Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis

Different Molecular Phenotypes of Progression in BRAF- and RAS-Like Papillary Thyroid Carcinoma

BACKGRUOUND

Papillary thyroid carcinoma (PTC) can be classified into two distinct molecular subtypes, BRAF-like (BL) and RASlike (RL). However, the molecular characteristics of each subtype according to clinicopathological factors have not yet been determined. We aimed to investigate the gene signatures and tumor microenvironment according to clinicopathological factors, and to identify the mechanism of progression in BL-PTCs and RL-PTCs.

METHODS

We analyzed RNA sequencing data and corresponding clinicopathological information of 503 patients with PTC from The Cancer Genome Atlas database. We performed differentially expressed gene (DEG), Gene Ontology, and molecular pathway enrichment analyses according to clinicopathological factors in each molecular subtype. EcoTyper and CIBERSORTx were used to deconvolve the tumor cell types and their surrounding microenvironment.

RESULTS

Even for the same clinicopathological factors, overlapping DEGs between the two molecular subtypes were uncommon, indicating that BL-PTCs and RL-PTCs have different progression mechanisms. Genes related to the extracellular matrix were commonly upregulated in BL-PTCs with aggressive clinicopathological factors, such as old age (≥55 years), presence of extrathyroidal extension, lymph node metastasis, advanced tumor-node-metastasis (TNM) stage, and high metastasis-age-completeness of resection- invasion-size (MACIS) scores (≥6). Furthermore, in the deconvolution analysis of tumor microenvironment, cancer-associated fibroblasts were significantly enriched. In contrast, in RL-PTCs, downregulation of immune response and immunoglobulin-related genes was significantly associated with aggressive characteristics, even after adjusting for thyroiditis status.

CONCLUSION

The molecular phenotypes of cancer progression differed between BL-PTC and RL-PTC. In particular, extracellular matrix and cancer-associated fibroblasts, which constitute the tumor microenvironment, would play an important role in the progression of BL-PTC that accounts for the majority of advanced PTCs.

Single-cell RNA sequencing reveals tumor immune microenvironment in human hypopharygeal squamous cell carcinoma and lymphatic metastasis

Background

Human hypopharygeal squamous cell carcinoma (HSCC) is a common head and neck cancer with a poor prognosis in advanced stages. The occurrence and development of tumor is the result of mutual influence and co-evolution between tumor cells and tumor microenvironment (TME). Tumor immune microenvironment (TIME) refers to the immune microenvironment surrounding tumor cells. Studying TIME in HSCC could provide new targets and therapeutic strategies for HSCC.

Methods

We performed single-cell RNA sequencing (scRNA-seq) and analysis of hypopharyngeal carcinoma, paracancerous, and lymphoid tissues from five HSCC patients. Subdivide of B cells, T cells, macrophages cells, and monocytes and their distribution in three kinds of tissues as well as marker genes were analyzed. Different genes of IGHG1 plasma cells and SPP1+ macrophages between HSCC tissues, adjacent normal tissues and lymphatic tissues were analyzed. Additionally, we studied proliferating lymphocytes, T cells exhaustion, and T cell receptor (TCR) repertoire in three kinds of tissues.

Results

Transcriptome profiles of 132,869 single cells were obtained and grouped into seven cell clusters, including epithelial cells, lymphocytes, mononuclear phagocytics system (MPs), fibroblasts, endothelial cells (ECs), plasmacytoid dendritic cells (pDCs), and mast cells. Tumor metastasis occurred in three lymphoid tissues. Four distinct populations were identified from lymphocytes, including B cells, plasma cells, T cells and proliferating lymphocytes. We found IGHA1 and IGHG1 specific plasma cells significantly overexpressed in HSCC tissues compared with normal hypopharygeal tissues and lymphatic tissues. Five distinct populations from MPs were identified, including macrophages, monocytes, mature dendritic cells (DCs), Type 1 conventional dendritic cells (cDC1) and Type 2 conventional dendritic cells (cDC2). SPP1+ macrophages were significantly overexpressed in HSCC tissues and lymphatic tissues compared with normal hypopharygeal tissues, which are thought to be M2-type macrophages. Exhaustion of CD8+ Teff cells occurred in HSCC tissues. At last, we verified that IgA and IgG1 protein expression levels were significantly up-regulated in HSCC tissues compared to adjacent normal tissues.

