Prognostic Value of Dynactin mRNA Expression in Cutaneous Melanoma

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Introduction

Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation

In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement

Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology

We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results

We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion

In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.

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.

Dual Oxidase 2 (DUOX2) as a Proteomic Biomarker for Predicting Treatment Response to Chemoradiation Therapy for Locally Advanced Rectal Cancer: Using High-Throughput Proteomic Analysis and Machine Learning Algorithm

High-throughput mass-spectrometry-based quantitative proteomic analysis was performed using formalin-fixed, paraffin-embedded (FFPE) biopsy samples obtained before treatment from 13 patients with locally advanced rectal cancer (LARC), who were treated with concurrent chemoradiation therapy (CCRT) followed by surgery. Patients were divided into complete responder (CR) and non-complete responder (nCR) groups. Immunohistochemical (IHC) staining of 79 independent FFPE tissue samples was performed to validate the predictive ability of proteomic biomarker candidates. A total of 3637 proteins were identified, and the expression of 498 proteins was confirmed at significantly different levels (differentially expressed proteins—DEPs) between two groups. In Gene Ontology enrichment analyses, DEPs enriched in biological processes in the CR group included proteins linked to cytoskeletal organization, immune response processes, and vesicle-associated protein transport processes, whereas DEPs in the nCR group were associated with biosynthesis, transcription, and translation processes. Dual oxidase 2 (DUOX2) was selected as the most predictive biomarker in machine learning algorithm analysis. Further IHC validation ultimately confirmed DUOX2 as a potential biomarker for predicting the response of nCR to CCRT. In conclusion, this study suggests that the treatment response to RT may be affected by the pre-treatment tumor microenvironment. DUOX2 is a potential biomarker for the early prediction of nCR after CCRT.

Sentinel Lymph Node Gene Expression Signature Predicts Recurrence-Free Survival in Cutaneous Melanoma

We sought to develop a sentinel lymph node gene expression signature score predictive of disease recurrence in patients with cutaneous melanoma. Gene expression profiling was performed on SLN biopsies using U133A 2.0 Affymetrix gene chips. The top 25 genes associated with recurrence-free survival (RFS) were selected and a penalized regression function was used to select 12 genes with a non-zero coefficient. A proportional hazards regression model was used to evaluate the association between clinical covariates, gene signature score, and RFS. Among the 45 patients evaluated, 23 (51%) had a positive SLN. Twenty-one (46.7%) patients developed disease recurrence. For the top 25 differentially expressed genes (DEG), 12 non-zero penalized coefficients were estimated (CLGN, C1QTNF3, ADORA3, ARHGAP8, DCTN1, ASPSCR1, CHRFAM7A, ZNF223, PDE6G, CXCL3, HEXIM1, HLA-DRB). This 12-gene signature score was significantly associated with RFS (p < 0.0001) and produced a bootstrap C index of 0.888. In univariate analysis, Breslow thickness, presence of primary tumor ulceration, SLN positivity were each significantly associated with RFS. After simultaneously adjusting for these prognostic factors in relation to the gene signature, the 12-gene score remained a significant independent predictor for RFS (p < 0.0001). This SLN 12-gene signature risk score is associated with melanoma recurrence regardless of SLN status and may be used as a prognostic factor for RFS.

Prognostic biomarkers of cutaneous melanoma

BACKGROUND/PURPOSE

Melanomas account for only approximately 4% of diagnosed skin cancers in the United States but are responsible for the majority of deaths caused by skin cancer. Both genetic factors and ultraviolet (UV) radiation exposure play a role in the development of melanoma. Although melanomas have a strong propensity to metastasize when diagnosed late, melanomas that are diagnosed and treated early pose a low mortality risk. In particular, the identification of patients with increased metastatic risk, who may benefit from early adjuvant therapies, is crucial, especially given the advent of new melanoma treatments. However, the accuracy of classic clinical and histological variables, including the Breslow thickness, presence of ulceration, and lymph node status, might not be sufficient to identify such individuals. Thus, there is a need for the development of additional prognostic melanoma biomarkers that can improve early attempts to stratify melanoma patients and reliably identify high-risk subgroups with the aim of providing effective personalized therapies.

METHODS

In our current work, we discuss and assess emerging primary melanoma tumor biomarkers and prognostic circulating biomarkers.

