Computational Proteome-Wide Study for the Prediction of Escherichia coli Protein Targeting in Host Cell Organelles and Their Implication in Development of Colon Cancer

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain.

AIM

To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.

RESULTS

An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression.

CONCLUSION

We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

Long Non-coding RNA LINC00473 Promotes Breast Cancer Progression via miR-424-5p/CCNE1 Pathway

BACKGROUND

There has been a large increase in the incidence of breast cancer (BC) among women. LINC00473 is a cancer-related lncRNA, participating in the progression of many cancers, but its role in the progression of BC awaits more elaboration.

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to quantify LINC00473, miR-424-5p, and cyclin E1 (CCNE1) mRNA expression levels in BC tissues and cells. Cell counting kit-8 (CCK-8) assay was employed to detect the cell viability; the cell migration and invasion abilities were evaluated by the Transwell assay. Western blot and immunohistochemistry (IHC) were adopted to study CCNE1 protein expression; dual-luciferase reporter assay was performed to clarify the targeting relationships among LINC00473, miR-424-5p, and CCNE1.

RESULTS

LINC00473 expression was elevated in BC tissues and cell lines, which was associated with lymph node metastasis and higher clinical stage of the patients with BC. LINC00473 proved to be a molecular sponge for miR-424-5p; LINC00473 knockdown impeded the growth, migration, invasion, and epithelial-mesenchymal transition of BC cells, while these effects were abolished by miR-424-5p inhibitors; miR-424-5p targeted CCNE1 to restrain its expression. LINC00473 positively regulated CCNE1 expression, and CCNE1 restoration counteracted the effects induced by LINC00473 knockdown in BC cells.

CONCLUSION

LINC00473 facilitates the progression of BC through miR-424-5p/CCNE1 axis.

Effect of gut microbiota on colorectal cancer progression and treatment

Microbiota is a collection of bacteria, archaea, eukaryotes, bacteriophages, viruses, and fungi that cover human body surfaces and cavities. They characterize inside the body due to several factors such as diet, nutrition, xenobiotic substances, and microbial infections. Several studies have shown that gut microbiota can induce resistance against pathogens and regulate the immune system. In addition, their disruption is associated with several physiological and biochemical disorders, including inflammatory bowel disease (IBD), obesity, autoimmune diseases such as diabetes, hypertension, colon cancer, and cardiovascular disease. Colorectal cancer (CRC) is the third-deadliest cancer worldwide, accounting for approximately 900,000 deaths per year globally. Gut microbiota has been heavily linked to CRC incidence and prevention via bacterial metabolites, invasion, translocation, host's defense modulations, and bacterial-immune system interactions. In addition, it can influence the metabolism of chemical compounds such as drugs and xenobiotics to manipulate the treatment response in CRC patients.

Deciphering the enigmatic crosstalk between prostate cancer and Alzheimer's disease: A current update on molecular mechanisms and combination therapy

Alzheimer's disease (AD) and prostate cancer (PCa) are considered the leading causes of death in elderly people worldwide. Although both these diseases have striking differences in their pathologies, a few underlying mechanisms are similar when cell survival is considered. In the current study, we employed an in-silico approach to decipher the possible role of bacterial proteins in the initiation and progression of AD and PCa. We further analyzed the molecular connections between these two life-threatening diseases. The androgen deprivation therapy used against PCa has been shown to promote castrate resistant PCa as well as AD. In addition, cell signaling pathways, such as Akt, IGF, and Wnt contribute to the progression of both AD and PCa. Besides, various proteins and genes are also common in disease progression. One such similarity is mTOR signaling. mTOR is the common downstream target for many signaling pathways and plays a vital role in both PCa and AD. Targeting mTOR can be a favorable line of treatment for both AD and PCa. However, drug resistance is one of the challenges in effective drug therapy. A few drugs that target mTOR have now become ineffective due to the development of resistance. In that regard, phytochemicals can be a rich source of novel drug candidates as they can act via multiple mechanisms. This review also presents mTOR targeting phytochemicals with promising anti-PCa, anti-AD activities, and approaches to overcome the issues associated with phytochemical-based therapies in clinical trials.

