Integrated Profiling Identifies PLOD3 as a Potential Prognostic and Immunotherapy Relevant Biomarker in Colorectal Cancer

PLOD3 facilitated T cell activation in the colorectal tumor microenvironment and liver metastasis by the TNF-α/ NF-κB pathway

BACKGROUND

Colorectal cancer (CRC) has been the third most prevalent cancer worldwide. Liver metastasis is the critical factor for the poor prognosis of CRC. Here, we investigated the expression and role of PLOD3 in CRC.

METHODS

Different liver metastasis models were established by injecting PLOD3 stable knockdown or overexpression CT26 or MC38 mouse CRC cells into the spleen of mice to verify the tumorigenicity and metastasis ability in vivo.

RESULTS

We identified PLOD3 is significantly overexpressed in liver metastasis samples of CRC. High expression of PLOD3 was significantly associated with poor survival of CRC patients. The knockdown of PLOD3 exhibited remarkable inhibition of proliferation, migration, and invasion in CRC cells, while the opposite results could be found in different PLOD3-overexpressed CRC cells. Stable knockdown of PLOD3 also significantly inhibited liver metastasis of CRC cells in different xenografts models, while stable overexpression of PLOD3 promotes liver metastasis and tumor progression. Further studies showed that PLOD3 facilitated the T cell activation in the tumor microenvironment and affected the TNF-α/ NF-κB pathway.

CONCLUSIONS

This study revealed the essential biological functions of PLOD3 in colon cancer progression and metastasis, suggesting that PLOD3 is a promising translational medicine target and bioengineering targeting PLOD3 overcomes CRC liver metastasis.

Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients

BACKGROUND

Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear.

OBJECTIVE

To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets.

METHODS

Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro.

RESULTS

We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway.

CONCLUSION

This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.

Identification of PLOD3 and LRRN3 as potential biomarkers for Parkinson's disease based on integrative analysis

Parkinson's disease (PD) is one of the most prevalent movement disorders and its diagnosis relies heavily on the typical clinical manifestations in the late stages. This study aims to screen and identify biomarkers of PD for earlier intervention. We performed a differential analysis of postmortem brain transcriptome studies. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify biomarkers related to Braak stage. We found 58 genes with significantly different expression in both PD brain tissue and blood samples. PD gene signature and risk score model consisting of nine genes were constructed using least absolute shrinkage and selection operator regression (LASSO) and logistic regression. PLOD3 and LRRN3 in gene signature were identified to serve as key genes as well as potential risk factors in PD. Gene function enrichment analysis and evaluation of immune cell infiltration revealed that PLOD3 was implicated in suppression of cellular metabolic function and inflammatory cell infiltration, whereas LRRN3 exhibited an inverse trend. The cellular subpopulation expression of the PLOD3 and LRRN3 has significant distributional variability. The expression of PLOD3 was more enriched in inflammatory cell subpopulations, such as microglia, whereas LRRN3 was more enriched in neurons and oligodendrocyte progenitor cells clusters (OPC). Additionally, the expression of PLOD3 and LRRN3 in Qilu cohort was verified to be consistent with previous results. Collectively, we screened and identified the functions of PLOD3 and LRRN3 based the integrated study. The combined detection of PLOD3 and LRRN3 expression in blood samples can improve the early detection of PD.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

A hybrid deep forest-based method for predicting synergistic drug combinations

Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.

Weighted gene co-expression network analysis combined with machine learning validation to identify key hub biomarkers in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies worldwide; however, the potentially possible molecular biological mechanism of CRC is still not completely comprehended. This study aimed to confirm candidate key hub genes involved in the growth and development of CRC and their connection with immune infiltration as well as the related pathways. Gene expression data were selected from the GEO dataset. Hub genes for CRC were identified on the basis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and LASSO regression. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were applied to reveal possible functions of the differential genes. Single-sample GSEA (ssGSEA) was implemented to identify the relationship between immune cells infiltration and hub genes. Two hundred and sixty-two differentially expressed genes (DEGs) were identified. Three modules were acquired based on WGCNA, and the blue module presented the highest relevance with CRC. Ten hub genes (AQP8, B3GALT5, CDH3, CEMIP, CPM, FOXQ1, PLAC8, SCNN1B, SPINK5, and SST) were acquired with LASSO analysis as underlying biomarkers for CRC. Compared with normal tissues, CRC tissues presented significantly higher numbers of CD4 T cells, CD8 T cells, B cells, natural regulatory T (Treg) cells, and monocytes. The functional enrichment analyses demonstrated that hub genes were primarily enriched in metabolic process, inflammatory-related, and immune-related response. Ten hub genes were identified to be involved in the occurrence and development of CRC and may be deemed as novel biomarkers for clinical diagnosis and treatment.

