A lymph node metastasis‐related protein‐coding genes combining with long noncoding RNA signature for breast cancer survival prediction

New Function Annotation of PROSER2 in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis due to the absence of diagnostic markers and molecular targets. Here, we took an unconventional approach to identify new molecular targets for pancreatic cancer. We chose uncharacterized protein evidence level 1 without function annotation from extensive proteomic research on pancreatic cancer and focused on proline and serine-rich 2 (PROSER2), which ranked high in the cell membrane and cytoplasm. In our study using cell lines and patient-derived orthotopic xenograft cells, PROSER2 exhibited a higher expression in cells derived from primary tumors than in those from metastatic tissues. PROSER2 was localized in the cell membrane and cytosol by immunocytochemistry. PROSER2 overexpression significantly reduced the metastatic ability of cancer cells, whereas its suppression had the opposite effect. Proteomic analysis revealed that PROSER2 interacts with STK25 and PDCD10, and their binding was confirmed by immunoprecipitation and immunocytochemistry. STK25 knockdown enhanced metastasis by decreasing p-AMPK levels, whereas PROSER2-overexpressing cells increased the level of p-AMPK, indicating that PROSER2 suppresses invasion via the AMPK pathway by interacting with STK25. This is the first demonstration of the novel role of PROSER2 in antagonizing tumor progression via the STK25-AMPK pathway in PDAC. LC-MS/MS data are available at MassIVE (MSV000092953) and ProteomeXchange (PXD045646).

The current advances of lncRNAs in breast cancer immunobiology research

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related death in women worldwide. Breast cancer development and progression are mainly associated with tumor-intrinsic alterations in diverse genes and signaling pathways and with tumor-extrinsic dysregulations linked to the tumor immune microenvironment. Significantly, abnormal expression of lncRNAs affects the tumor immune microenvironment characteristics and modulates the behavior of different cancer types, including breast cancer. In this review, we provide the current advances about the role of lncRNAs as tumor-intrinsic and tumor-extrinsic modulators of the antitumoral immune response and the immune microenvironment in breast cancer, as well as lncRNAs which are potential biomarkers of tumor immune microenvironment and clinicopathological characteristics in patients, suggesting that lncRNAs are potential targets for immunotherapy in breast cancer.

Development and validation of a novel anoikis-related gene signature for predicting prognosis in ovarian cancer

Anoikis plays a critical role in variable cancer types. However, studies that focus on the prognostic values of anoikis-related genes (ANRGs) in OV are scarce. Cohorts with transcriptome data and corresponding clinicopathologic data of OV patients were collected and consolidated from public databases. Multiple bioinformatics approaches were used to screen key genes from 446 anoikis-related genes, including Cox regression analysis, random survival forest analysis, and Kaplan-Meier analysis of best combinations. A five-gene signature was constructed in the discovery cohort (TCGA) and validated in four validation cohorts (GEO). Risk score of the signature stratified patients into high-risk (HRisk) and low-risk (LRisk) subgroups. Patients in the HRisk group were associated with worse OS than those in the LRisk group in both the TCGA cohort (p<0.0001, HR=2.718, 95%CI:1.872-3.947) and the four GEO cohorts (p<0.05). Multivariate Cox regression analyses confirmed that the risk score served as an independent prognostic factor in both cohorts. The signature's predictive capacity was further demonstrated by the nomogram analysis. Pathway enrichment analysis revealed that immunosuppressive and malignant progression-related pathways were enriched in the HRisk group, including TGF-β, WNT and ECM pathways. The LRisk group was characterized by immune-active signaling pathways (interferon-gamma, T cell activation, etc.) and higher proportions of anti-tumor immune cells (NK, M1, etc.) while HRisk patients were associated with higher stromal scores and less TCR richness. In conclusion, the signature reveals a close relationship between the anoikis and prognosis and may provide a potential therapeutic target for OV patients.