Conclusion

Overall, this study revealed TIME in HSCC and lymphatic metastasis, and provided potential therapeutic targets for HSCC.

Establishment and validation of an aging-related risk signature associated with prognosis and tumor immune microenvironment in breast cancer

BACKGROUND

Breast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated.

MATERIALS AND METHODS

The expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real-time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes.

RESULTS

An 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR.

CONCLUSIONS

In summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.

Identification of the Antigens Recognised by Colorectal Cancer Patients Using Sera from Patients Who Exhibit a Crohn's-like Lymphoid Reaction

A Crohn’s-like lymphoid reaction (CLR) is observed in about 15% of colorectal cancer (CRC) patients and is associated with favourable outcomes. To identify the immune targets recognised by CRC CLR patient sera, we immunoscreened a testes cDNA library with sera from three patients. Immunoscreening of the 18 antigens identified by SEREX with sera from normal donors showed that only the heavy chain of IgG3 (IGHG3) and a novel antigen we named UOB-COL-7, were solely recognised by sera from CRC CLR patients. ELISA showed an elevation in IgG3 levels in patients with CRC (p = 0.01). To extend our studies we analysed the expression of our SEREX-identified antigens using the RNA-sequencing dataset (GSE5206). We found that the transcript levels of multiple IGHG probesets were highly significant (p < 0.001) in their association with clinical features of CRC while above median levels of DAPK1 (p = 0.005) and below median levels of GTF2H5 (p = 0.004) and SH3RF2 (p = 0.02) were associated with improved overall survival. Our findings demonstrate the potential of SEREX-identified CRC CLR antigens to act as biomarkers for CRC and provide a rationale for their further characterization and validation.

Identification of early biomarkers in saliva in genetically engineered mouse model C(3)1-TAg of breast cancer

Breast cancer is one of leading causes of death worldwide in the female population. Deaths from breast cancer could be reduced significantly through earlier and more efficient detection of the disease. Saliva, an oral fluid that contains an abundance of protein biomarkers, has been recognized as a promising diagnostic biofluid that is easy to isolate through non-invasive techniques. Assays on saliva can be performed rapidly and are cost-effective. Therefore, our work aimed to identify salivary biomarkers present in the initial stages of breast cancer, where cell alterations are not yet detectable by histopathological analysis. Using state-of-the-art techniques, we employed a transgenic mouse model of mammary cancer to identify molecular changes in precancerous stage breast cancer through protein analysis in saliva. Through corroborative molecular approaches, we established that proteins related to metabolic changes, inflammatory process and cell matrix degradation are detected in saliva at the onset of tumor development. Our work demonstrated that salivary protein profiles can be used to identify cellular changes associated with precancerous stage breast cancer through non-invasive means even prior to biopsy-evident disease.

Preliminary Investigation of the Biomarkers of Acute Renal Transplant Rejection Using Integrated Proteomics Studies, Gene Expression Omnibus Datasets, and RNA Sequencing

A kidney transplant is often the best treatment for end-stage renal disease. Although immunosuppressive therapy sharply reduces the occurrence of acute allograft rejection (AR), it remains the main cause of allograft dysfunction. We aimed to identify effective biomarkers for AR instead of invasive kidney transplant biopsy. We integrated the results of several proteomics studies related to AR and utilized public data sources. Gene ontology (GO) and pathway analyses were used to identify important biological processes and pathways. The performance of the identified proteins was validated using several public gene expression omnibus (GEO) datasets. Samples that performed well were selected for further validation through RNA sequencing of peripheral blood mononuclear cells of patients with AR (n = 16) and non-rejection (n = 19) from our medical center. A total of 25 differentially expressed proteins (DEPs) overlapped in proteomic studies of urine and blood samples. GO analysis showed that the DEPs were mainly involved in the immune system and blood coagulation. Pathway analysis showed that the complement and coagulation cascade pathways were well enriched. We found that immunoglobulin heavy constant alpha 1 (IGHA1) and immunoglobulin κ constant (IGKC) showed good performance in distinguishing AR from non-rejection groups validated with several GEO datasets. Through RNA sequencing, the combination of IGHA1, IGKC, glomerular filtration rate, and donor age showed good performance in the diagnosis of AR with ROC AUC 91.4% (95% CI: 82–100%). Our findings may contribute to the discovery of potential biomarkers for AR monitoring.

