Study on the Prognostic Values of Dynactin Genes in Low-Grade Glioma

Altered expression of vesicular trafficking machinery in prostate cancer affects lysosomal dynamics and provides insight into the underlying biology and disease progression

BACKGROUND

This study focuses on the role of lysosomal trafficking in prostate cancer, given the essential role of lysosomes in cellular homoeostasis.

METHODS

Lysosomal motility was evaluated using confocal laser scanning microscopy of LAMP-1-transfected prostate cells and spot-tracking analysis. Expression of lysosomal trafficking machinery was evaluated in patient cohort databases and through immunohistochemistry on tumour samples. The roles of vesicular trafficking machinery were evaluated through over-expression and siRNA. The effects of R1881 treatment on lysosome vesicular trafficking was evaluated by RNA sequencing, protein quantification and fixed- and live-cell microscopy.

RESULTS

Altered regulation of lysosomal trafficking genes/proteins was observed in prostate cancer tissue, with significant correlations for co-expression of vesicular trafficking machinery in Gleason patterns. The expression of trafficking machinery was associated with poorer patient outcomes. R1881 treatment induced changes in lysosomal distribution, number, and expression of lysosomal vesicular trafficking machinery in hormone-sensitive prostate cancer cells. Manipulation of genes involved in lysosomal trafficking events induced changes in lysosome positioning and cell phenotype, as well as differential effects on cell migration, in non-malignant and prostate cancer cells.

CONCLUSIONS

These findings provide novel insights into the altered regulation and functional impact of lysosomal vesicular trafficking in prostate cancer pathogenesis.

Dynein-Powered Cell Locomotion Guides Metastasis of Breast Cancer

The principal cause of death in cancer patients is metastasis, which remains an unresolved problem. Conventionally, metastatic dissemination is linked to actomyosin-driven cell locomotion. However, the locomotion of cancer cells often does not strictly line up with the measured actomyosin forces. Here, a complementary mechanism of metastatic locomotion powered by dynein-generated forces is identified. These forces arise within a non-stretchable microtubule network and drive persistent contact guidance of migrating cancer cells along the biomimetic collagen fibers. It is also shown that the dynein-powered locomotion becomes indispensable during invasive 3D migration within a tissue-like luminal network formed by spatially confining granular hydrogel scaffolds (GHS) made up of microscale hydrogel particles (microgels). These results indicate that the complementary motricity mediated by dynein is always necessary and, in certain instances, sufficient for disseminating metastatic breast cancer cells. These findings advance the fundamental understanding of cell locomotion mechanisms and expand the spectrum of clinical targets against metastasis.

Dynein-Powered Cell Locomotion Guides Metastasis of Breast Cancer

Metastasis is a principal cause of death in cancer patients, which remains an unresolved fundamental and clinical problem. Conventionally, metastatic dissemination is linked to the actomyosin-driven cell locomotion. However, locomotion of cancer cells often does not strictly line up with the measured actomyosin forces. Here, we identify a complementary mechanism of metastatic locomotion powered by the dynein-generated forces. These forces that arise within a non-stretchable microtubule network drive persistent contact guidance of migrating cancer cells along the biomimetic collagen fibers. We also show that dynein-powered locomotion becomes indispensable during invasive 3D migration within a tissue-like luminal network between spatially confining hydrogel microspheres. Our results indicate that the complementary contractile system of dynein motors and microtubules is always necessary and in certain instances completely sufficient for dissemination of metastatic breast cancer cells. These findings advance fundamental understanding of cell locomotion mechanisms and expand the spectrum of clinical targets against metastasis.

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning

Background

Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC.

Purpose

This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients.

Methods

The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels.

Results

A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts.

Conclusion

The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.

Identification of DNA Repair-Related Genes Predicting Clinical Outcome for Thyroid Cancer

Recent studies have demonstrated the utility and superiority of DNA repair-related genes as novel biomarkers for cancer diagnosis, prognosis, and therapy. Here, we aimed to screen the potential survival-related DNA repair-related genes in thyroid cancer (TC). TCGA datasets were utilized to analyze the differentially expressed DNA repair-related genes between TC and nontumor tissues. The K–M approach and univariate analysis were employed to screen survival-related genes. RT-PCR was employed to examine the expression of DNA repair-related genes in TC samples and matched noncancer samples. CCK-8 analyses were used to determine cellular proliferation. Herein, our team discovered that the expression of four DNA repair-related genes was remarkably upregulated in TC samples in contrast to noncancer samples. Survival assays identified 14 DNA repair-related genes. In our cohort, we observed that the expression of TAF13 and DCTN4 was distinctly elevated in TC specimens in contrast to nontumor specimens. Moreover, knockdown of TAF13 and DCTN4 was observed to inhibit the TC cellular proliferation. Overall, the upregulation of TAF13 and DCTN4 is related to decreased overall survival in TC patients. Therefore, the assessment of TAF13 and DCTN4 expression may be useful for predicting prognosis in these patients.

Prediction of Survival Outcome in Lower-Grade Glioma Using a Prognostic Signature with 33 Immune-Related Gene Pairs

Background

Lower-grade glioma (LGG) is one of the prevalent malignancies threatening human health, with considerable intrinsic heterogeneities in their biological behavior. Previous studies have revealed that the immune component is a key factor influencing the formation and development of malignancies. In this study, we aim to use a novel approach to develop a prognostic signature of immune-related gene pairs (IRGPs) to determine the survival outcome of patients with LGG.

Methods

Transcriptomic profiles and clinical data for LGG were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and used as training and validation data sets, respectively. IRGPs influencing the overall survival (OS) of patients with LGG in the training data set were screened by performing univariate Cox regression analysis. Next, a prognostic IRGPs signature was constructed using least absolute shrinkage and selection operator (LASSO) regression. Finally, we cross-validated the two databases to verify the stability of the prognostic signature.

Results

A total of 33 IRGPs influencing prognosis of LGG in the training data set were included in the prognostic signature. Patients with high risk scores (RSs) in the training and validation data sets had a poorer OS than those with low RSs. Moreover, significant differences were observed in tumor-infiltrating immune cells (TICs) between high- and low-RS groups. Functional enrichment analyses results revealed that genes in the high-RS group were enriched in the immune-related activities and developmental processes.

Conclusion

The prognostic signature containing 33 IRGPs has a significant correlation with OS and relative levels of immune cells associated with LGG. The results of the present study provide new insights into the prediction of survival outcome and therapeutic response of LGG.