PROSER2 is a poor prognostic biomarker for patients with osteosarcoma and promotes proliferation, migration and invasion of osteosarcoma cells

Proline- and serine-rich 2 (PROSER2) is encoded by the 47th open reading frame on human chromosome 10. Bioinformatic analysis has shown PROSER2 was significantly correlated with prognostic outcome of osteosarcoma patients. Its role in the progression and metastasis of human osteosarcoma has not been elucidated until now. Bioinformatics analysis was performed on 101 patients with osteosarcoma from The Cancer Genome Atlas database. High levels of PROSER2 were associated with a poor prognosis in patients with osteosarcoma. PROSER2 expression was significantly upregulated in clinical specimens from patients with osteosarcoma and osteosarcoma cell lines. MTT assay was performed to test the cell viability and Transwell assay was used to test the migration and invasion of MG63 cells. PROSER2 knockdown inhibited the viability, migration and invasion of MG63 cells. Gene Set Enrichment Analysis and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes analysis showed that the differentially expressed genes were primarily involved in 'calcium signaling pathway' and 'Wnt signaling' in patients with osteosarcoma and high PROSER2 expression. Western blotting analysis revealed that PROSER2 regulated migration and invasion of osteosarcoma via the Wnt/nuclear factor of activated T-cells (NFAT)c1 signaling pathway. In conclusion, PROSER2 promoted the proliferation, migration and invasion of osteosarcoma cells via the Wnt/Ca²⁺/NFATc1 signaling pathway by increasing nuclear localization of NFATc1.

Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer

Background

Increasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.

Methods

Multiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package.

Results

Three different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.

Conclusion

This work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

Development and validation of a novel ferroptosis-related gene signature for predicting prognosis and immune microenvironment in head and neck squamous cell carcinoma

Ferroptosis plays an important role across variable cancer types. However, few studies have focused on the prognostic patterns of ferroptosis-related genes in HNSCC. Cohorts with mRNA expression profiles, as well as corresponding clinical data of HNSCC patients from published studies, were collected and consolidated from public databases. We performed random survival forest analysis, Kaplan-Meier (KM) analysis of best combinations, and Cox regression analysis on 231 ferroptosis-related genes to construct a gene signature in the discovery cohort (TCGA), and later validated it in the validation cohort (GEO). The 7-gene signature was constructed to stratify patients into two groups according to their level of risk. Poorer overall survival (OS) was detected in the high risk (HRisk) group than in the low risk (LRisk) group in both the TCGA cohort (P < 0.0001, HR = 1.71, 95%CI:1.41-2.07) and the GEO cohort (P < 0.001, HR = 1.68, 95%CI:1.32-2.13). The risk score was identified as an independent predictive factor of OS in multivariate Cox regression analyses (HR > 1, P < 0.0001) in both cohorts. The signature's predictive capacity was proven by the time-dependent receiver operating characteristic (ROC) curve analysis and nomogram analysis. Functional enrichment analysis revealed that immunosuppressive pathways such as matrix extracellular space, and (transforming growth factor-β)TGF-β were enriched. The HRisk group was strongly associated with upregulation of both cancer-related pathways and stromal scores, while higher proportions of anti-tumor immune cells and immune signatures were enriched in the LRisk group. In conclusion, the signature based on 7 ferroptosis-related genes could be applicable for predicting the prognosis of HNSCC, indicating that ferroptosis may be a potential therapeutic target for HNSCC.

Construction of a Promising Tumor-Infiltrating CD8+ T Cells Gene Signature to Improve Prediction of the Prognosis and Immune Response of Uveal Melanoma

Background

CD8+ T cells work as a key effector of adaptive immunity and are closely associated with immune response for killing tumor cells. It is crucial to understand the role of tumor-infiltrating CD8+ T cells in uveal melanoma (UM) to predict the prognosis and response to immunotherapy.

