New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics.

Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment.

BACKGROUND: In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms. METHODS: To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes. RESULTS: Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint. CONCLUSIONS: The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Non-invasive STEMI-related biomarkers based on meta-analysis and gene prioritization.

BACKGROUND AND AIMS: Acute ST-Segment Myocardial infarction (STEMI) is a common cardiovascular issue with a considerable burden of the disease. The underlying genetic basis and non-invasive markers were not well-established. METHODS: Here, we implemented a systematic literature review and meta-analyses integration methods on 217 STEMI patients and 72 normal individuals to prioritize and detect the STEMI-related non-invasive markers. Five high-scored genes were experimentally assessed on 10 STEMI patients and 9 healthy controls. Finally, the presence of co-expressed nodes of top-score genes was explored. RESULTS: The differential expression of ARGL, CLEC4E, and EIF3D were significant for Iranian patients. The ROC curve for gene CLEC4E revealed an AUC (95% CI) of 0.786 (0.686-0.886) in the prediction of STEMI. The Cox-PH model was fitted to stratify high/low risk heart failure progression (CI-index = 0.83, Likelihood-Ratio-Test = 3e-10). The SI00AI2 was a common biomarker between STEMI and NSTEMI patients. CONCLUSIONS: In conclusion, the high-scored genes and prognostic model could be applicable for Iranian patients.

Non-coding RNA in SARS-CoV-2: Progress toward therapeutic significance.

The recently developed pathogenic virus, SARS-CoV-2, was found in the Hubei Province, China. Giving rise to a broad spectrum of symptoms, SARS-CoV-2 rapidly spread across the globe, causing multi-systemic and dangerous complications, with death in extreme cases. Thereby, the number of research cases increases every day on preventing infection and treating its resulting damage. Accumulating evidence suggests noncoding RNAs (ncRNAs) are necessary for modifying virus infection and antiviral immune reaction, along with biological processes regulating SARS-CoV-2 and subsequent disease states. Therefore, understanding these mechanisms might provide a further understanding of the pathogenesis and feasible therapy alternatives against SARS-CoV2. Consequently, the molecular biology of SARS-CoV-2, ncRNA's role in its infection, and various RNA therapy tactics against the virus have been presented in this review section.

Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers.

BACKGROUND: Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identification of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identified. The relationship between modules was assessed using the Pearson correlation coefficient (p-value < 0.05). An elastic network classifier was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efficiency of the model, the Area Under the Curve (AUC) was computed using five-fold cross-validation in an external dataset. RESULTS: The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organization, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73-1.00) was observed for the predictive model including 11 genes. CONCLUSION: The intricate association between biological pathways of identified modules may reveal the hidden significance of 5-hmC in GI cancers. The identified predictive model and new biomarkers may be beneficial in cancer detection and precision medicine using liquid biopsy in the early stages.

Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression.

BACKGROUND: Elucidating the dynamic topological changes across different stages of breast cancer, called stage re-wiring, could lead to identifying key latent regulatory signatures involved in cancer progression. Such dynamic regulators and their functions are mostly unknown. Here, we reconstructed differential co-expression networks for four stages of breast cancer to assess the dynamic patterns of cancer progression. A new computational approach was applied to identify stage-specific subnetworks for each stage. Next, prognostic traits of genes and the efficiency of stage-related groups were evaluated and validated, using the Log-Rank test, SVM classifier, and sample clustering. Furthermore, by conducting the stepwise VIF-feature selection method, a Cox-PH model was developed to predict patients' risk. Finally, the re-wiring network for prognostic signatures was reconstructed and assessed across stages to detect gain/loss, positive/negative interactions as well as rewired-hub nodes contributing to dynamic cancer progression. RESULTS: After having implemented our new approach, we could identify four stage-specific core biological pathways. We could also detect an essential non-coding RNA, AC025034.1, which is not the only antisense to ATP2B1 (cell proliferation regulator), but also revealed a statistically significant stage-descending pattern; Moreover, AC025034.1 revealed both a dynamic topological pattern across stages and prognostic trait. We also identified a high-performance Overall-Survival-Risk model, including 12 re-wired genes to predict patients' risk (c-index = 0.89). Finally, breast cancer-specific prognostic biomarkers of LINC01612, AC092142.1, and AC008969.1 were identified. CONCLUSIONS: In summary new scoring method highlighted stage-specific core pathways for early-to-late progressions. Moreover, detecting the significant re-wired hub nodes indicated stage-associated traits, which reflects the importance of such regulators from different perspectives.

Pan-cancer analysis of biological events on cell cycle instability in gastrointestinal cancers with integrative scoring method.

Omics data integration plays an essential role in manifesting hidden cancer insights. To detect the main combinatorial/parallel impact of cancer events, integrative approaches in pan-cancer studies must be used. Here, we assessed gastrointestinal (GI) cancers from several perspectives of genomics, transcriptomics, epigenomics, and also combinatorial impacts using a novel integrative approach to score genes. Next, scores were diffused on a signaling network and extracted subnetworks. We also implemented our new scoring method to compare upper-/lower-GI cancers, investigate the regulatory mechanisms of lncRNAs, and detect amplifications/deletions between GI and non-GI cancers. The integrative subnetwork indicated the interplay among essential protein families in the cell cycle. The copy-number-variation-related subnetwork revealed minor cell cycle and immune effects, whereas the methylation-related subnetwork revealed significant immune effects. The top-score lncRNAs indicated a distinct regulatory pattern for lower-/upper-, and accessory-GI categories. In summary, cell cycle dysfunction might be largely the consequence of combinatorial abnormalities.