Antioxidant Gene Signature Impacts the Immune Infiltration and Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

Pathological and Biological Significance of the Specific Glycan, TRA-1-60, on Aggressive Gastric Adenocarcinoma

The glycans form a unique complex on the surface of cancer cells and play a pivotal role in tumor progression, impacting proliferation, invasion, and metastasis. TRA-1-60 is a glycan that was identified as a critical marker for the establishment of fully reprogrammed inducible pluripotent stem cells. Its expression has been detected in multiple cancer tissues, including embryonal carcinoma, prostate cancer, and pancreatic cancer, but the biological and pathological characterization of TRA-1-60-expressing tumor cells remains unclear within various types of malignancies. Here, we report the biological characteristics of TRA-1-60-expressing gastric cancer cells, especially those with its cell surface expression, and the therapeutic significance of targeting TRA-1-60. The cells with cell membrane expression of TRA-1-60 were mainly observed in the invasive area of patient gastric cancer tissues and correlated with advanced stages of the disease based on histopathological and clinicopathological analyses. In vitro analysis using a scirrhous gastric adenocarcinoma line, HSC-58, which highly expresses TRA-1-60 on its plasma membrane, revealed increased stress-resistant mechanisms, supported by the upregulation of glutathione synthetase and NCF-1 (p47phox) via lipid-ROS regulatory pathways, as detected by RNA-seq analysis followed by oxidative stress gene profiling. Our in vivo therapeutic study using the TRA-1-60-targeting antibody-drug conjugate, namely, Bstrongomab-conjugated monomethyl auristatin E, showed robust efficacy in a mouse model of peritoneal carcinomatosis induced by intraperitoneal xenograft of HSC-58, by markedly reducing massive tumor ascites. Thus, targeting the specific cell surface glycan, TRA-1-60, shows a significant therapeutic impact in advanced-stage gastric cancers.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Proteomic analysis of holocarboxylase synthetase deficient-MDA-MB-231 breast cancer cells revealed the biochemical changes associated with cell death, impaired growth signaling, and metabolism

We have previously shown that the holocarboxylase synthetase (HLCS) is overexpressed in breast cancer tissue of patients, and silencing of its expression in triple-negative cancer cell line inhibits growth and migration. Here we investigated the global biochemical changes associated with HLCS knockdown in MDA-MB-231 cells to discern the pathways that involve HLCS. Proteomic analysis of two independent HLCS knockdown cell lines identified 347 differentially expressed proteins (DEPs) whose expression change > 2-fold (p < 0.05) relative to the control cell line. GO enrichment analysis showed that these DEPs were mainly associated with the cellular process such as cellular metabolic process, cellular response to stimulus, and cellular component organization or biogenesis, metabolic process, biological regulation, response to stimuli, localization, and signaling. Among the 347 identified DEPs, 64 proteins were commonly found in both HLCS knockdown clones, confirming their authenticity. Validation of some of these DEPs by Western blot analysis showed that plasminogen activator inhibitor type 2 (SerpinB2) and interstitial collagenase (MMP1) were approximately 90% decreased in HLCS knockdown cells, consistent with a 50%-60% decrease in invasion ability of knockdown cells. Notably, argininosuccinate synthase 1 (ASS1), one of the enzymes in the urea cycle, showed approximately a 10-fold increase in the knockdown cells, suggesting the crucial role of HLCS in supporting the urea cycle in the triple-negative cancer cell line. Collectively, our proteomic data provide biochemical insights into how suppression of HLCS expression perturbs global changes in cellular processes and metabolic pathways, impairing cell growth and invasion.

LncRNA HOTAIR as a ceRNA is related to breast cancer risk and prognosis

PURPOSE

Breast cancer (BC) is one of the biggest threats to women's health. LncRNA HOTAIR is related to the recurrence and metastasis of BC. Whether HOTAIR can serve as an effective biomarker to distinguish BC patients with different prognosis need to be further studied.

METHODS

The miRNA and mRNA expression profile data of BC patients were downloaded from TCGA database. Univariate Cox regression was used to screen differential expression genes (DEGs). The miRcode database and miRWalk database were used to predict miRNA binding to HOTAIR and binding sites of miRNAs, respectively. Kaplan-Meier (KM) analysis was used to estimate the overall survival rate of BC patients. Finally, qRT-PCR and western blot were applied to evaluate the expression level of HOTAIR and mRNAs between BC cells and normal mammary cells.

