Identifying hub genes of clear cell renal cell carcinoma associated with the proportion of regulatory T cells by weighted gene co-expression network analysis

Signaling lymphocytic activation molecule family receptors as potential immune therapeutic targets in solid tumors

Recently, cancer immunotherapy has revolutionized cancer treatment. Various forms of immunotherapy have a manageable safety profile and result in prolongation of overall survival in patients with solid tumors, but only in a proportion of patients. Various factors in the tumor microenvironment play critical roles and may be responsible for this lack of therapeutic response. Signaling lymphocytic activation molecule family (SLAMF) members are increasingly being studied as factors impacting the tumor immune microenvironment. SLAMF members consist of nine receptors mainly expressed in immune cells. However, SLAMF receptors have also been detected in cancer cells, and they may be involved in a spectrum of anti-tumor immune responses. Here, we review the current knowledge of the expression of SLAMF receptors in solid tumors and tumor-infiltrating immune cells and their association with patient outcomes. Furthermore, we discuss the therapeutic potential of targeting SLAMF receptors to improve outcomes of cancer therapy in solid tumors. We believe the research on SLAMF receptor-targeted strategies may enhance anti-cancer immunity in patients with solid tumors and improve clinical outcomes.

Genomic Regions Associated with Resistance to Gastrointestinal Nematode Parasites in Sheep-A Review

Gastrointestinal nematodes (GINs) can be a major constraint and global challenge to the sheep industry. These nematodes infect the small intestine and abomasum of grazing sheep, causing symptoms such as weight loss, diarrhea, hypoproteinemia, and anemia, which can lead to death. The use of anthelmintics to treat infected animals has led to GIN resistance, and excessive use of these drugs has resulted in residue traced in food and the environment. Resistance to GINs can be measured using multiple traits, including fecal egg count (FEC), Faffa Malan Chart scores, hematocrit, packed cell volume, eosinophilia, immunoglobulin (Ig), and dagginess scores. Genetic variation among animals exists, and understanding these differences can help identify genomic regions associated with resistance to GINs in sheep. Genes playing important roles in the immune system were identified in several studies in this review, such as the CFI and MUC15 genes. Results from several studies showed overlapping quantitative trait loci (QTLs) associated with multiple traits measuring resistance to GINs, mainly FEC. The discovery of genomic regions, positional candidate genes, and QTLs associated with resistance to GINs can help increase and accelerate genetic gains in sheep breeding programs and reveal the genetic basis and biological mechanisms underlying this trait.

Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC.

Methods

First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification.

Results

The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options.

Conclusions

The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.

Integrative analysis developing and validating potential candidate biomarkers for cancer stemness features of pan-renal cell carcinoma

In our study, 49 key genes significantly associated with renal cell carcinoma (RCC) stemness were obtained. Next, we developed a molecular prognostic signature associated with stemness features of pan-RCC. The difference in OS between high-risk and low-risk group was statistically significant (P < 0.05). The area under ROC curve for 1-years OS, 5-years OS and 10-years OS was 0.759, 0.712 and 0.918, respectively. The results of validation in TCGA cohort and ICGC cohort revealed the predictive capability of this signature. Further, we selected three genes and further validation showed that these three hub genes were potential hub biomarkers for pan-RCC stemness features.

ARPC1A correlates with poor prognosis in prostate cancer and is up-regulated by glutamine metabolism to promote tumor cell migration, invasion and cytoskeletal changes

OBJECTIVE

This study aimed to identify potential biomarkers for prostate cancer (PCa) progression and metastasis, and to discern their biological functions.

METHODS

Bioinformatics methods were used to screen for hub genes. The expression level of key hub genes in PCa was determined and their prognostic significance was examined. A series of functional assays were performed to investigate the function of the highest-ranking hub gene.

