Computational systems biology approach for permanent tumor elimination and normal tissue protection using negative biasing: Experimental validation in malignant melanoma as case study

Complete spontaneous tumor regression (without treatment) is well documented to occur in animals and humans as epidemiological analysis show, whereby the malignancy is permanently eliminated. We have developed a novel computational systems biology model for this unique phenomenon to furnish insight into the possibility of therapeutically replicating such regression processes on tumors clinically, without toxic side effects. We have formulated oncological informatics approach using cell-kinetics coupled differential equations while protecting normal tissue. We investigated three main tumor-lysis components: (ⅰ) DNA blockade factors, (ⅱ) Interleukin-2 (IL-2), and (ⅲ) Cytotoxic T-cells (CD8⁺ T). We studied the temporal variations of these factors, utilizing preclinical experimental investigations on malignant tumors, using mammalian melanoma microarray and histiocytoma immunochemical assessment. We found that permanent tumor regression can occur by: 1) Negative-Bias shift in population trajectory of tumor cells, eradicating them under first-order asymptotic kinetics, and 2) Temporal alteration in the three antitumor components (DNA replication-blockade, Antitumor T-lymphocyte, IL-2), which are respectively characterized by the following patterns: (a) Unimodal Inverted-U function, (b) Bimodal M-function, (c) Stationary-step function. These provide a time-wise orchestrated tri-phasic cytotoxic profile. We have also elucidated gene-expression levels corresponding to the above three components: (ⅰ) DNA-damage G2/M checkpoint regulation [genes: CDC2-CHEK], (ⅱ) Chemokine signaling: IL-2/15 [genes: IL2RG-IKT3], (ⅲ) T-lymphocyte signaling (genes: TRGV5-CD28). All three components quantitatively followed the same activation profiles predicted by our computational model (Smirnov-Kolmogorov statistical test satisfied, α = 5%). We have shown that the genes CASP7-GZMB are signatures of Negative-bias dynamics, enabling eradication of the residual tumor. Using the negative-biasing principle, we have furnished the dose-time profile of equivalent therapeutic agents (DNA-alkylator, IL-2, T-cell input) so that melanoma tumor may therapeutically undergo permanent extinction by replicating the spontaneous tumor regression dynamics.

Iterative bicluster-based Bayesian principal component analysis and least squares for missing-value imputation in microarray and RNA-sequencing data

Microarray and RNA-sequencing (RNA-seq) techniques each produce gene expression data that can be expressed as a matrix that often contains missing values. Thus, a process of missing-value imputation that uses coherence information of the dataset is necessary. Existing imputation methods, such as iterative bicluster-based least squares (bi-iLS), use biclustering to estimate the missing values because genes are only similar under correlative experimental conditions. Also, they use the row average to obtain a temporary complete matrix, but the use of the row average is considered to be a flaw. The row average cannot reflect the real structure of the dataset because the row average only uses the information of an individual row. Therefore, we propose the use of Bayesian principal component analysis (BPCA) to obtain the temporary complete matrix instead of using the row average in bi-iLS. This alteration produces new missing values imputation method called iterative bicluster-based Bayesian principal component analysis and least squares (bi-BPCA-iLS). Several experiments have been conducted on two-dimension independent gene expression datasets, which are microarray (e.g., cell-cycle expression dataset of yeast saccharomyces cerevisiae) and RNA-seq (gene expression data from schizosaccharomyces pombe) datasets. In the case of the microarray dataset, our proposed bi-BPCA-iLS method showed a significant overall improvement in the normalized root mean square error (NRMSE) values of 10.6% from the local least squares (LLS) and 0.6% from the bi-iLS. In the case of the RNA-seq dataset, our proposed bi-BPCA-iLS method showed an overall improvement in the NRMSE values of 8.2% from the LLS and 3.1% from the bi-iLS. The additional computational time of bi-BPCA-iLS is not significant compared to bi-iLS.

Upregulation of BIRC5 plays essential role in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers in the world, the detection and prognosis of which are still unsatisfactory. Thus, it is essential to explore the factors that may identify ESCC and evaluate the prognosis of ESCC patients.

RESULTS

Both protein and mRNA expression levels of BIRC5 are upregulated in ESCC group rather than non-ESCC group (standardized mean difference > 0). BIRC5 mRNA expression is related to the age, tumor location, lymph node stage and clinical stage of ESCC patients (p < 0.05). BIRC5 expression makes it feasible to distinguish ESCC from non-ESCC (area under the curve > 0.9), and its high expression is related to poor prognosis of ESCC patients (restrictive survival time difference = -0.036, p < 0.05). BIRC5 may play an important role in ESCC by influencing the cell cycle pathway, and CDK1, MAD2L and CDC20 may be the hub genes of this pathway. The transcription factors-MAZ and TFPD1 -are likely to regulate the transcription of BIRC5, which may be one of the factors for the high expression of BIRC5 in ESCC.

CONCLUSIONS

The current study shows that upregulation of BIRC5 may have essential clinical value in ESCC, and contributes to the understanding of the pathogenesis of ESCC.

Downregulation of CDC14B in 5218 breast cancer patients: A novel prognosticator for triple-negative breast cancer

Breast cancer is the most common female malignancy worldwide and the prognosis of triple-negative breast cancer (TNBC) and advanced breast cancer patients is unsatisfying. The exploration of novel prognostic indicators and appropriate targets is crucial for improving the treatment outcomes of breast cancer patients. The cell division cycle protein 14B (CDC14B) is known for its roles in cell cycle control, but its expression status and molecular function in breast cancer is unknown. This study explores the expression patterns and clinical values of CDC14B in breast cancer tissues. For this research, the authors downloaded gene microarrays and RNA sequencing datasets to examine the expression levels of CDC14B in 5218 breast cancer tissues, comparing them to the expression levels in 1176 normal breast tissues. The relationships between CDC14B and clinicopathologic characteristics of breast cancer were also addressed. The mutation conditions of CDC14B were then clarified using cBioPortal. Finally, differentially expressed genes and co-expressed genes related to CDC14B were filtered using the Limma-Voom package. These genes were intersected to conduct functional annotations and to construct a protein-protein interaction network. It was observed that CDC14B was significantly downregulated in breast cancer tissues but not in normal breast tissues (standardized mean difference = -1.17 [-1.50--0.85], area under the curve = 0.88). In addition, CDC14B downregulation was correlated with the poor prognosis of TNBC patients (hazard ratios < 1; p < 0.05). Amplification was detected to be the most frequent alteration of CDC14B. The presence of this alteration forecasted unfavourable overall survival outcomes in breast cancer patients (p < 0.05). Dysregulated genes that co-expressed with CDC14B were pivotal in cell cycle (namely mitotic-nuclear division and DNA packaging complex) and cancer-related signaling pathways (namely the peroxisome proliferators activated receptor [PPAR] signalling pathway and the AMP-activated protein kinase [AMPK] signalling pathway). Moreover, the genes ADIPOQ and CCNE2 were identified as two promising prognostic factors in breast cancer. In summary, CDC14B was downregulated in breast cancer tissue and may be a promising hallmark in TNBC patients. The dysregulated genes co-expressed with CDC14B may play an important role in the development of breast cancer through PPAR and AMPK signalling pathways.