A Novel IGLC2 Gene Linked With Prognosis of Triple-Negative Breast Cancer

Background

Immunoglobulin-related genes are associated with the favorable prognosis of triple-negative breast cancer (TNBC) patients. We aimed to analyze the function and prognostic value of immunoglobulin lambda constant 2 (IGLC2) in TNBC patients.

Methods

We knocked down the gene expression of IGLC2 (IGLC2-KD) in MDA-MB-231 cells to evaluate the proliferation, migration, and invasion of tumors via 3-(4,5-Dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, wound healing, and transwell cell migration assay respectively. Relapse-free survival (RFS) and distant metastasis-free survival (DMFS) analyses were conducted using the KM plotter online tool. The GSE76275 data set was used to analyze the association of IGLC2 and clinical characteristics. A pathway enrichment analysis was conducted using the next-generation sequencing data of wild-type and IGLC2-KD MDA-MB-231 cells.

Results

The low gene expression of IGLC2 was related to unfavorable RFS, DMFS. The high expression of IGLC2 was exhibited in the basal-like immune-activated (BLIA) TNBC molecular subtype, which was immune-activated and showed excellent response to immune therapy. IGLC2 was positively correlated with programmed death-ligand 1 (PD-L1) as shown by Spearman correlation (r = 0.25, p < 0.0001). IGLC2 had a strong prognostic effect on lymph node-negative TNBC (RFS range: 0.31, q value= 8.2e-05; DMFS = 0.16, q value = 8.2e-05) but had no significance on lymph node-positive ones. The shRNA-mediated silencing of IGLC2 increased the proliferation, migration, and invasion of MDA-MB-231 cells. The results of pathway enrichment analysis showed that IGLC2 is related to the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix–receptor interaction. We confirmed that MDA-MB-231 tumor cells expressed IGLC2, subverting the traditional finding of generation by immune cells.

Conclusions

IGLC2 linked with the proliferation, migration, and invasion of MDA-MB-231 cells. A high expression of IGLC2 was related to favorable prognosis for TNBC patients. IGLC2 may serve as a biomarker for the identification of TNBC patients who can benefit the most from immune checkpoint blockade treatment.

Construction and Validation of a Prognostic Risk Model for Triple-Negative Breast Cancer Based on Autophagy-Related Genes

Background

Autophagy plays an important role in triple-negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.

Methods

A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed autophagy-related genes (DE-ARGs) between normal samples and TNBC samples were determined by the DESeq2 package. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed. According to the LASSO regression results based on univariate Cox, we identified a prognostic signature for overall survival (OS), which was further validated by using the Gene Expression Omnibus (GEO) cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of an ARG in our model for TNBC cell survival and metastasis.

Results

There are 43 DE-ARGs identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3 was established based on univariate Cox regression and LASSO regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan–Meier curves and receiver operating characteristic (ROC) curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as an independent predictor. Subsequently, a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of the ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells.

Conclusion

Based on six ARGs (CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3), we developed a risk prediction model that can help clinical doctors effectively predict the survival status of TNBC patients. Our data suggested that EIF4EBP1 might promote the proliferation and migration in TNBC cell lines. These findings provided a novel insight into the vital role of the autophagy-related genes in TNBC and may provide new therapeutic targets for TNBC.