RESULTS

Actin related protein 2/3 complex subunit 1A (ARPC1A) was identified as the hub gene. ARPC1A was highly expressed in PCa tissues and cell lines, and was an independent prognostic factor for predicting biochemical recurrence after radical prostatectomy and overall survival of PCa patients. Knockdown of ARPC1A inhibited PCa cell migration, invasion and cytoskeleton formation, but had no impact on cell proliferation and cell cycle progression. In vivo, ARPC1A overexpression promoted lung metastasis of PCa, but had no efffect on tumor growth. Additionally, glutamine metabolism was identified as an upstream regulator of ARPC1A, and promoted migration, invasion and cytoskeletal changes of PCa cell through ARPC1A.

CONCLUSION

These findings suggested that ARPC1A, which correlates with poor prognosis in PCa, functions downstream of glutamine metabolism to regulate cytoskeletal changes, cellular migration and cellular invasion in this disease.

miR-21-5p/PRKCE axis implicated in immune infiltration and poor prognosis of kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC or ccRCC) is the most notorious subtype of renal cell carcinoma for its poor prognosis. Mounting evidence has highlighted the key role of PRKCE in the initiation and development of several types of human cancer, including kidney renal clear cell carcinoma (KIRC). However, the mechanism of PRKCE aberrant expression and the specific clinical correlation of PRKCE expression with immune cell infiltration in KIRC remains elusive. Therefore, we analyzed the relationship between PRKCE and KIRC using many databases, including Oncomine, TCGA, GTEx, TIMER, and GEO. We found that PRKCE decreased in KIRC tumor tissue compared to normal tissue. The Kaplan-Meier Plotter analysis and Univariate and Multivariate Cox analyses were used to evaluate the association between PRKCE and clinicopathological variables and prognosis. Low PRKCE expression was associated with poor survival and histologic grade, T stage, pathologic stage, and M stage. Besides, the C-indexes and calibration plots of the nomogram based on multivariate analysis showed an effective predictive performance for KIRC patients. In addition, PRKCE may be positively correlated with inflammation and negatively correlated with proliferation, metastasis, and invasion as identified by CancerSEA. Moreover, overexpression of PRKCE suppressed ACHN and Caki-1 cell proliferation, migration, and invasion in vitro. Additionally, methylation level data acquired from UALCAN, DiseaseMeth, CCLE, LinkedOmics, and MEXPRESS was used to investigate the relationship between PRKCE expression and PRKCE methylation level. Furthermore, upstream potential miRNA predictions were further performed to explore the mechanism of PRKCE decreased expression in KIRC using multiple online databases available on publicly assessable bioinformatics platforms. High PRKCE methylation levels and hsa-miR-21-5p may contribute to PRKCE low expression in KIRC. Finally, an analysis of immune infiltration indicated that PRKCE was associated with immune cell infiltration. Importantly, PRKCE may affect prognosis partially by regulating immune infiltration in KIRC. In summary, PRKCE may serve as a novel prognostic biomarker reflecting immune infiltration level and a novel therapeutic target in KIRC.

A Novel Ferroptosis-Based Molecular Signature Associated with Biochemical Recurrence-Free Survival and Tumor Immune Microenvironment of Prostate Cancer