RESULTS

Several promising biomarkers show prognostic value (eg, exosomal MIA (ie, melanoma inhibitory activity), serum S100B, AMLo signatures, and mRNA signatures); however, the scarcity of reliable data precludes the use of these biomarkers in current clinical applications.

CONCLUSION

Further research is needed on several promising biomarkers for melanoma. Large-scale studies are warranted to facilitate the clinical translation of prognostic biomarker applications for melanoma in personalized medicine.

A novel 7 RNA-based signature for prediction of prognosis and therapeutic responses of wild-type BRAF cutaneous melanoma

Background

The prognosis of wild-type BRAF cutaneous melanoma (WT Bf-CM) patients remains poor due to the lack of therapeutic options. However, few studies have investigated the factors contributing to the prognosis of WT Bf-CM patients.

Methods

In this paper, we proposed and validated a novel 7-RNA based signature to predict the prognosis of WT Bf-CM by analyzing the information from TCGA database.

Results

Dependence of this signature to other clinical factors were verified and a nomogram was also drawn to promote its application in clinical practice. Functional analysis suggested that the predictive function of this signature might attribute to the prediction of the up-regulation of RNA splicing, transcription, and cellular proliferation in the high-risk group, which have been demonstrated to be linked to malignancy of cancer. Moreover, functional analysis and therapy response analysis supported that the prognosis is highly related to PI3K/Akt/mTOR pathway among WT Bf-CM patients.

Conclusion

Collectively, this study will provide a preliminary bioinformatics evidence for the molecular mechanism and potential drug targets that could improving WT Bf-CM prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12575-022-00170-2.

Construction and Identification of a Novel 5-Gene Signature for Predicting the Prognosis in Breast Cancer

Background: Breast cancer is one of the most common malignancies in women worldwide. The purpose of this study was to identify the hub genes and construct prognostic signature that could predict the survival of patients with breast cancer (BC).

Methods: We identified differentially expressed genes between the responder group and non-responder group based on the GEO cohort. Drug-resistance hub genes were identified by weighted gene co-expression network analysis, and a multigene risk model was constructed by univariate and multivariate Cox regression analysis based on the TCGA cohort. Immune cell infiltration and mutation characteristics were analyzed.

Results: A 5-gene signature (GP6, MAK, DCTN2, TMEM156, and FKBP14) was constructed as a prognostic risk model. The 5-gene signature demonstrated favorable prediction performance in different cohorts, and it has been confirmed that the signature was an independent risk indicater. The nomogram comprising 5-gene signature showed better performance compared with other clinical features, Further, in the high-risk group, high M2 macrophage scores were related with bad prognosis, and the frequency of TP53 mutations was greater in the high-risk group than in the low-risk group. In the low-risk group, high CD8+ T cell scores were associated with a good prognosis, and the frequency of CDH1 mutations was greater in the low-risk group than that in the high-risk group. At the same time, patients in the low risk group have a good response to immunotherapy in terms of immunotherapy. The results of immunohistochemistry showed that MAK, GP6, and TEMEM156 were significantly highly expressed in tumor tissues, and DCTN2 was highly expressed in normal tissues.

Conclusions: Our study may find potential new targets against breast cancer, and provide new insight into the underlying mechanisms.

Binding between ROCK1 and DCTN2 triggers diabetes‑associated centrosome amplification in colon cancer cells

Type 2 diabetes increases the risk various types of cancer and is associated with a poor prognosis therein. There is also evidence that the disease is associated with cancer metastasis. Centrosome amplification can initiate tumorigenesis with metastasis in vivo and increase the invasiveness of cancer cells in vitro. Our previous study reported that type 2 diabetes promotes centrosome amplification via the upregulation and centrosomal translocation of Rho-associated protein kinase 1 (ROCK1), which suggests that centrosome amplification is a candidate biological link between type 2 diabetes and cancer development. In the present study, functional proteomics analysis was used to further investigate the molecular pathways underlying centrosome amplification by targeting ROCK1 binding partners. High glucose, insulin and palmitic acid were used to induce centrosome amplification, and immunofluorescent staining was employed to visualize centrosomal alterations. Combined with immunoprecipitation, mass spectrometry-based proteomics analysis was used to identify ROCK1 binding proteins, and protein complex disruption was achieved by siRNA-knockdown. In total, 1,148 ROCK1 binding proteins were identified, among which 106 proteins were exclusively associated with the treated samples, 193 were only associated with the control samples, and 849 were found in both the control and treated samples. Of the proteins with evidence of centrosomal localization, Dynactin subunit 2 (DCTN2) was confirmed to be localized to the centrosomes. Treating the cells with high glucose, insulin and palmitic acid increased the protein levels of ROCK1 and DCTN2, promoted their binding with each other, and triggered centrosome amplification. Disruption of the protein complex by knocking down ROCK1 or DCTN2 expression partially attenuated centrosome amplification, while simultaneous knockdown of both proteins completely inhibited centrosome amplification. These results suggested ROCK1-DCTN2 binding as a signal for the regulation of centrosome homeostasis, which is key for diabetes-associated centrosome amplification, and enriches our knowledge of centrosome biology. Therefore, the ROCK1-DCTN2 complex may serve as a target for inhibiting centrosome amplification both in research or future therapeutic development.

