Analysis of RPL37A, MTSS1, and HTRA1 expression as potential markers for pathologic complete response and survival

RNA-seq analysis-based study on the effects of gestational diabetes mellitus on macrosomia

Background

Both the mother and the infant are negatively impacted by macrosomia. Macrosomia is three times as common in hyperglycemic mothers as in normal mothers. This study sought to determine why hyperglycemic mothers experienced higher macrosomia. Methods: Hematoxylin and Eosin staining was used to detect the placental structure of normal mother(NN), mothers who gave birth to macrosomia(NM), and mothers who gave birth to macrosomia and had hyperglycemia (DM). The gene expressions of different groups were detected by RNA-seq. The differentially expressed genes (DEGs) were screened with DESeq2 R software and verified by qRT-PCR. The STRING database was used to build protein-protein interaction networks of DEGs. The Cytoscape was used to screen the Hub genes of the different group.

Results

The NN group's placental weight differed significantly from that of the other groups. The structure of NN group's placenta is different from that of the other group, too. 614 and 3207 DEGs of NM and DM, respectively, were examined in comparison to the NN group. Additionally, 394 DEGs of DM were examined in comparison to NM. qRT-PCR verified the results of RNA-seq. Nucleolar stress appears to be an important factor in macrosomia, according on the results of KEGG and GO analyses. The results revealed 74 overlapped DEGs that acted as links between hyperglycemia and macrosomia, and 10 of these, known as Hub genes, were key players in this process. Additionally, this analysis believes that due of their close connections, non-overlapping Hubs shouldn't be discounted.

Conclusion

In diabetic mother, ten Hub genes (RPL36, RPS29, RPL8 and so on) are key factors in the increased macrosomia in hyperglycemia. Hyperglycemia and macrosomia are linked by 74 overlapping DEGs. Additionally, this approach contends that non-overlapping Hubs shouldn't be ignored because of their tight relationships.

A potential new biomarker in HNSCC: metastasis suppressor protein 1 (MTSS1)

OBJECTIVE

Metastasis suppressor protein 1 (MTSS1) is a prognostic tumour marker in different malignant epithelial tumour entities and previously mainly the MTSS1 expression was analysed. This study evaluated the best analysis method as a prognosis and aggressiveness tumour marker in head and neck squamous cell carcinoma (HNSCC).

STUDY DESIGN

MTSS1 expression, MTSS1 intensity, interpretation MTSS1 score and MTSS1 edging score were analysed in formalin-fixed paraffin-embedded tissue slices of 60 patients with proven HNSCC and correlated with clinical and pathological outcome parameters.

RESULTS

A lack of MTSS1 expression showed tumour aggressiveness, but surprisingly, mainly MTSS1 intensity was correlated with a worse patient outcome. There was a significant correlation between higher MTSS1 intensity and an increased risk for lymph node metastasis (P = .027) and a significant increased risk for extracapsular growth (P = .016). Furthermore, disease-specific survival was worse in cases with higher MTSS1 intensity (P = .001).

CONCLUSION

MTSS1 intensity has a high scientific potential for further studies and could potentially be used as a prognostic marker in diagnostic and therapeutic decision-making.

A performance evaluation of drug response prediction models for individual drugs

Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC₅₀]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC_50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R² ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R² 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.

AhR Activation Leads to Attenuation of Murine Autoimmune Hepatitis: Single-Cell RNA-Seq Analysis Reveals Unique Immune Cell Phenotypes and Gene Expression Changes in the Liver

The aryl hydrocarbon receptor (AhR) is a ubiquitously expressed ligand-activated transcription factor. While initially identified as an environmental sensor, this receptor has been shown more recently to regulate a variety of immune functions. AhR ligands vary in structure and source from environmental chemicals such as 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and indoles found in cruciferous vegetables to endogenous ligands derived from tryptophan metabolism. In the current study, we used TCDD, a high affinity AhR ligand to study the impact of AhR activation in the murine model of autoimmune hepatitis (AIH). Primarily, we used single-cell RNA-sequencing (scRNA-seq) technology to study the nature of changes occurring in the immune cells in the liver at the cellular and molecular level. We found that AhR activation attenuated concanavalin A (ConA)-induced AIH by limiting chemotaxis of pro-inflammatory immune cell subsets, promoting anti-inflammatory cytokine production, and suppressing pro-inflammatory cytokine production. scRNA-seq analysis showed some unusual events upon ConA injection such as increased presence of mature B cells, natural killer (NK) T cells, CD4+ or CD8+ T cells, Kupffer cells, memory CD8+ T cells, and activated T cells while TCDD treatment led to the reversal of most of these events. Additionally, the immune cells showed significant alterations in the gene expression profiles. Specifically, we observed downregulation of inflammation-associated genes including Ptma, Hspe1, and CD52 in TCDD-treated AIH mice as well as alterations in the expression of migratory markers such as CXCR2. Together, the current study characterizes the nature of inflammatory changes occurring in the liver during AIH, and sheds light on how AhR activation during AIH attenuates liver inflammation by inducing phenotypic and genotypic changes in immune cells found in the liver.

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

Cancer risk by environmental exposure is modulated by an individual's genetics and age at exposure. This age-specific period of susceptibility is referred to as the "Window of Susceptibility" (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.

High temperature requirement A1 in cancer: biomarker and therapeutic target

As the life expectancy of the population increases worldwide, cancer is becoming a substantial public health problem. Considering its recurrence and mortality rates, most cancer cases are difficult to cure. In recent decades, a large number of studies have been carried out on different cancer types; unfortunately, tumor incidence and mortality have not been effectively improved. At present, early diagnostic biomarkers and accurate therapeutic strategies for cancer are lacking. High temperature requirement A1 (HtrA1) is a trypsin-fold serine protease that is also a chymotrypsin-like protease family member originally discovered in bacteria and later discovered in mammalian systems. HtrA1 gene expression is decreased in diverse cancers, and it may play a role as a tumor suppressor for promoting the death of tumor cells. This work aimed to examine the role of HtrA1 as a cell type-specific diagnostic biomarker or as an internal and external regulatory factor of diverse cancers. The findings of this study will facilitate the development of HtrA1 as a therapeutic target.