The OmpA of commensal Escherichia coli of CRC patients affects apoptosis of the HCT116 colon cancer cell line

BACKGROUND

Colorectal cancer ranks third globally among all types of cancers. Dysbiosis of the gut microbiota of people with CRC is one of the effective agents in the tumorigenesis and metastasis in this type of cancer. The population of Escherichia coli strains, a component of gut microbiota, is increased in the gut of people with CRC compared with healthy people. So, E.coli strains isolated from these patients may have a role in tumorigenesis. Because the most isolated strains belong to the B2 phylogenuetic group, there seems to be a linkage between the bacterium components and malignancy.

MATERIAL AND METHODS

In this study, the proteomic comparison between isolated Ecoli from CRC patients and healthy people was assayed. The isolated spot was studied by Two-dimensional gel electrophoresis (2DE) and Liquid chromatography-mass spectrometry (LC-MS). The results showed that the expression of Outer membrane protein A (OmpA) protein increased in the commensal E.coli B2 phylogenetic group isolated from CRC patients. Additionally, we analyzed the effect of the OmpA protein on the expression of the four genes related to apoptosis in the HCT116 colon cancer cell line.

RESULTS

This study identified that OmpA protein was overexpressed in the commensal E.coli B2 phylogenetic group isolated from CRC patients compared to the E.coli from the control group. This protein significantly decreased the expression of Bax and Bak, pro-apoptotic genes, as well as the expression of P53 in the HCT116 Cell Line, P < 0.0001. LC-MS and protein bioinformatics results confirmed that this protein is outer membrane protein A, which can bind to nucleic acid and some of the organelle proteins on the eukaryotic cell surface.

CONCLUSIONS

According to our invitro and insilico investigations, OmpA of gut E.coli strains that belong to the B2 phylogenetic group can affect the eukaryotic cell cycle.

Gut microbiota correlates with antitumor activity in patients with mCRC and NSCLC treated with cetuximab plus avelumab

Gut microbiota is involved in immune modulation and immune checkpoint inhibitors (ICIs) efficacy. Single‐arm phase II CAVE‐mCRC and CAVE‐LUNG clinical trials investigated cetuximab + avelumab combination in RAS wild‐type (WT) metastatic colorectal cancer (mCRC) and chemo‐refractory nonsmall cell lung cancer (NSCLC) patients, respectively. A comprehensive gut microbiota genetic analysis was done in basal fecal samples of 14 patients from CAVE‐mCRC trial with circulating tumor DNA (ctDNA) RAS/BRAF WT and microsatellite stable (MSS) disease. Results were validated in a cohort of 10 patients from CAVE‐Lung trial. 16S rRNA sequencing revealed 23 027 bacteria species in basal fecal samples of 14 patients from CAVE‐mCRC trial. In five long‐term responding patients (progression‐free survival [PFS], 9‐24 months) significant increases in two butyrate‐producing bacteria, Agathobacter M104/1 (P = .018) and Blautia SR1/5 (P = .023) were found compared to nine patients with shorter PFS (2‐6 months). A significantly better PFS was also observed according to the presence or absence of these species in basal fecal samples. For Agathobacter M104/1, median PFS (mPFS) was 13.5 months (95% confidence interval [CI], 6.5‐20.5 months) vs 4.6 months (95% CI, 1.8‐7.4 months); P = .006. For Blautia SR1/5, mPFS was 5.9 months (95% CI, 2.2‐9.7 months) vs 3.6 months (95% CI, 3.3‐4.0 months); P = .021. Similarly, in CAVE‐Lung validation cohort, Agathobacter M104/1 and Blautia SR1/5 expression were associated with PFS according to their presence or absence in basal fecal samples. Agathobacter and Blautia species could be potential biomarkers of outcome in mCRC, and NSCLC patients treated with cetuximab + avelumab. These findings deserve further investigation.

PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion

Phage therapy has become one of the most promising alternatives to antibiotics in the treatment of bacterial diseases, and identifying phage-host interactions (PHIs) helps to understand the possible mechanism through which a phage infects bacteria to guide the development of phage therapy. Compared with wet experiments, computational methods of identifying PHIs can reduce costs and save time and are more effective and economic. In this paper, we propose a PHI prediction method with a generative adversarial network (GAN)-based data augmentation and sequence-based feature fusion (PHIAF). First, PHIAF applies a GAN-based data augmentation module, which generates pseudo PHIs to alleviate the data scarcity. Second, PHIAF fuses the features originated from DNA and protein sequences for better performance. Third, PHIAF utilizes an attention mechanism to consider different contributions of DNA/protein sequence-derived features, which also provides interpretability of the prediction model. In computational experiments, PHIAF outperforms other state-of-the-art PHI prediction methods when evaluated via 5-fold cross-validation (AUC and AUPR are 0.88 and 0.86, respectively). An ablation study shows that data augmentation, feature fusion and an attention mechanism are all beneficial to improve the prediction performance of PHIAF. Additionally, four new PHIs with the highest PHIAF score in the case study were verified by recent literature. In conclusion, PHIAF is a promising tool to accelerate the exploration of phage therapy.

Cytotoxic potentials of silibinin assisted silver nanoparticles on human colorectal HT-29 cancer cells

It is of interest to study the cytotoxicity of silibinin assisted silver nanoparticles in human colorectal (HT-29) cancer cells. Silver nanoparticles were synthesized using silibinin as a reducing agent. The synthesized silibinin assisted silver nanoparticles ( SSNPs) were characterized and analyzed using a transmission electron microscope and spectrophotometer. The SSNPs synthesized in this study are spherical and their size ranges from 10 to 80 nm. HT-29 cells were treated with different concentrations (2, 4, 6, 8 and 10 ng/mL) of SSNPs and cytotoxicity was evaluated. The apoptosis was using flow cytometry. p53 protein expression using western blot. SSNPs are induced a decrease in viability and increased concentration-dependent cytotoxicity in HT-29 cells. SSNPs treatment also caused apoptosis-related morphological changes. SSNPs treatments at 8 and 16 ng/ml showed a prominent apoptotic change i.e., 70.3% and 83.6% respectively, and decreased viability of HT-29 cells 20% and 11.2% respectively as compared to control cells. SSNPs treatments induced p53 expression in HT-29 cells. Data shows that SSNPs have the potential to induce apoptosis in colorectal cancer cells. This provides insights for the further evaluation of SSNPs in fighting colon cancer.

Expression and prognostic analyses of the insulin-like growth factor 2 mRNA binding protein family in human pancreatic cancer

BACKGROUND

Despite advances in early diagnosis and treatment, cancer remains the leading cause of mortality worldwide. The insulin-like growth factor 2 mRNA binding protein (IGF2BP) family has been reported to be involved in a variety of human malignant tumours. However, little is known about their expression and prognostic value in human pancreatic cancer. Therefore, we performed a detailed cancer versus normal differential analysis.

METHODS

The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases were used to analyse the mRNA expression levels of the IGF2BP family in various cancers, including pancreatic cancer. Then, the LinkedOmics and GEPIA databases were used to assess the relation between the expression levels of IGF2BPs and overall survival (OS). Then, univariate and multivariate Cox regression analyses were performed, and subgroups based on grade and stage were analysed. The signalling pathways associated with IGF2BP2 and IGF2BP3 were then investigated via gene set enrichment analysis (GSEA).

RESULTS

IGF2BP2 and IGF2BP3 were associated with each subset of OS based on grade and stage. Further clinical correlation analysis of IGF2BP2 and IGF2BP3 confirmed that IGF2BP2 and IGF2BP3 are fundamental factors in promoting pancreatic cancer progression.

CONCLUSION

IGF2BP2 and IGF2BP3 are key factors in promoting the progression of pancreatic cancer and are closely related to overall survival.