Computer classification and construction of a novel prognostic signature based on moonlighting genes in prostate cancer

Advanced prostate cancer (PRAD) patients have poor prognosis and rising morbidity despite the ongoing iteration of molecular therapeutic agents. As newly discovered proteins with several functions, Moonlighting proteins have showed an important role in tumor progression but has not been extensively investigated in PRAD. Our study aimed to identify moonlighting-related prognostic biomarkers and prospective PRAD therapy targets. 103 moonlighting genes were gathered from previous literatures. A PRAD classification and multivariate Cox prognostic signature were constructed using dataset from The Cancer Genome Atlas (TCGA). Subsequently, we tested our signature’s potential to predict biochemical failure-free survival (BFFS) using GSE21032, a prostate cancer dataset from Gene Expression Omnibus (GEO). The performance of this signature was demonstrated by Kaplan-Meier (KM), receiver operator characteristic (ROC), areas under ROC curve (AUC), and calibration curves. Additionally, immune infiltration investigation was conducted to determine the impact of these genes on immune system. This signature’s influence on drug susceptibility was examined using CellMiner’s drug database. Both training and validation cohorts demonstrated well predictive capacity of this 9-gene signature for PRAD. The 3-year AUCs for TCGA-PRAD and GSE21032 were 0.802 and 0.60 respectively. It can effectively classify patients into various biochemical recurrence risk groups. These genes were also assessed to be connected with tumor mutation burden (TMB), immune infiltration and therapy. This work created and validated a moonlighting gene signature, revealing fresh perspectives on moonlighting proteins in predicting prognosis and improving treatment of PRAD.

The Underlying Roles of Exosome-Associated PIGR in Fatty Acid Metabolism and Immune Signaling in Colorectal Cancer

The polymeric immunoglobulin receptor (PIGR), an exosome-associated glycoprotein, plays an important role in the occurrence and development of different tumors. This study aimed to investigate whether PIGR is essential for colorectal cancer (CRC). Comprehensive bioinformatics analysis and immunohistochemistry (IHC) revealed that expression of PIGR was significantly decreased in CRC patients. Upregulated PIGR displayed favorable prognostic values in CRC patients. Several algorithms, such as TISIDB and TIMER, were used to evaluate the roles of PIGR expression in the regulation of immune response in CRC. Moreover, GSEA enrichment analysis indicated the underlying role of PIGR in the regulation of fatty acid metabolism in CRC. Taken together, our findings might provide a new potential prognostic and immune-associated biomarker for CRC and supply a new destination for PIGR-related immunotherapy in clinical treatment.

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Background

Tumor-infiltrating immune cells (TIICs) are associated with chemotherapy response. This study aimed to explore the prognostic value of a TIIC-related tumor microenvironment score (TMEscore) in patients with colorectal cancer (CRC) who underwent chemotherapy and construct a TMEscore-related gene signature to determine its predictive value.

Methods

Gene profiles of patients who underwent fluoropyrimidine-based chemotherapy were collected, and their TIIC fractions were calculated and clustered. Differentially expressed genes (DEGs) between clusters were used to calculate the TMEscore. The association between the TMEscore, chemotherapy response, and survival rate was analyzed. Machine learning methods were used to identify key TMEscore-related genes, and a gene signature was constructed to verify the predictive value.

Results

Two clusters based on the TIIC fraction were identified, and the TMEscore was calculated based on the DEGs of the two clusters. The TMEscore was higher in patients who responded to chemotherapy than in those who did not, and was associated with the survival rate of patients who underwent chemotherapy. Three machine learning methods, support vector machine (SVM), decision tree (DT), and Extreme Gradient Boosting (XGBoost), identified three TMEscore-related genes (ADH1C, SLC26A2, and NANS) associated with the response to chemotherapy. A TMEscore-related gene signature was constructed, and three external cohorts validated that the gene signature could predict the response to chemotherapy. Five datasets and clinical samples showed that the expression of the three TMEscore-related genes was increased in tumor tissues compared to those in control tissues.