Objective: To identify ferroptosis-related molecular clusters, and to develop and validate a ferroptosis-based molecular signature for predicting biochemical recurrence-free survival (BCRFS) and tumor immune microenvironment of prostate cancer (PCa). Materials and Methods: The clinical data and transcriptome data of PCa were downloaded from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained from FerrDb database. We performed consensus clustering analysis to identify ferroptosis-related molecular subtypes for PCa. Univariate and multivariate Cox regression analysis were used to establish a ferroptosis-based signature for predicting BCRFS. Internal verification, external verification and subgroup survival analysis were then successfully performed. Results: There was a total of 40 differentially expressed FRGs in PCa. We then identified three ferroptosis-related molecular clusters of PCa, which have significantly different immune infiltrating cells, tumor immune microenvironment and PD-L1 expression level. More importantly, a novel ferroptosis-based signature for predicting BCRFS of PCa based on four FRGs (including ASNS, GPT2, NFE2L2, RRM2) was developed. Internal and external verifications were then successfully performed. Patients with high-risk score were associated with significant poor BCRFS compared with those with low-risk score in training cohort, testing cohort and validating cohort, respectively. The area under time-dependent Receiver Operating Characteristic (ROC) curve were 0.755, 0.705 and 0.726 in training cohort, testing cohort and validating cohort, respectively, indicating the great performance of this signature. Independent prognostic analysis indicated that this signature was an independent predictor for BCRFS of PCa. Subgroup analysis revealed that this signature was particularly suitable for younger or stage T III-IV or stage N0 or cluster 1-2 PCa patients. Patients with high-risk score have significantly different tumor immune microenvironment in comparison with those with low-risk score. The results of qRT-PCR successfully verified the mRNA expression levels of ASNS, GPT2, RRM2 and NFE2L2 in DU-145 and RWPE-1 cells while the results of IHC staining exactly verified the relative protein expression levels of ASNS, GPT2, RRM2 and NFE2L2 between PCa and BPH tissues. Conclusions: This study successfully identified three ferroptosis-related molecular clusters. Besides, we developed and validated a novel ferroptosis-based molecular signature, which performed well in predicting BCRFS and tumor immune microenvironment of PCa.

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.

Methods

CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan-Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.

Results

Red module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.

Conclusion

We developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Development and Validation of Novel Biomarkers Related to M2 Macrophages Infiltration by Weighted Gene Co-Expression Network Analysis in Prostate Cancer

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients.

Identification of vital prognostic genes related to tumor microenvironment in pheochromocytoma and paraganglioma based on weighted gene co-expression network analysis

Pheochromocytoma and paraganglioma (PCPG) is a rare neuroendocrine tumor. This study aims to identify vital prognostic genes which were associated with PCPG tumor microenvironment (TME). We downloaded transcriptome data of PCPG from TCGA database and calculated the immune scores and stromal scores by using the ESTIMATE algorithm. DEGs related to TMB were then identified. We conducted WGCNA to further extract the TME-related modules. GO, KEGG pathway analysis, and PPI network were performed. Survival analysis was conducted to identify the hub genes associated with the prognosis of PCPG. A total of 150 PCPG samples were included in this study. We obtained 1507 and 2067 DEGs based on immune scores and stromal scores, respectively. WGCNA analysis identified the red module and brown module were correlated with immune sores while the turquoise module and red module were significantly associated with stromal scores. Functional enrichments analysis revealed that 307 TME-related genes were correlated with the inflammation or immune response. Survival analysis showed that three TME-relate genes (ADGRE1, CCL18, and LILRA6) were associated with PCPG prognosis. These three hub genes including ADGRE1, CCL18, and LILRA6 might be involved in the progression of PCPG and could serve as potential biomarkers and novel therapeutic targets.

Tumor volume: a new prognostic factor of oncological outcome of localized clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most frequent malignancies; however, the present prognostic factors was deficient. This study aims to explore whether there is a relationship between tumor volume (TV) and oncological outcomes for localized ccRCC.

METHODS

Seven hundred forty-nine localized ccRCC patients underwent surgery in our hospital. TV was outlined and calculated using a three-dimensional conformal radiotherapy planning system. We used receiver operating characteristic (ROC) curves to identified optimal cut-off value. Univariable and multivariable Cox regression models were performed to explore the association between TV and oncological outcomes. Kaplan-Meier method and log-rank test were used to estimate survival probabilities and determine the significance, respectively. Time-dependent ROC curve was utilized to assess the prognostic effect.