RESULTS

The patients with high HOTAIR expression had poor prognosis in BC. Totally 10 genes correlated with BC prognosis were identified from 170 DEGs, among which PAX7, IYD, ZIC2, MS4A1, TPRXL, CD24, LHX1 were positively correlated with HOTAIR, while CHAD, NPY1R, TPRG1 were opposite. The levels of IYD, ZIC2, CD24 mRNA and protein were increased in BC tissues and BC cells. In BC cells, the levels of IYD, ZIC2 and CD24 mRNA and protein were significantly increased in HOTAIR overexpressed group. HOTAIR had the strongest interaction with hsa-miR-129-5p, followed by hsa-miR-107.

CONCLUSION

HOTAIR regulated the expression of downstream genes by interacting with 8 miRNAs and ultimately affected the prognosis of BC patients.

Redox Signaling Modulates Activity of Immune Checkpoint Inhibitors in Cancer Patients

Although immunotherapy is already a staple of cancer care, many patients may not benefit from these cutting-edge treatments. A crucial field of research now focuses on figuring out how to improve treatment efficacy and assess the resistance mechanisms underlying this uneven response. For a good response, immune-based treatments, in particular immune checkpoint inhibitors, rely on a strong infiltration of T cells into the tumour microenvironment. The severe metabolic environment that immune cells must endure can drastically reduce effector activity. These immune dysregulation-related tumour-mediated perturbations include oxidative stress, which can encourage lipid peroxidation, ER stress, and T regulatory cells dysfunction. In this review, we have made an effort to characterize the status of immunological checkpoints, the degree of oxidative stress, and the part that latter plays in determining the therapeutic impact of immunological check point inhibitors in different neoplastic diseases. In the second section of the review, we will make an effort to assess new therapeutic possibilities that, by affecting redox signalling, may modify the effectiveness of immunological treatment.

A novel lipid metabolism gene signature for clear cell renal cell carcinoma using integrated bioinformatics analysis

Background: Clear cell renal cell carcinoma (ccRCC), which is the most prevalent type of renal cell carcinoma, has a high mortality rate. Lipid metabolism reprogramming is a hallmark of ccRCC progression, but its specific mechanism remains unclear. Here, the relationship between dysregulated lipid metabolism genes (LMGs) and ccRCC progression was investigated. Methods: The ccRCC transcriptome data and patients' clinical traits were obtained from several databases. A list of LMGs was selected, differentially expressed gene screening performed to detect differential LMGs, survival analysis performed, a prognostic model established, and immune landscape evaluated using the CIBERSORT algorithm. Gene Set Variation Analysis and Gene set enrichment analysis were conducted to explore the mechanism by which LMGs affect ccRCC progression. Single-cell RNA-sequencing data were obtained from relevant datasets. Immunohistochemistry and RT-PCR were used to validate the expression of prognostic LMGs. Results: Seventy-one differential LMGs were identified between ccRCC and control samples, and a novel risk score model established comprising 11 LMGs (ABCB4, DPEP1, IL4I1, ENO2, PLD4, CEL, HSD11B2, ACADSB, ELOVL2, LPA, and PIK3R6); this risk model could predict ccRCC survival. The high-risk group had worse prognoses and higher immune pathway activation and cancer development. Conclusion: Our results showed that this prognostic model can affect ccRCC progression.

Identification of quantitative trait loci for survival in the mutant dynactin p150Glued mouse model of motor neuron disease

Amyotrophic lateral sclerosis (ALS) is the most common degenerative motor neuron disorder. Although most cases of ALS are sporadic, 5-10% of cases are familial, with mutations associated with over 40 genes. There is variation of ALS symptoms within families carrying the same mutation; the disease may develop in one sibling and not in another despite the presence of the mutation in both. Although the cause of this phenotypic variation is unknown, it is likely related to genetic modifiers of disease expression. The identification of ALS causing genes has led to the development of transgenic mouse models of motor neuron disease. Similar to families with familial ALS, there are background-dependent differences in disease phenotype in transgenic mouse models of ALS suggesting that, as in human ALS, differences in phenotype may be ascribed to genetic modifiers. These genetic modifiers may not cause ALS rather their expression either exacerbates or ameliorates the effect of the mutant ALS causing genes. We have reported that in both the G93A-hSOD1 and G59S-hDCTN1 mouse models, SJL mice demonstrated a more severe phenotype than C57BL6 mice. From reciprocal intercrosses between G93A-hSOD1 transgenic mice on SJL and C57BL6 strains, we identified a major quantitative trait locus (QTL) on mouse chromosome 17 that results in a significant shift in lifespan. In this study we generated reciprocal intercrosses between transgenic G59S-hDCTN1 mice on SJL and C57BL6 strains and identified survival QTLs on mouse chromosomes 17 and 18. The chromosome 17 survival QTL on G93A-hSOD1 and G59S-hDCTN1 mice partly overlap, suggesting that the genetic modifiers located in this region may be shared by these two ALS models despite the fact that motor neuron degeneration is caused by mutations in different proteins. The overlapping region contains eighty-seven genes with non-synonymous variations predicted to be deleterious and/or damaging. Two genes in this segment, NOTCH3 and Safb/SAFB1, have been associated with motor neuron disease. The identification of genetic modifiers of motor neuron disease, especially those modifiers that are shared by SOD1 and dynactin-1 transgenic mice, may result in the identification of novel targets for therapies that can alter the course of this devastating illness.