Conclusions

The TIIC-based TMEscore was associated with the survival of CRC patients who underwent fluoropyrimidine-based chemotherapy, and predicted the response to chemotherapy. The TMEscore-related gene signature had a better predictive value for response to chemotherapy than for survival.

A Fe2+-dependent self-inhibited state influences the druggability of human collagen lysyl hydroxylase (LH/PLOD) enzymes

Multifunctional human collagen lysyl hydroxylase (LH/PLOD) enzymes catalyze post-translational hydroxylation and subsequent glycosylation of collagens, enabling their maturation and supramolecular organization in the extracellular matrix (ECM). Recently, the overexpression of LH/PLODs in the tumor microenvironment results in abnormal accumulation of these collagen post-translational modifications, which has been correlated with increased metastatic progression of a wide variety of solid tumors. These observations make LH/PLODs excellent candidates for prospective treatment of aggressive cancers. The recent years have witnessed significant research efforts to facilitate drug discovery on LH/PLODs, including molecular structure characterizations and development of reliable high-throughput enzymatic assays. Using a combination of biochemistry and in silico studies, we characterized the dual role of Fe2+ as simultaneous cofactor and inhibitor of lysyl hydroxylase activity and studied the effect of a promiscuous Fe2+ chelating agent, 2,2’-bipyridil, broadly considered a lysyl hydroxylase inhibitor. We found that at low concentrations, 2,2’-bipyridil unexpectedly enhances the LH enzymatic activity by reducing the inhibitory effect of excess Fe2+. Together, our results show a fine balance between Fe2+-dependent enzymatic activity and Fe2+-induced self-inhibited states, highlighting exquisite differences between LH/PLODs and related Fe2+, 2-oxoglutarate dioxygenases and suggesting that conventional structure-based approaches may not be suited for successful inhibitor development. These insights address outstanding questions regarding druggability of LH/PLOD lysyl hydroxylase catalytic site and provide a solid ground for upcoming drug discovery and screening campaigns.

Multicenter phase II trial of Camrelizumab combined with Apatinib and Eribulin in heavily pretreated patients with advanced triple-negative breast cancer

In the later-line setting or for patients with PD-L1-negative tumors, immunotherapy-based regimens remain ineffective against advanced triple-negative breast cancer (TNBC). In this multicentered phase II trial (NCT04303741), 46 patients with pretreated advanced TNBC were enrolled to receive camrelizumab 200 mg (day 1), and apatinib 250 mg daily, plus eribulin 1.4 mg/m2 (day 1 and 8) on a 21-day cycle until progression, or unacceptable toxicity. Primary endpoint was objective response rate (ORR) according to RECIST 1.1. Secondary endpoints included toxicities, disease control rate (DCR), clinical benefit rate, progression-free survival (PFS), and 1-year overall survival. With a median of 3 lines of prior chemotherapy in the advanced setting, 17.4% had received PD-1/PD-L1 blockade plus chemotherapy for advanced disease. The ORR was 37.0% (17/46, 95% CI 23.2-52.5). The DCR was 87.0% (40/46, 95% CI 73.7-95.1). Median PFS was 8.1 (95% CI 4.6-10.3) months. Tertiary lymphoid structure was associated with higher ORR. Patients with lower tumor PML or PLOD3 expression had favorable ORR and PFS. PD-L1 status was not associated with ORR/PFS. Grade 3/4 treatment-related adverse events occurred in 19 (41.3%) of 46 patients. Camrelizumab plus apatinib and eribulin shows promising efficacy with a measurable safety profile in patients with heavily pretreated advanced TNBC.

PLOD Family: A Novel Biomarker for Prognosis and Personalized Treatment in Soft Tissue Sarcoma

Despite various treatment attempts, the heterogenous group of soft tissue sarcomata (STS) with more than 100 subtypes still shows poor outcomes. Therefore, effective biomarkers for prognosis prediction and personalized treatment are of high importance. The Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase (PLOD) gene family, which is related to multiple cancer entities, consists of three members which encode important enzymes for the formation of connective tissue. The relation to STS, however, has not yet been explored. In this study, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to analyze the role of PLOD1–3 in STS. It was found that an overexpression of PLOD family members correlates with poor prognosis, which might be due to an increased infiltration of immune-related cells in the tumor microenvironment. In STS, the expression of PLOD genes could be a novel biomarker for prognosis and a personalized, more aggressive treatment in these patients.