Identification of condition-specific biomarker systems in uterine cancer

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

A prospective study revealing the role of an immune-related eRNA, WAKMAR2, in breast cancer

Enhancer RNAs (eRNAs) are a subclass of non-coding RNAs that are generated during the transcription of enhancer regions and play an important role in tumourigenesis. In this study, we focused on the crucial eRNAs that participate in immune responses in invasive breast cancer (IBC). We first used The Cancer Genome Atlas and Human enhancer RNA Atlas to screen for tissue-specific eRNAs and their target genes. Through Pearson correlation analysis with immune genes, the eRNA WAKMAR2 was identified as a key candidate involved in IBC. Our further research suggested that WAKMAR2 is crucial in regulating the tumour microenvironment and may function by regulating immune-related genes, including IL27RA, RAC2, FABP7, IGLV1-51, IGHA1, and IGHD. Quantitative reverse transcription-polymerase chain reaction was used to detect the expression of WAKMAR2 in IBC and normal tissues, and the effect of WAKMAR2 on the regulation of downstream genes in MB-231 and MCF7 cells was studied in vitro. WAKMAR2 was found to be highly involved in tumour immunity and was downregulated in IBC tissues. Furthermore, the expression of WAKMAR2 and its target genes was observed at the pan-cancer level. This study provides evidence to suggest new potential targets for the treatment of breast cancer.

Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0-81.4% and 74.6-78% respectively (rfm, ACC 63.2-65.5%, AUC 61.9-74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10^-8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

Gene Expression-Based Prediction of Neoadjuvant Chemotherapy Response in Early Breast Cancer: Results of the Prospective Multicenter EXPRESSION Trial

PURPOSE

Expression-based classifiers to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) are not routinely used in the clinic. We aimed to build and validate a classifier for pCR after NACT.

PATIENTS AND METHODS

We performed a prospective multicenter study (EXPRESSION) including 114 patients treated with anthracycline/taxane-based NACT. Pretreatment core needle biopsies from 91 patients were used for gene expression analysis and classifier construction, followed by validation in five external cohorts (n = 619).

RESULTS

A 20-gene classifier established in the EXPRESSION cohort using a Youden index-based cut-off point predicted pCR in the validation cohorts with an accuracy, AUC, negative predictive value (NPV), positive predictive value, sensitivity, and specificity of 0.811, 0.768, 0.829, 0.587, 0.216, and 0.962, respectively. Alternatively, aiming for a high NPV by defining the cut-off point for classification based on the complete responder with the lowest predicted probability of pCR in the EXPRESSION cohort led to an NPV of 0.960 upon external validation. With this extreme-low cut-off point, a recommendation to not treat with anthracycline/taxane-based NACT would be possible for 121 of 619 unselected patients (19.5%) and 112 of 322 patients with luminal breast cancer (34.8%). The analysis of the molecular subtypes showed that the identification of patients who do not achieve a pCR by the 20-gene classifier was particularly relevant in luminal breast cancer.

CONCLUSIONS

The novel 20-gene classifier reliably identifies patients who do not achieve a pCR in about one third of luminal breast cancers in both the EXPRESSION and combined validation cohorts.

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Aim

Immune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).

Methods

Immune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.

Results

We selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.

Conclusion

The current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

Identification of genes that correlate clear cell renal cell carcinoma and obesity and exhibit potential prognostic value

Background

Renal cell carcinoma (RCC) is a common urologic malignancy. Although the relationship between clear cell RCC (ccRCC) and obesity has been well-established by several large-scale retrospective studies, the molecular mechanisms and genetic characteristics behind this correlation remains unclear. In the current study, several bioinformatics tools were used to identify the key genes in ccRCC related to obesity.

Methods

Microarray data comparing ccRCC with normal renal tissues in patients with and without obesity were downloaded from the GEO database for screening of differentially expressed genes (DEGs). The DEGs were verified with expression level and survival analysis using several online bioinformatics tools.

Results

In the current study, the differential expression of five genes correlated with both ccRCC and obesity; IGHA1 and IGKC as oncogenes, and MAOA, MUC20 and TRPM3 as tumor suppressor genes. These genes were verified by comparing the relationship between the expression levels and survival outcomes from open-source data in The Cancer Genome Atlas (TCGA) dataset.

Conclusions

In conclusion, the five genes differentially expressed in ccRCC and obesity are related to disease progression and prognosis, and therefore could provide prognostic value for patients with ccRCC.

