Predicting Early Cancer With Molecular Vibration in Serum

Treatment of Cancer

Author: Chinese Academy of Sciences
Published: 2023/07/29
Publication Type: Research Paper - Peer-Reviewed: Yes
Contents: Summary - Introduction - Main - Related

Synopsis: A team of scientists have taken a significant leap forward by developing a label-free SERS-Artificial intelligence method for cancer screening (SERS-AICS). This technology ingeniously merges the detection strengths of traditional SERS system with the analytical power of advanced big data tool. The study demonstrates the potential for developing a sensitive tool for the early detection of various cancers. The predictive technique can identify individuals potentially harboring cancer from their blood sample obtained in routine healthy exam. Anyone with suspicious findings would then be referred further evaluation by definitive diagnosis.

Introduction

Cancer, a leading cause of death worldwide is typically diagnosed at an advanced stage when survival rates are low. Most early-stage cancers are asymptomatic, and traditional methods such as imaging or histopathological testing are not feasible as routine screening tests for the general population due to high cost and other clinical constraints.

Main Digest

While several surface-enhanced Raman scattering (SERS)-based cancer detection methods have been developed to boast high sensitivity and selectivity, they tend to focus on a single or just a few biomarkers, and often only for a narrow range of cancer types, hampered by an insufficient sample size. Moreover, many researches remain at the preliminary stages lacking data that us easy to interpret and failing to leverage more efficient high-throughput analysis methods.

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Fig 1. SERS-AICS characterization of five cancers with high mortality. ROC curves with covariance matrices-assisted SVM model for distinguishing (A) 244 lung cancer patients, (B) 216 colorectal carcinoma patients, (C) 195 gastric cancer patients, (D) 203 hepatocellular carcinoma patients, (E) 193 esophageal carcinoma patients, (F) 400 mixture cancer patients from 324 healthy controls in the internal cohort. The (G) accuracy, (H) sensitivity and (I) specificity of single or multiple cancers/healthy control, the overall accuracy, sensitivity and specificity of all cancers could reach at 95.81%, 95.87%, 95.40%. The 400 mixed cancer patients were obtained by randomly selecting 80 samples from the five types of cancer each - Image Credit: Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu and Xiangheng Xiao.
Fig 1. SERS-AICS characterization of five cancers with high mortality. ROC curves with covariance matrices-assisted SVM model for distinguishing (A) 244 lung cancer patients, (B) 216 colorectal carcinoma patients, (C) 195 gastric cancer patients, (D) 203 hepatocellular carcinoma patients, (E) 193 esophageal carcinoma patients, (F) 400 mixture cancer patients from 324 healthy controls in the internal cohort. The (G) accuracy, (H) sensitivity and (I) specificity of single or multiple cancers/healthy control, the overall accuracy, sensitivity and specificity of all cancers could reach at 95.81%, 95.87%, 95.40%. The 400 mixed cancer patients were obtained by randomly selecting 80 samples from the five types of cancer each - Image Credit: Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu and Xiangheng Xiao.
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In a new paper published in eLight, a team of scientists, led by Professor Xiangheng Xiao from College of Physical Sciences, Wuhan University, have taken a significant leap forward by developing a label-free SERS-Artificial intelligence method for cancer screening (SERS-AICS). This technology ingeniously merges the detection strengths of traditional SERS system with the analytical power of advanced big data tool.

The team tested as little as 15ul of patient serum samples with Ag nanowires each for lung, colorectal, hepatic, gastric, and esophageal cancers, capturing the subtle changes in vibrational signals of molecules in cancer samples due to their altered physiology and pathology.