Materials and Methods

Single-cell transcriptomes of UM with immune-related genes were combined to screen the CD8+ T-cell-associated immune-related genes (CDIRGs) for subsequent analysis. Next, a prognostic gene signature referred to tumor-infiltrating CD8+ T cells was constructed and validated in several UM bulk RNA sequencing datasets. The risk score of UM patients was calculated and classified into high- or low-risk subgroup. The prognostic value of risk score was estimated by using multivariate Cox analysis and Kaplan–Meier survival analysis. Moreover, the potential ability of gene signature for predicting immunotherapy response was further explored.

Results

In total, 202 CDIRGs were screened out from the single-cell RNA sequencing of GSE139829. Next, a gene signature containing three CDIRGs (IFNGR1, ANXA6, and TANK) was identified, which was considered as an independent prognostic indicator to robustly predict overall survival (OS) and metastasis-free survival (MFS) of UM. In addition, the UM patients were classified into high- and low-risk subgroups with different clinical characteristics, distinct CD8+ T-cell immune infiltration, and immunotherapy response. Gene set enrichment analysis (GSEA) showed that immune pathways such as allograft rejection, inflammatory response, interferon alpha and gamma response, and antigen processing and presentation were all positively activated in low-risk phenotype.

Conclusion

Our work gives an inspiration to explain the limited response for the current immune checkpoint inhibitors to UM. Besides, we constructed a novel gene signature to predict prognosis and immunotherapy responses, which may be regarded as a promising therapeutic target.

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform

Background

The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mechanisms of cancers. Most of the conventional machine learning methods involved a gene filtering step, in which tens of thousands of genes were firstly filtered based on the gene expression levels by a statistical method with an arbitrary cutoff. Although gene filtering procedure helps to reduce the feature dimension and avoid overfitting, there is a risk that some pathogenic genes important to the disease will be ignored.

Results

In this study, we proposed a novel deep learning approach by combining a convolutional neural network with stationary wavelet transform (SWT-CNN) for stratifying cancer patients and predicting their clinical outcomes without gene filtering based on tumor genomic profiles. The proposed SWT-CNN overperformed the state-of-art algorithms, including support vector machine (SVM) and logistic regression (LR), and produced comparable prediction performance to random forest (RF). Furthermore, for all the cancer types, we firstly proposed a method to weight the genes with the scores, which took advantage of the representative features in the hidden layer of convolutional neural network, and then selected the prognostic genes for the Cox proportional-hazards regression. The results showed that risk stratifications can be effectively improved by using the identified prognostic genes as feature, indicating that the representative features generated by SWT-CNN can well correlate the genes with prognostic risk in cancers and be helpful for selecting the prognostic gene signatures.

Conclusions

Our results indicated that gene expression-based SWT-CNN model can be an excellent tool for stratifying the prognostic risk for cancer patients. In addition, the representative features of SWT-CNN were validated to be useful for evaluating the importance of the genes in the risk stratification and can be further used to identify the prognostic gene signatures.

Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

Background

Myocarditis is an inflammatory myocardial disease, which may lead to heart failure and sudden death. Despite extensive research into the pathogenesis of myocarditis, effective treatments for this condition remain elusive. This study aimed to explore the potential pathogenesis and hub genes for viral myocarditis.

Methods

A weighted gene co-expression network analysis (WGCNA) was performed based on the gene expression profiles derived from mouse models at different stages of viral myocarditis (GSE35182). Functional annotation was executed within the key modules. Potential hub genes were predicted based on the intramodular connectivity (IC). Finally, potential microRNAs that regulate gene expression were predicted by miRNet analysis.

Results

Three gene co-expression modules showed the strongest correlation with the acute or chronic disease stage. A significant positive correlation was detected between the acute disease stage and the turquoise module, the genes of which were mainly enriched in antiviral response and immune-inflammatory activation. Furthermore, a significant positive correlation and a negative correlation were identified between the chronic disease stage and the brown and yellow modules, respectively. These modules were mainly associated with the cytoskeleton, phosphorylation, cellular catabolic process, and autophagy. Subsequently, we predicted the underlying hub genes and microRNAs in the three modules.