RESULTS

Log rank test showed that higher Fuhrman grade, advanced pT classification and higher TV were associated with shortened OS, cancer-specific survival (CSS), freedom from metastasis (FFM) and freedom from local recurrence (FFLR). multivariable analysis showed higher Fuhrman grade and higher TV were predictors of adverse OS and CSS. The AUC of TV for FFLR was 0.822. The AUC of TV (0.864) for FFM was higher than that of pT classification (0.818) and Fuhrman grade (0.803). For OS and CSS, the AUC of TV was higher than that of Fuhrman grade (0.832 vs. 0.799; 0.829 vs 0.790).

CONCLUSIONS

High TV was an independent predictor of poor CSS, OS, FFLR and FFM of localized ccRCC. Compared with pT classification and Fuhrman grade, TV could be a new and better prognostic factor of oncological outcome of localized ccRCC, which might contribute to tailored follow-up or management strategies.

The effect of CCL5 on the immune cells infiltration and the prognosis of patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is the most representative subtype of renal cancer. Immune infiltration was associated with the survival time of patients with tumors. C-C chemokine ligand 5 (CCL5) can promote the malignant process of tumor and be related to infiltration immune cells in some cancers, but not reported in KIRC. Methods: The expression profile and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. The correlation between the expression level of CCL5 and clinical features in KIRC was analyzed. Gene Set Enrichment Analysis (GSEA) was utilized to explore the functions and pathways of CCL5 in KIRC. Then, the analysis between the survival and immune infiltration cells was carried out, as well as the non-parametric tests between the CCL5 expression and the ratios of immune infiltration cells. Results: The correlations between the expression levels of CCL5 in KIRC and clinical features including survival time, pathological stage, grade, and status of the patient, have been identified. Meanwhile, GSEA analysis has shown relationships between the expression of CCL5 and immune pathways. The immune infiltrated cells were correlated with the prognosis of KIRC, especially regulatory T cells (Tregs), mast cells, and dendritic cells. And Tregs was associated with the CCL5 expression. Conclusion: The increased expression of CCL5 is related to poor prognosis and clinical features. Meanwhile, CCL5 is related to Tregs ratios and CCL5 may act as a typical chemokine to recruit Tregs in KIRC. CCL5 could be used as a biomarker for the prognosis prediction and a potential therapeutic target for patients with KIRC.

Risk factors for intraoperative cyst rupture in partial nephrectomy for cystic renal masses

PURPOSE

To determine the risk factors of intraoperative cyst rupture in partial nephrectomy (PN) for a cystic renal mass (CRM) and their effect on the prognosis of patients.

MATERIALS AND METHODS

Patients who underwent partial nephrectomy for CRMs from January 2009 to January 2015 were included. Uni/multivariate Logistic/Cox analysis and Kaplan-Meier analysis were performed.

RESULTS

A total of 174 patients were included in this study. There were 27 (15.5%) intraoperative cyst ruptures. The median follow-up time was 60 months. Multivariate logistic analysis showed that the E component (P = 0.018) and N component (P = 0.022) of the R.E.N.A.L. nephrometry score, Bosniak category III (P = 0.044), and surgeon's experience (P = 0.030) were risk factors associated with intraoperative cyst rupture in PN for CRMs. The 5-year recurrence-free survival (RFS), cancer-free survival (CFS) and overall survival (OS) were 92.7%, 90.32% and 94.4%, respectively, in 124 cases of malignant CRM. Kaplan-Meier analysis demonstrated that 5-year RFS and 5-year CFS in patients with cyst rupture was worse than those without cyst rupture (P = 0.006 and 0.003, respectively). Multivariate Cox analysis revealed that intraoperative cyst rupture was independent risk factor for 5-year RFS and 5-year CFS (P = 0.039 and 0.013, respectively). However, there was no significant difference in OS between the two groups (P = 0.275).

CONCLUSIONS

The prevalence of intraoperative cyst rupture is relatively high. Higher E and N scores, Bosniak category III, and lacking surgical experience (<20 cases) increase the risk of occurrence of intraoperative cyst rupture.