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Fig 2. Early screening for four representative cancers by SERS-AICS. (A) The SERS-AICS method could also effectively distinguish common diseases from early cancers in stage I and II with high accuracy. (B) ROC curves with covariance matrices-assisted SVM model for distinguishing 45 common disease patients from 33 early stage of cancer patients about lung. (C) ROC curves with covariance matrices-assisted SVM model for distinguishing 42 common disease patients from 32 early stage of cancer patients about colorectum. (D) ROC curves with covariance matrices-assisted SVM model for distinguishing 39 common disease patients from 36 early stage of cancer patients about gastric. (E) ROC curves with covariance matrices-assisted SVM model for distinguishing 33 common disease patients from 32 early stage of cancer patients about liver. (F) The accuracy, sensitivity and specificity of different common disease/early stage of cancer - Image Credit: Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu and Xiangheng Xiao.
Fig 2. Early screening for four representative cancers by SERS-AICS. (A) The SERS-AICS method could also effectively distinguish common diseases from early cancers in stage I and II with high accuracy. (B) ROC curves with covariance matrices-assisted SVM model for distinguishing 45 common disease patients from 33 early stage of cancer patients about lung. (C) ROC curves with covariance matrices-assisted SVM model for distinguishing 42 common disease patients from 32 early stage of cancer patients about colorectum. (D) ROC curves with covariance matrices-assisted SVM model for distinguishing 39 common disease patients from 36 early stage of cancer patients about gastric. (E) ROC curves with covariance matrices-assisted SVM model for distinguishing 33 common disease patients from 32 early stage of cancer patients about liver. (F) The accuracy, sensitivity and specificity of different common disease/early stage of cancer - Image Credit: Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu and Xiangheng Xiao.
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The researchers then trialed and validated their predictive workflow to recognize cancer by analyzing molecular vibrational spectrum from two independent cohorts involving 382 healthy individuals and 1,582 cancer patients. The system demonstrated impressive efficacy with an accuracy of 95.81%, a sensitivity of 95.40% and a specificity of 95.87% overall for five cancer types. Additionally, it was successful in distinguishing samples at an early stage of cancer from those with common diseases, while facilitating the creation of a data platform for more in-depth analysis.

"This was very promising, as early-stage screening should detect changes in molecular fingerprinting information that are intermediate between healthy and disease states," said Prof. Xiao. "And what's truly exciting is that it isn't restricted to one or a just handful biomarkers, but expand to encompass an all-inclusive 'panoramic' view for every single alternative signals in cancers."

"Our study demonstrates the potential for developing a sensitive tool for the early detection of various cancers," Xiao said. "The predictive technique can identify individuals potentially harboring cancer from their blood sample obtained in routine healthy exam. Anyone with suspicious findings would then be referred further evaluation by definitive diagnosis."

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Fig 3. Construction of cancer-related database at bond level by SERS-AICS. (A) The original spectral data of 1465 dimensions of serum with no obvious specificity can obtain 50 valid dimensions with the best specificity after the correlation between dimensions is judged by the covariance matrix, which related to the molecular bond energy information in serum associated with cancer or disease. (B) Heatmap of the covariance matrix formed on 30 true dimensions between 600-623.77051 cm-1 using peak data for lung cancer and healthy controls, the dimension at 600 cm-1 and 618.03276 cm-1 showed the minimal correlation. (C) List of common valid dimensions of different cancers compared with normal control group or common disease - Image Credit: Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu and Xiangheng Xiao.
Fig 3. Construction of cancer-related database at bond level by SERS-AICS. (A) The original spectral data of 1465 dimensions of serum with no obvious specificity can obtain 50 valid dimensions with the best specificity after the correlation between dimensions is judged by the covariance matrix, which related to the molecular bond energy information in serum associated with cancer or disease. (B) Heatmap of the covariance matrix formed on 30 true dimensions between 600-623.77051 cm-1 using peak data for lung cancer and healthy controls, the dimension at 600 cm-1 and 618.03276 cm-1 showed the minimal correlation. (C) List of common valid dimensions of different cancers compared with normal control group or common disease - Image Credit: Shilian Dong, Dong He, Qian Zhang, Chaoning Huang, Zhiheng Hu, Chenyang Zhang, Lei Nie, Kun Wang, Wei Luo, Jing Yu, Bin Tian, Wei Wu, Xu Chen, Fubing Wang, Jing Hu and Xiangheng Xiao.
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In future work, the researchers plan to analyze the spectrum of molecular vibration associated with various clinical characteristics of caner to gain a comprehensive understanding of the disease, potentially aiding in selecting targeted therapies. They also aim to broaden the application of the SERS-AICS method to detect a wider range of cancers and other diseases, promising a transformative step forward in early-state cancer detection and patient monitoring.

Funding Information

This research received funding from the National Natural Science Foundation of China, the Science Fund for Creative Research Groups of the Natural Science Foundation of Hubei Province, the Experimental Technology project of Wuhan University, the Sichuan Science and Technology Program, the Fundamental Research Funds for the Central Universities and medical Sci-Tech innovation platform of Zhongnan Hospital.

Attribution/Source(s):

This peer reviewed publication was selected for publishing by the editors of Disabled World due to its significant relevance to the disability community. Originally authored by Chinese Academy of Sciences, and published on 2023/07/29, the content may have been edited for style, clarity, or brevity. For further details or clarifications, Chinese Academy of Sciences can be contacted at english.cas.cn. NOTE: Disabled World does not provide any warranties or endorsements related to this article.

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