Global Proteome and Phosphoproteome Characterization of Sepsis-induced Kidney Injury

Sepsis-induced acute kidney injury (S-AKI) is the most common complication in hospitalized and critically ill patients, highlighted by a rapid decline of kidney function occurring a few hours or days after sepsis onset. Systemic inflammation elicited by microbial infections is believed to lead to kidney damage under immunocompromised conditions. However, although AKI has been recognized as a disease with long-term sequelae, partly because of the associated higher risk of chronic kidney disease (CKD), the understanding of kidney pathophysiology at the molecular level and the global view of dynamic regulations in situ after S-AKI, including the transition to CKD, remains limited. Existing studies of S-AKI mainly focus on deriving sepsis biomarkers from body fluids. In the present study, we constructed a mid-severity septic murine model using cecal ligation and puncture (CLP), and examined the temporal changes to the kidney proteome and phosphoproteome at day 2 and day 7 after CLP surgery, corresponding to S-AKI and the transition to CKD, respectively, by employing an ultrafast and economical filter-based sample processing method combined with the label-free quantitation approach. Collectively, we identified 2,119 proteins and 2950 phosphosites through multi-proteomics analyses. Among them, we identified an array of highly promising candidate marker proteins indicative of disease onset and progression accompanied by immunoblot validations, and further denoted the pathways that are specifically responsive to S-AKI and its transition to CKD, which include regulation of cell metabolism regulation, oxidative stress, and energy consumption in the diseased kidneys. Our data can serve as an enriched resource for the identification of mechanisms and biomarkers for sepsis-induced kidney diseases.

Proteomic Profiling of Archived Tissue of Primary Melanoma Identifies Proteins Associated with Metastasis

Formalin-fixed paraffin embedded (FFPE) clinical tissues represent an abundant and unique resource for translational proteomic studies. In the US, melanoma is the 5th and 6th most common cancer in men and women, respectively, affecting over 230,000 people annually and metastasising in 5–15% of cases. Median survival time for distant metastatic melanoma is 6–9 months with a 5-year-survival of < 15%. In this study, 24 primary FFPE tumours which have metastasised (P-M) and 24 primary FFPE tumours which did not metastasise (P-NM) were subjected to proteomic profiling. In total, 2750 proteins were identified, of which 16 were significantly differentially expressed. Analysis of TCGA data demonstrated that expression of the genes encoding for 6 of these 16 proteins had a significant effect on survival in cutaneous melanoma. Pathway analysis of the proteomics data revealed mechanisms likely involved in the process of melanoma metastasis, including cytoskeleton rearrangement, extracellular changes and immune system alterations. A machine learning prediction model scoring an AUC of 0.922, based on these 16 differentially expressed proteins was able to accurately classify samples into P-M and P-NM. This study has identified potential biomarkers and key processes relating to melanoma metastasis using archived clinical samples, providing a basis for future studies in larger cohorts.

Construction of a Prognostic Immune Signature for Squamous-Cell Lung Cancer to Predict Survival

Background

Limited treatment strategies are available for squamous-cell lung cancer (SQLC) patients. Few studies have addressed whether immune-related genes (IRGs) or the tumor immune microenvironment can predict the prognosis for SQLC patients. Our study aimed to construct a signature predict prognosis for SQLC patients based on IRGs.

Methods

We constructed and validated a signature from SQLC patients in The Cancer Genome Atlas (TCGA) using bioinformatics analysis. The underlying mechanisms of the signature were also explored with immune cells and mutation profiles.

Results

A total of 464 eligible SQLC patients from TCGA dataset were enrolled and were randomly divided into the training cohort (n = 232) and the testing cohort (n = 232). Eight differentially expressed IRGs were identified and applied to construct the immune signature in the training cohort. The signature showed a significant difference in overall survival (OS) between low-risk and high-risk cohorts (P < 0.001), with an area under the curve of 0.76. The predictive capability was verified with the testing and total cohorts. Multivariate analysis revealed that the 8-IRG signature served as an independent prognostic factor for OS in SQLC patients. Naive B cells, resting memory CD4 T cells, follicular helper T cells, and M2 macrophages were found to significantly associate with OS. There was no statistical difference in terms of tumor mutational burden between the high-risk and low-risk cohorts.

