as shown in Figure4(b).In liver cancer patients all component contents were signi cantly reduced compared with those in healthy individuals.The three peaks with the largest difference are 1003, 1156, and 1519 cm −1 , which are due to phenylalanine, protein, carotene, carotenoids

2 twisting CH 2 ,
CH 3 bending mode,CH 2 deformation C=C stretch mode C-C & conjugated C=C band stretch Carbonyl stretch (C=O), C=C stretch C=C stretching vibrations,

Flow
Figure 2

Figure 7 See
Figure 7

Table 1
The information of patients with liver cancer and healthy individuals.