Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Integrative analyses of multi-omics data constructing tumor microenvironment and immune-related molecular prognosis model in human colorectal cancer

The increasing prevalence and incidence of colorectal cancer (CRC), particularly in young adults, underscore the imperative to comprehend its fundamental mechanisms, discover novel diagnostic and prognostic markers, and enhance therapeutic strategies. Here, we integrated multi-omics data, including gene expression, somatic mutation data and DNA methylation data, to unravel the intricacies of tumor microenvironment (TME) in CRC and search for novel prognostic markers. By calculating the immune score for each patient from the expression profile, we delineated the differential immune cell fraction, constructed an immune-related multi-omics atlas, and identified molecular characteristics. The entire colorectal dataset (n = 343) was randomly divided into training (n = 249) and testing datasets (n = 94). We screened 144 immune-related genes, 6 mutant genes, and 38 methylation probes associated with overall survival (OS). These makers were then incorporated into a 10-gene prognostic model using Lasso and Cox regression in the training dataset, and the model's performance was evaluated in an independent validation dataset. The model exhibited satisfactory results (average concordance index [C-index] = 0.77), with the average 1-year, 3-year, and 5-year AUCs being 0.79, 0.76, and 0.76 in the training dataset and 0.74, 0.80, and 0.90 in the testing dataset. Furthermore, the prognostic model demonstrated applicability in guiding chemotherapy for CRC patients and exhibited a degree of pan-cancer utility in risk stratification. In conclusion, our integrated analysis of multi-omics data revealed immune-related genetic and epigenetic characteristics of the TME. We propose an integrative prognostic model that can stratify risk and guide chemotherapy for CRC patients. The generalizability of the model in risk stratification across different cancer types was validated in Pan-Cancer cohort.

Silencing of matrix metalloprotease-12 delays the progression of castration-resistant prostate cancer by regulating autophagy and lipolysis

The complex pathogenesis of castration-resistant prostate cancer (CRPC) makes it challenging to identify effective treatment methods. Matrix metalloproteinase (MMP)-12 can degrade elastin as well as various extracellular matrix (ECM) components, which is associated with cancer progression. However, the relationship between MMP-12 and CRPC progression is poorly understood. In this study, we observed the effect of MMP-12 on the progression of CRPC and further explored its potential mechanism of action. High levels of MMP-12 were observed in patients with CRPC. We therefore developed cell co-culture and mouse models to study the function of MMP-12. Silencing MMP-12 in CRPC cells disrupted lipid utilization and autophagy marker expression via the CD36/CPT1 and P62/LC3 pathways, respectively, leading to reduced CRPC cell migration and invasion. Moreover, animal experiments confirmed that MMP-12-knockdown CRPC xenograft tumors exhibited reduced tumor growth, and the mechanisms involved the promotion of cancer cell autophagy and the inhibition of lipid catabolism. According to our results, MMP-12 played important roles in the progression of CRPC by disrupting adipocyte maturation and regulating cancer migration and invasion via the modulation of autophagy and lipid catabolism pathways.

In situ grown magnetic COF@MOF with a phosphoserine anchor for in-depth N-glycopeptide analysis in serum

A hydrophilic phosphoserine-functionalized magnetic organic framework composite (termed Fe3O4@COF@MOF-PS) was synthesized by an in situ growth strategy for effective capture of N-glycopeptides. Fe3O4@COF@MOF-PS exhibited high sensitivity (0.2 fmol μL-1), outstanding exclusion of size capability (1 : 10 000), good selectivity (1 : 2000), and reusability (at least 10 times). It also exhibited remarkable performance in the N-glycopeptide analysis in complex biological samples. Via nano-LC-MS/MS analysis, a total of 223 N-glycopeptides with 161 glycosylation sites assigned to 91 glycoproteins and 331 N-glycopeptides with 243 glycosylation sites assigned to 134 glycoproteins were identified in sera from cervical cancer patients and normal controls, respectively. Biological processes and molecular functional analyses indicate that the captured glycoproteins are of significant relevance to cervical cancer, for example, gene coverage or expression of cell adhesion and extracellular matrix structural constituents. Thus, Fe3O4@COF@MOF-PS not only efficiently captures N-glycopeptides, but also has the possibility of screening potential disease markers and elucidating the process of cervical cancer development.