Conclusions

This study revealed the main biological processes in different stages of viral myocarditis and predicted hub genes in both the acute and chronic disease stages. Our results may be helpful for developing new therapeutic targets for viral myocarditis in future research.

Identification of a 9-gene prognostic signature for breast cancer

Breast cancer (BRCA) is the most common cancer among women and is the second leading cause of cancer death in women. In this study, we developed a 9‐gene prognostic signature to predict the prognosis of patients with BRCA. GSE20685, GSE42568, GSE20711, and GSE88770 were used as training sets. The Kaplan–Meier plot was constructed to assess survival differences and log‐rank test was performed to evaluate the statistical significance. The overall survival (OS) of patients in the low‐risk group was significantly higher than that in the high‐risk group. ROC analysis indicated that this 9‐gene signature shows good diagnostic efficiency both in OS and disease‐free survival (DFS). The 9‐gene signature was further validated through GSE16446, GSE7390, and TCGA‐BRCA datasets. We also established a nomogram that integrates clinicopathological features and 9‐gene signature. The analysis of the calibration plot showed that the nomogram has good prognostic performance. More convincingly, real‐time reverse transcription‐polymerase chain reaction (RT‐PCR) results indicated that the protective prognostic factors in BRCA patients were downregulated, whereas the dangerous prognostic factors were upregulated. The innovation of this article is not only constructing a prognostic gene signature, but also combining with clinical information to further establish a nomogram to better predict the survival probability of patients. It is worth mentioning that this signature also does not depend on other clinical factors or variables.

Bromodomain 4 is a potent prognostic marker associated with immune cell infiltration in breast cancer

Bromodomain 4 (BRD4), a member of the bromodomain and extra-terminal domain protein family, has become a promising epigenetic target in cancer and inflammatory diseases; however, the detailed biological role of BRD4 in breast cancer (BRCA) remains undetermined. We analysed the BRD4 expression levels using the Oncomine and TIMER databases and evaluated the clinical impact of BRD4 on BRCA prognosis using Kaplan-Meier plot and PrognoScan. The correlation between BRD4 and tumour-infiltrating immune cells was investigated using TIMER. Furthermore, the correlation between BRD4 expression levels was also analysed using TIMER in addition to the GEPIA database for immune cell gene markers. BRD4 expression was significantly higher in BRCA tissues than in normal tissues, which was significantly correlated with poor overall survival (OS). Specifically, high BRD4 expression was correlated with worse OS and progression-free survival in patients with BRCA. In addition, BRD4 expression was correlated with levels of infiltrating monocytes (CSF1R, cor = 0.204, P = 9.19e-12), tumour-associated macrophages (CD68, cor = 0.129, P = 1.81e-05), M1/M2 macrophages and different effector T cells (including Th1/Th2/Treg) in BRCA. These findings suggest that BRD4 could be used as a prognostic biomarker for determining prognosis and immune cell infiltration levels in BRCA.

An immune-related prognostic signature for predicting breast cancer recurrence

Breast cancer (BC) is the most common cancer among women worldwide and is the second leading cause of cancer‐related deaths in women. Increasing evidence has validated the vital role of the immune system in BC development and recurrence. In this study, we identified an immune‐related prognostic signature of BRCA that could help delineate risk scores of poor outcome for each patient. This prognostic signature comprised information on five danger genes—TSLP, BIRC5, S100B, MDK, and S100P—and three protect genes RARRES3, BLNK, and ACO1. Kaplan‐Meier survival curve showed that patients classified as low‐risk according to optimum cut‐off risk score had better prognosis than those identified within the high‐risk group. ROC analysis indicated that the identified prognostic signature had excellent diagnostic efficiency for predicting 3‐ and 5‐years relapse‐free survival (RFS). Multivariate Cox regression analysis proved that the prognostic signature is independent of other clinical parameters. Stratification analysis demonstrated that the prognostic signature can be used to predict the RFS of BC patients within the same clinical subgroup. We also developed a nomogram to predict the RFS of patients. The calibration plots exhibited outstanding performance. The validation sets (GSE21653, GSE20711, and GSE88770) were used to external validation. More convincingly, the real time RT‐PCR results of clinical samples demonstrated that danger genes were significantly upregulated in BC samples, whereas protect genes were downregulated. In conclusion, we developed and validated an immune‐related prognostic signature, which exhibited excellent diagnostic efficiency in predicting the recurrence of BC, and will help to make personalized treatment decisions for patients at different risk score.