Novel amino acid metabolism‐related gene signature to predict prognosis in clear cell renal cell carcinoma

Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes.

Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM‐related differentially expressed genes were screened via the “limma” package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the “survminer” package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m⁶A)-related genes, and sensitivity to chemotherapy was assessed.

Results: A novel consensus AMM‐related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m⁶A modification, and sensitivity to chemotherapy drugs.

Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.

Identification of epigenetic dysregulation gene markers and immune landscape in kidney renal clear cell carcinoma by comprehensive genomic analysis

Kidney cancer is one the most lethal cancers of the urinary system, but current treatments are limited and its prognosis is poor. This study focused on kidney renal clear cell carcinoma (KIRC) and analyzed the relationship between epigenetic alterations and KIRC prognosis, and explored the prognostic significance of these findings in KIRC patients. Based on multi-omics data, differentially expressed histone-modified genes were identified using the R package limma package. Gene enhancers were detected from data in the FANTOM5 database. Gene promoters were screened using the R package ChIPseeker, and the Bumphunter in the R package CHAMP was applied to screen differentially methylated regions (DMR). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) functional enrichment analysis of genes was performed using the R package clusterProfiler. We identified 51 dysregulated epigenetic protein coding genes (epi-PCGs) from 872 epi-PCGs, and categorized three molecular subtypes (C1, C2, and C3) of KIRC samples with significantly different prognosis. Notably, among the three molecular subtypes, we found a markedly differential immune features in immune checkpoints, cytokines, immune signatures, and immune cell distribution. C2 subtype had significantly lower enrichment score of IFNγ, cytotoxic score (CYT), and angiogenesis. In addition, an 8-gene signature containing 8 epi-PCGs (ETV4, SH2B3, FATE1, GRK5, MALL, HRH2, SEMA3G, and SLC10A6) was developed for predicting KIRC prognosis. Prognosis of patients with a high 8-gene signature score was significantly worse than those with a low 8-gene signature score, which was also validated by the independent validation data. The 8-gene signature had a better performance compared with previous signatures of KIRC. Overall, this study highlighted the important role of epigenetic regulation in KIRC development, and explored prognostic epi-PCGs, which may provide a guidance for exploiting further pathological mechanisms of KIRC and for developing novel drug targets.

Iron accumulation typifies renal cell carcinoma tumorigenesis but abates with pathological progression, sarcomatoid dedifferentiation, and metastasis

Iron is a potent catalyst of oxidative stress and cellular proliferation implicated in renal cell carcinoma (RCC) tumorigenesis, yet it also drives ferroptosis that suppresses cancer progression and represents a novel therapeutic target for advanced RCC. The von Hippel Lindau (VHL)/hypoxia-inducible factor-α (HIF-α) axis is a major regulator of cellular iron, and its inactivation underlying most clear cell (cc) RCC tumors introduces both iron dependency and ferroptosis susceptibility. Despite the central role for iron in VHL/HIF-α signaling and ferroptosis, RCC iron levels and their dynamics during RCC initiation/progression are poorly defined. Here, we conducted a large-scale investigation into the incidence and prognostic significance of total tissue iron in ccRCC and non-ccRCC patient primary tumor cancer cells, tumor microenvironment (TME), metastases and non-neoplastic kidneys. Prussian Blue staining was performed to detect non-heme iron accumulation in over 1600 needle-core sections across multiple tissue microarrays. We found that RCC had significantly higher iron staining scores compared with other solid cancers and, on average, >40 times higher than adjacent renal epithelium. RCC cell iron levels correlated positively with TME iron levels and inversely with RCC levels of the main iron uptake protein, transferrin receptor 1 (TfR1/TFRC/CD71). Intriguingly, RCC iron levels, including in the TME, decreased significantly with pathologic (size/stage/grade) progression, sarcomatoid dedifferentiation, and metastasis, particularly among patients with ccRCC, despite increasing TfR1 levels, consistent with an increasingly iron-deficient tumor state. Opposite to tumor iron changes, adjacent renal epithelial iron increased significantly with RCC/ccRCC progression, sarcomatoid dedifferentiation, and metastasis. Lower tumor iron and higher renal epithelial iron each predicted significantly shorter ccRCC patient metastasis-free survival. In conclusion, iron accumulation typifies RCC tumors but declines toward a relative iron-deficient tumor state during progression to metastasis, despite precisely opposite dynamics in adjacent renal epithelium. These findings raise questions regarding the historically presumed selective advantage for high iron during all phases of cancer evolution, suggesting instead distinct tissue-specific roles during RCC carcinogenesis and early tumorigenesis versus later progression. Future study is warranted to determine how the relative iron deficiency of advanced RCC contributes to ferroptosis resistance and/or introduces a heightened susceptibility to iron deprivation that might be therapeutically exploitable.