The emerging double-edged sword role of Sirtuins in the gastric inflammation-carcinoma sequence revealed by bulk and single-cell transcriptomes

Histone modification and the inflammation-carcinoma sequence (ICS) have been acknowledgedly implicated in gastric carcinogenesis. However, the extremum expression of some histone modification genes (HMGs) in intestinal metaplasia (IM) rather than GC obscures the roles of HMGs in ICS. In this study, we assumed an explanation that the roles of HMGs in ICS were stage specific. Bulk RNA-seq on endoscopy biopsy samples from a total of 50 patients was accompanied by reanalysis of a set of published single-cell transcriptomes, which cross-sectionally profiled the transcriptomic features of chronic superficial gastritis (SG), atrophy gastritis (AG), IM, and early gastric cancer (GC). Differential analysis observed significantly peaked expression of SIRT6 and SIRT7 at IM. Weighted correlation network analysis on bulk transcriptome recognized significant correlations between SIRT1/6 and IM. The single-cell atlas identified one subgroup of B cells expressing high level of TFF1 (TFF1^hi naive B cell) that theoretically played important roles in defending microbial infection, while SIRT6 displayed a positive correlation with TFF1^low naive B cells. Moreover, gene set enrichment analysis at different lesions (SG-AG, AG-IM, and IM-GC) highlighted that gene sets contributing to IM, e.g., Brush Border, were largely enriched from co-expressing genes of Sirtuins (SIRTs) in AG-IM. Surveys of the genes negatively correlated with SIRT6 in public databases considered SIRT6 as tumor suppressors, which was confirmed by the cell proliferation and migration assays after transient transfection of SIRT6 overexpression vector into AGS cells. All the above observations were then confirmed by serial section-based immunohistochemistry against Ki-67, MUC2, MUC5AC, p53, and SIRT6 on the endoscopic submucosal dissection tissue. By contrast, the expression of the other HMGs varied even opposite within same family. Taken together, this study preliminarily demonstrated the two-edged sword role of SIRTs in ICS and, by extension, showed that the roles of HMGs in ICS were probably stage specific. Our study may provide new insights into and attract attention on gastric prevention and therapy targeting HMGs.

Identification of a six-gene prognostic signature for bladder cancer associated macrophage

As major components of the tumor microenvironment (TME), tumor-associated macrophages (TAMs) play an exceedingly complicated role in tumor progression and tumorigenesis. However, few studies have reported the specific TAM gene signature in bladder cancer. Herein, this study focused on developing a TAM-related prognostic model in bladder cancer patients based on The Cancer Genome Atlas (TCGA) data. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key genes related to TAM (M2 macrophage). Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed the functional categories of the key genes. Simultaneously, we used the Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regressions to establish a TMA-related prognostic model containing six key genes: TBXAS1, GYPC, HPGDS, GAB3, ADORA3, and FOLR2. Subsequently, single-cell sequencing data downloaded from Gene Expression Omnibus (GEO) suggested that the six genes in the prognostic model were expressed in TAM specifically and may be involved in TAM polarization. In summary, our research uncovered six-TAM related genes that may have an effect on risk stratification in bladder cancer patients and could be regarded as potential TAM-related biomarkers.

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using "ConsensusClusterPlus" based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients.

CXCR4 knockdown enhances sensitivity of paclitaxel via the PI3K/Akt/mTOR pathway in ovarian carcinoma

Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy. EOC control remains difficult, and EOC patients show poor prognosis regarding metastasis and chemotherapy resistance. The aim of this study was to estimate the effect of CXCR4 knockdown-mediated reduction of cancer stem cells (CSCs) and epithelial-mesenchymal transition (EMT) stemness and enhancement of chemotherapy sensitivity in EOC. Mechanisms contributing to these effects were also explored. Our data showed distinct contribution of CXCR4 overexpression by dependent PI3K/Akt/mTOR signaling pathway in EOC development. CXCR4 knockdown resulted in a reduction in CSCs and EMT formation and enhancement of chemotherapy sensitivity in tumor cells, which was further advanced by blocking CXCR4-PI3K/Akt/mTOR signaling. This study also documented the critical role of silencing CXCR4 in sensitizing ovarian CSCs to chemotherapy. Thus, targeting CXCR4 to suppress EOC progression, specifically in combination with paclitaxel (PTX) treatment, may have clinical application value.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

Construction and Validation of a Novel Immunosignature for Overall Survival in Uveal Melanoma

Objectives

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients.

Methods

Single-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model.

Results

Higher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p < 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy.

Conclusion

We developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.