Methods

Disease trend analysis platform accurately predicts the occurrence of cervical cancer under mixed diseases

Cervical cancer (CC) is one of the most common gynecological malignancies. Cytological screening, while being the most common and accurate method for detecting cervical cancer, is both time-consuming and costly. Predicting CC based on bioinformatics can assist in the rapid early screening of CC in clinical practice. Most recent CC prediction methods require a large amount of detection data or sequencing data and are not ideal for CC detection in complex disease samples. We developed the Disease trend analysis platform (Dtap), which can quickly predict the occurrence of diseases using only blood routine data. Blood routine data was collected from 1,292 cervical cancer patients, 4,860 patients with complex diseases, and 4,980 healthy individuals from various sources. The results show that the Dtap-based trend model maintained good and stable performance in the prediction task of multiple datasets as well as complex disease samples. Finally, we built DTAPCC (http://bioinfor.imu.edu.cn/dtapcc), a Dtap-based CC disease prediction platform, to help users quickly predict CC and visualize trend features.

Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer

Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-scale multi-omics data such as gene expression, DNA methylation, miRNA expression, and copy number variations can provide insights on personalized survival. However, how to effectively integrate multi-omics data remains a challenging task. In this paper, we develop multi-omics integration methods to improve the prediction of overall survival for breast cancer and ovarian cancer patients. Because multi-omics data for the same patient jointly impact the survival of cancer patients, features from different -omics modality are related and can be modeled by either association or causal relationship (e.g., pathways). By extracting these relationships among modalities, we can get rid of the irrelevant information from high-throughput multi-omics data. However, it is infeasible to use the Brute Force method to capture all possible multi-omics interactions. Thus, we use deep neural networks with novel divergence-based consensus regularization to capture multi-omics interactions implicitly by extracting modality-invariant representations. In comparing the concatenation-based integration networks with our new divergence-based consensus networks, the breast cancer overall survival C-index is improved from 0.655±0.062 to 0.671±0.046 when combing DNA methylation and miRNA expression, and from 0.627±0.062 to 0.667±0.073 when combing miRNA expression and copy number variations. In summary, our novel deep consensus neural network has successfully improved the prediction of overall survival for breast cancer and ovarian cancer patients by implicitly learning the multi-omics interactions.

Deep learning-based multimodal image analysis for cervical cancer detection

Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PET/CT, have established roles with superior sensitivity and specificity in most cancer imaging applications. However, a typical FDG-PET/CT analysis involves the time-consuming process of interpreting hundreds of images, and the intense image screening work has greatly hindered clinicians. We propose a computer-aided deep learning-based framework to detect cervical cancer using multimodal medical images to increase the efficiency of clinical diagnosis. This framework has three components: image registration, multimodal image fusion, and lesion object detection. Compared to traditional approaches, our adaptive image fusion method fuses multimodal medical images. We discuss the performance of deep learning in each modality, and we conduct extensive experiments to compare the performance of different image fusion methods with some state-of-the-art (SOTA) object-detection deep learning-based methods in images with different modalities. Compared with PET, which has the highest recognition accuracy in single-modality images, the recognition accuracy of our proposed method on multiple object detection models is improved by an average of 6.06%. And compared with the best results of other multimodal fusion methods, our results have an average improvement of 8.9%.

Carboxyl-PEG modified Fe3O4 nanoparticles as an ultrasensitive SERS substrate for multiplex detection of exogenous hormones related to endometrial cancer

Traditional surface-enhanced Raman scattering (SERS) technology often struggles to achieve high-precision discrimination in the simultaneous detection of multiple exogenous hormones due to complex spectral overlap and matrix interference, limiting its application in trace analyte analysis within complex matrices (e.g., biological samples). This study developed a SERS substrate based on carboxyl-terminal polyethylene glycol (PEG)-modified iron oxide (Fe