Clinical significance of ring finger protein 2 high expression in skin squamous cell carcinoma

Although ring finger protein 2 (RNF2) serves an important role in the occurrence, development and regulation of various types of cancer, RNF2 expression in skin squamous cell carcinoma (SCC) remains unknown. The aim of the present study was to investigate the role of RNF2 expression in SCC and adjacent tissues from patients. The protein and gene expression levels of RNF2 in SCC and adjacent tissues were detected by immunohistochemistry (IHC), western blot analysis and semi-quantitative reverse transcription (RT) PCR. Single factor analysis was used to study the association between RNF2 expression level and the clinicopathological characteristics of patients with SCC. Multifactor Cox survival analysis was used to examine the association between RNF2 expression and the overall survival rate of postoperative patients with SCC. The results from IHC staining demonstrated that the positive expression rate of RNF2 was 84.68% (210/248) and 56.05% (139/248) in SCC and in adjacent tissues, respectively. Furthermore, results from western blot analysis demonstrated that RNF2 protein expression in SCC tissues was significantly higher compared with that in the adjacent tissues (P<0.05). The positive rate of RNF2 mRNA in SCC was 81.05% (201/248), which was significantly higher compared with that in the adjacent tissues 54.44% (135/248; P<0.05). Furthermore, RNF2 protein and gene expression levels were associated with tumor diameter, tumor stage, tumor metastasis and the degree of tumor differentiation in patients with SCC. Patients exhibiting higher RNF2 protein expression in SCC tissues had a significantly shorter disease-specific survival rate compared with patients with low RNF2 expression. In addition, RNF2 protein expression, tumor diameter, tumors site and tumor stage were independent factors affecting the overall survival rate of postoperative patients. High protein and gene expression levels of RNF2 in SCC tissues may be associated with the occurrence and development of SCC and prognosis of patients. The results form this study may serve the development of novel therapeutic options and diagnostic strategies for patients with SCC.

BTN3A2 serves as a prognostic marker and favors immune infiltration in triple-negative breast cancer

Immune infiltration is reported to be highly associated with tumor progress. Since butyrophilin subfamily 3 member A2 (BTN3A2) serves as a crucial mediator in immune activation, we aimed to investigate the correlation of BTN3A2 in immune infiltration and tumor prognosis via extensive-cancer analysis. The levels of BTN3A2 expression in extensive cancers were analyzed with Oncomine and TIMER databases. Univariate cox and multivariate cox regression analyses were conducted to assess the associations of BTN3A2 to prognosis of various cancers. The correlations of BTN3A2 with immune infiltration were assessed by TIMER database. It suggested that BTN3A2 was a potential prognosis signature for breast cancer (BRCA) and ovarian cancer (OV). However, immune infiltrations were highly correlated with BTN3A2 in triple-negative breast cancer (TNBC), compared with OV and other subtypes of BRCA. Multivariate cox regression analysis revealed that BTN3A2 was an independently prognostic signature of TNBC, as well as weighted correlation network analysis suggested BTN3A2 was only correlated with TNBC, rather than other subtypes of BRCA. Immune cell subtypes correlation analysis showed that BTN3A2 was highly correlated with general T, CD8+ T, T helper type 1, exhausted T cells, and dendritic cells in TNBC. And the coexpression geneset of BTN3A2 was mainly involved in T-cell receptor interaction and the nuclear factor-κB (NF-κB) signaling pathway. Collectively, BTN3A2 that was positively associated with better prognosis could be served as a special diagnostic and independently prognostic marker for TNBC by regulating the T-cell receptor interaction and NF-κB signaling pathways.