A new machine learning method for cancer mutation analysis

STARD7 could be an immunological and prognostic biomarker: from pan-cancer analysis to hepatocellular carcinoma validation

BACKGROUND

As the emergence of technologies such as sequencing and gene mapping, significant advancements have been made in understanding the landscape of tumors. However, the effective treatment of tumors continues to pose a tremendous challenge in clinical practice, which highlights the importance of predicting tumor markers and studying drug resistance mechanisms. The prognosis and differential expression of STARD7 in human pan-cancer were investigated by bioinformatic methods and experimental verification.

METHODS

The expression, diagnostic, and prognostic significance of the STARD7 gene were comprehensive analyzed using bioinformatics techniques. Furthermore, we validated our projected outcomes in liver cancer through experimental methodologies, including the use of qRT-PCR, CCK8 and transwell assays.

RESULTS

The STARD7 gene exhibits differential expression in 25 tumors, with high expression observed in 22 tumors. These distinct expression patterns within different tumor types are closely associated with poor prognosis and diagnosis. Furthermore, the STARD7 gene plays a role in regulating the tumor immune microenvironment. Methylation levels of STARD7 vary among 20 types of tumors and are correlated with survival outcomes. Furthermore, the experiment results demonstrated that STARD7 is highly expressed in hepatocellular carcinoma cells. Suppression of STARD7 significantly impedes the proliferation, migration, and invasion of HepG-2 and SMMC-7721 cells.

CONCLUSIONS

STARD7 has the potential to function as a crucial prognostic biomarker and exhibit correlation with tumor immunity in various types of human cancers. The implications of our findings extend to informing cancer immune-therapy and promoting the advancement of precision immune-oncology.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

Uncovering driver genes in breast cancer through an innovative machine learning mutational analysis method

Breast cancer has become a severe public health concern and one of the leading causes of cancer-related death in women worldwide. Several genes and mutations in these genes linked to breast cancer have been identified using sophisticated techniques, despite the fact that the exact cause of breast cancer is still unknown. A commonly used feature for identifying driver mutations is the recurrence of a mutation in patients. Nevertheless, some mutations are more likely to occur than others for various reasons. Sequencing analysis has shown that cancer-driving genes operate across complex networks, often with mutations appearing in a modular pattern. In this work, as a retrospective study, we used TCGA data, which is gathered from breast cancer patients. We introduced a new machine-learning approach to examine gene functionality in networks derived from mutation associations, gene-gene interactions, and graph clustering for breast cancer analysis. These networks have uncovered crucial biological components in critical pathways, particularly those that exhibit low-frequency mutations. The statistical strength of the clinical study is significantly boosted by evaluating the network as a whole instead of just single gene effects. Our method successfully identified essential driver genes with diverse mutation frequencies. We then explored the functions of these potential driver genes and their related pathways. By uncovering low-frequency genes, we shed light on understudied pathways associated with breast cancer. Additionally, we present a novel Monte Carlo-based algorithm to identify driver modules in breast cancer. Our findings highlight the significance and role of these modules in critical signaling pathways in breast cancer, providing a comprehensive understanding of breast cancer development. Materials and implementations are available at: [https://github.com/MahnazHabibi/BreastCancer].

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.