Conclusion

Our study constructed and validated an 8-IRG signature prognostic model that predicts clinical outcomes for SQLC patients. However, this signature model needs further validation with a larger number of patients.

Protein Profiling of Serum Extracellular Vesicles Reveals Qualitative and Quantitative Differences After Differential Ultracentrifugation and ExoQuickTM Isolation

Solid tumor biopsies are the current standard for precision medicine. However, the procedure is invasive and not always feasible. In contrast, liquid biopsies, such as serum enriched for extracellular vesicles (EVs) represent a non-invasive source of cancer biomarkers. In this study, we compared two EV isolation methods in the context of the protein biomarker detection in inflammatory bowel disease (IBD) and colorectal cancer (CRC). Using serum samples of a healthy cohort as well as CRC and IBD patients, EVs were isolated by ultracentrifugation and ExoQuick™ in parallel. EV associated protein profiles were compared by multiplex-fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) and subsequent identification by mass spectrometry. Validation of gelsolin (GSN) was performed using fluorescence-quantitative western blot. 2D-DIGE resolved 936 protein spots in all serum-enriched EVs isolated by ultracentrifugation or ExoQuick™. Hereof, 93 spots were differently expressed between isolation approaches. Higher levels of GSN in EVs obtained with ExoQuick™ compared to ultracentrifugation were confirmed by western blot (p = 0.0006). Although patient groups were distinguishable after both EV isolation approaches, sample preparation strongly influences EVs’ protein profile and thus impacts on inter-study reproducibility, biomarker identification and validation. The results stress the need for strict SOPs in EV research before clinical implementation can be reached.

Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.

Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma

This work aimed to investigate tumor-infiltrating immune cells (TIICs) and immune-associated genes in the tumor microenvironment of osteosarcoma. An algorithm known as ESTIMATE was applied for immune score assessment, and osteosarcoma cases were assigned to the high and low immune score groups. Immune-associated genes between these groups were compared, and an optimal immune-related risk model was built by Cox regression analyses. The deconvolution algorithm (referred to as CIBERSORT) was applied to assess 22 TIICs for their amounts in the osteosarcoma microenvironment. Osteosarcoma cases with high immune score had significantly improved outcome (P<0.01). The proportions of naive B cells and M0 macrophages were significantly lower in high immune score tissues compared with the low immune score group (P<0.05), while the amounts of M1 macrophages, M2 macrophages, and resting dendritic cells were significantly higher (P<0.05). Important immune-associated genes were determined to generate a prognostic model by Cox regression analysis. Interestingly, cases with high risk score had poor outcome (P<0.01). The areas under the curve (AUC) for the risk model in predicting 1, 3 and 5-year survival were 0.634, 0.781, and 0.809, respectively. Gene set enrichment analysis suggested immunosuppression in high-risk osteosarcoma patients, in association with poor outcome.

Bioinformatic profiling of prognosis-related genes in the breast cancer immune microenvironment

In the microenvironment of breast cancer, immune cell infiltration is associated with an improved prognosis. To identify immune-related prognostic markers and therapeutic targets, we determined the lymphocyte-specific kinase (LCK) metagene scores of samples from breast cancer patients in The Cancer Genome Atlas. The LCK metagene score correlated highly with other immune-related scores, as well as with the clinical stage, prognosis and tumor suppressor gene mutation status (BRCA2, TP53, PTEN) of patients in the four breast cancer subtypes. A weighted gene co-expression network analysis was performed to detect representative genes from LCK metagene-related gene modules. In two of these modules, the levels of the co-expressed genes correlated highly with LCK metagene levels, so we conducted an enrichment analysis to discover their functions. We also identified differentially expressed genes in samples with high and low LCK metagene scores. By examining the overlapping results from these analyses, we obtained 115 genes, and found that 22 of them were independent predictors of overall survival in breast cancer patients. These genes were validated for their prognostic and diagnostic value with external data sets and paired tumor and non-tumor tissues. The genes identified herein could serve as diagnostic/prognostic markers and immune-related therapeutic targets in breast cancer.