Comprehensive analysis of the glutathione S-transferase Mu (GSTM) gene family in ovarian cancer identifies prognostic and expression significance

Background

Ovarian cancer (OC) is one of the most common types of gynecologic tumor over the world. The Glutathione S-transferase Mu (GSTM) has five members, including GSTM1-5. These GSTMs is involved in cell metabolism and detoxification, but their role in OC remains unknown.

Methods

Data from multiple public databases associated with OC and GSTMs were collected. Expression, prognosis, function enrichment, immune infiltration, stemness index, and drug sensitivity analysis was utilized to identify the roles of GSTMs in OC progression. RT-qPCR analysis confirmed the effect of AICAR, AT-7519, PHA-793887 and PI-103 on the mRNA levels of GSTM3/4.

Results

GSTM1-5 were decreased in OC samples compared to normal ovary samples. GSTM1/5 were positively correlated with OC prognosis, but GSTM3 was negatively correlated with OC prognosis. Function enrichment analysis indicated GSTMs were involved in glutathione metabolism, drug metabolism, and drug resistance. Immune infiltration analysis indicated GSTM2/3/4 promoted immune escape in OC. GSTM5 was significantly correlated with OC stemness index. GSTM3/4 were remarkedly associated with OC chemoresistance, especially in AICAR, AT-7519, PHA-793887 and PI-103.

Conclusion

GSTM3 was negatively correlated with OC prognosis, and associated with OC chemoresistance and immune escape. This gene may serve as potential prognostic biomarkers and therapeutic target for OC patients.

Interferon-alpha2 treatment of patients with polycythemia vera and related neoplasms favorably impacts deregulation of oxidative stress genes and antioxidative defense mechanisms

Chronic inflammation is considered a major driving force for clonal expansion and evolution in the Philadelphia-negative myeloproliferative neoplasms, which include essential thrombocythemia, polycythemia vera and primary myelofibrosis (MPNs). One of the key mutation drivers is the JAK2V617F mutation, which has been shown to induce the generation of reactive oxygen species (ROS). Using whole blood gene expression profiling, deregulation of several oxidative stress and anti-oxidative defense genes has been identified in MPNs, including significant downregulation of TP53, the NFE2L2 or NRF2 genes. These genes have a major role for maintaining genomic stability, regulation of the oxidative stress response and in modulating migration or retention of hematopoietic stem cells. Therefore, their deregulation might give rise to increasing genomic instability, increased chronic inflammation and disease progression with egress of hematopoietic stem cells from the bone marrow to seed in the spleen, liver and elsewhere. Interferon-alpha2 (rIFNα) is increasingly being recognized as the drug of choice for the treatment of patients with MPNs. Herein, we report the first gene expression profiling study on the impact of rIFNα upon oxidative stress and antioxidative defense genes in patients with MPNs (n = 33), showing that rIFNα downregulates several upregulated oxidative stress genes and upregulates downregulated antioxidative defense genes. Treatment with rIFNα induced upregulation of 19 genes in ET and 29 genes in PV including CXCR4 and TP53. In conclusion, this rIFNα- mediated dampening of genotoxic damage to hematopoietic cells may ultimately diminish the risk of additional mutations and accordingly clonal evolution and disease progression towards myelofibrotic and leukemic transformation.

Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

Background

Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration.

Methods

We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs.

Results

We identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity.

Conclusion

We established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC.

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed.

Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established.

Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan–Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/).

Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.