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

OBJECTIVE

This present study aims to investigate the potential prognostic values of dynactin genes (DCTN) for predicting the overall survival (OS) in low-grade glioma (LGG) patients.

METHODS

The DCTN mRNA expression data were downloaded from The Cancer Genome Atlas database containing 518 patients with LGG. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses for DCTN genes were performed by using Database for Annotation, Visualization, and Integrated Discovery platform, and their enrichment results were verified by using the Biological Networks Gene Ontology tool. Next, the correlations between DCTN genes and LGG were identified by Pearson correlation coefficient analysis. The OS was estimated by Kaplan-Meier survival analysis. The cBio Cancer Genomics Portal was used to analyze the mutations of DCTN genes and their effects on the prognosis of LGG. The correlation between the abundance of immune infiltration and tumor purity of DCTN genes were predicted by The Tumor Immune Estimation Resource.

RESULTS

Our research showed that the mRNA expression of DCTN4 in tumor tissues was much higher (P < 0.01) than that in normal tissues. Meanwhile, there was a certain correlation between the DCTN genes. Survival analysis showed that the high expression of DCTN1, DCTN3, DCTN4, DCTN6, and their co-expression were significantly correlated with favorable OS in LGG patients (P < 0.05). In DCTN2, a high mutation rate was observed. Further research showed that the genetic alteration in DCTN genes was related to a poor OS and progression-free survival of LGG patients. The expression of DCTN genes had a certain correlation with immune infiltrating cells.

CONCLUSION

Our study showed that the high expressions of DCTN1, DCTN3, DCTN4, and DCTN6 were associated with a favorable OS of LGG patients, indicating that these DCTN genes are potential biomarkers for evaluating the prognosis of LGG patients.

Distinct prognostic value of dynactin subunit 4 (DCTN4) and diagnostic value of DCTN1, DCTN2, and DCTN4 in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is ranked as the third most commonly diagnosed cancer in both women and men, and it is the most frequently occurring malignant tumor. Dynactin is a protein compound based on multiple subunits, including dynactin 1–6 (DCTN1–6), in most categories of cytoplasmic dynein performance in eukaryotes. Nevertheless, correlations between the DCTN family and the prognosis and diagnosis of COAD remain unidentified.

Methods

Statistics for DCTN mRNA expression in patients with COAD were acquired from The Cancer Genome Atlas. Kaplan–Meier analyses and a Cox regression model were applied to determine overall survival, with computation of HRs and 95% CIs. Several online data portals were used to assess the biological process, and pathway examination was performed using the Kyoto Encyclopedia of Genes and Genomes to predict the biological functionality of DCTN genes.

Results

We found that high expression of DCTN4 was linked with satisfactory results for overall survival (P=0.042, HR=0.650, 95% CI 0.429–0.985). The expression of DCTN1, DATN2, and DCTN4 was closely correlated with the frequency of colon tumors (P<0.001, area under the curve [AUC]=0.8811, 95% CI 0.8311–0.9312; P<0.001, AUC=0.870, 96% CI 0.833–0.9071; and P=0.0051, AUC=0.6317, 95% CI 0.5725–0.6908, respectively). In the enrichment examination, the level of gene expression was related to the cell cycle, cell apoptosis, and the cell metastasis pathway.

Conclusion

The expression levels of DCTN1, DCTN2, and DCTN4 could allow differentiation between cancer-bearing tissues and paracancerous tissue. These genes can be applied as biomarkers to predict the prognosis and diagnosis of COAD.