OGDHL closely associates with tumor microenvironment and can serve as a prognostic biomarker for papillary thyroid cancer

Novel role of lncRNAs regulatory network in papillary thyroid cancer

Papillary thyroid cancer (PTC) is the most common endocrine malignancy. The incidence of PTC has increased annually worldwide. Thus, PTC diagnosis and treatment attract more attention. Noncoding RNAs (lncRNAs) play crucial roles in PTC progression and act as prognostic biomarkers. Moreover, microRNAs (miRNAs) and epithelial-mesenchymal transition (EMT)-associated proteins have potential biomarkers for diagnosing and treating PTC. However, the correlation of lncRNAs with miRNAs and EMT-associated proteins needs further clarification. The present review highlights the recent advances of lncRNAs in PTC. We significantly summarized the two molecular regulatory mechanisms in PTC progress, including lncRNAs-miRNAs-protein signaling axes and lncRNAs-EMT pathways. This review will help our understanding of the association between lncRNAs and PTC and may assist us in evaluating the prognosis for PTC patients. Taken together, targeting the lncRNAs regulatory network has promising applications in diagnosing and treating PTC.

Low OGDHL expression affects the prognosis and immune infiltration of kidney renal clear cell carcinoma

Background

Oxoglutarate dehydrogenase-like (OGDHL) modulates glutamine metabolism to influence tumor progression. Therefore, we aimed to explore the potential role of OGDHL in the prognosis of kidney renal clear cell carcinoma (KIRC) and its effect on immune infiltration.

Methods

The Cancer Genome Atlas, Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis, Human Protein Atlas, and The University of Alabama at Birmingham Cancer databases and the GSE53757 dataset were utilized to analyze expression difference and prognosis of OGDHL in tumor and normal tissue; diagnostic value was assessed using receiver operating characteristic curves. Correlations with clinical features and survival prognosis were analyzed. Independent prognostic factors were identified using univariate and multifactorial Cox regression analysis. We used the CIBERSORT analysis tool to discover the proportion of tumor-infiltrating immune cells (TIICs) in KIRC patients. Next, the differences in the proportion of TIICs under different OGDHL expression were analyzed. Finally, we explored the potential mechanisms by which OGDHL expression affects patient survival using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA).

Results

OGDHL expression was markedly downregulated in KIRC tissues compared to in normal tissues, and the downregulation of OGDHL expression was significantly associated with tumor progression (including tumor stage and grade) and poor prognosis. Cox regression analyses revealed OGDHL to be an independent prognostic factor for KIRC. CIBERSORT analysis showed that OGDHL expression is associated with differences in the proportion of several TIICs, particularly resting mast cells. Finally, GO and KEGG analysis showed that OGDHL was associated with extracellular matrix and epithelial cell differentiation involved in kidney development. GSEA indicated that low OGDHL was closely related to the activation of carcinogenic signaling pathways, including epithelial mesenchymal transition, tumor necrosis factor alpha and nuclear factor kappa B signaling pathway, negative regulation of apoptotic signaling, collagen formation, etc.

Conclusions

OGDHL level can be monitored for diagnosing KIRC. Reduced expression is associated with poor prognosis and immune infiltration of KIRC. OGDHL is expected to become a new target for the treatment of KIRC.

The m6A modification-mediated OGDHL exerts a tumor suppressor role in ccRCC by downregulating FASN to inhibit lipid synthesis and ERK signaling

Metabolic reprogramming is a hallmark of cancer, and the impact of lipid metabolism as a crucial aspect of metabolic reprogramming on clear cell renal cell carcinoma (ccRCC) progression has been established. However, the regulatory mechanisms underlying the relationship between metabolic abnormalities and ccRCC progression remain unclear. Therefore, this study aimed to identify key regulatory factors of metabolic reprogramming in ccRCC and provide potential therapeutic targets for ccRCC patients. Potential metabolic regulatory factors in ccRCC were screened using bioinformatics analysis. Public databases and patient samples were used to investigate the aberrant expression of Oxoglutarate dehydrogenase-like (OGDHL) in ccRCC. The function of OGDHL in ccRCC growth and metastasis was evaluated through in vitro and in vivo functional experiments. Mechanistic insights were obtained through luciferase reporter assays, chromatin immunoprecipitation, RNA methylation immunoprecipitation, and mutagenesis studies. OGDHL mRNA and protein levels were significantly downregulated in ccRCC tissues. Upregulation of OGDHL expression effectively inhibited ccRCC growth and metastasis both in vitro and in vivo. Furthermore, FTO-mediated OGDHL m6A demethylation suppressed its expression in ccRCC. Mechanistically, low levels of OGDHL promoted TFAP2A expression by inhibiting ubiquitination levels, which then bound to the FASN promoter region and transcriptionally activated FASN expression, thereby promoting lipid accumulation and ERK pathway activation. Our findings demonstrate the impact of OGDHL on ccRCC progression and highlight the role of the FTO/OGDHL/TFAP2A/FASN axis in regulating ccRCC lipid metabolism and progression, providing new targets for ccRCC therapy.

A new molecular subclassification and in silico predictions for diagnosis and prognosis of papillary thyroid cancer by alternative splicing profile

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets. Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively. Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents. Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.

A comprehensive analysis focusing on cuproptosis to investigate its clinical and biological relevance in uterine corpus endometrial carcinoma and its potential in indicating prognosis

Cuproptosis, a novel copper-dependent cell death involving mitochondrial respiration, is distinct from other known death mechanisms, which inspires us to study further in uterine corpus endometrial carcinoma (UCEC). Herein, leveraging comprehensive data from TCGA-UCEC, we conducted transcriptional and genetic analyses of 13 recently identified cuproptosis genes. We discovered severe genetic instability of cuproptosis genes, extensive positive correlations among those genes with each other at the mRNA level, and their involvement in oncogenic pathways in UCEC samples. Next, WGCNA was performed to identify a potential module regulating cuproptosis, in which the hub genes, in addition to 13 cuproptosis genes, were drawn to construct a scoring system termed Cu. Score. Furthermore, its clinical and biological relevance and tumor immune landscape, genetic alterations, as well as predicted sensitivity of chemotherapy drugs in different Cu. Score subgroups had been discussed extensively and in detail. Additionally, univariate Cox and LASSO regression were performed to identify 13 cuproptosis-related prognostic genes to establish a prognostic signature, the Risk. Score. Integrating the Risk. Score and clinical parameters, we established a nomogram with excellent performance to predict the 1-/3-/5-year survival probabilities of UCEC patients. To conclude, we conducted a comprehensive analysis encompassing cuproptosis and developed a cuproptosis scoring system and a prognostic prediction model for UCEC, which may offer help with individualized assessment and treatment for UCEC patients from the perspective of a novel death mechanism.

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.

A novel signature to predict thyroid cancer prognosis and immune landscape using immune-related LncRNA pairs

BACKGROUND

Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. Therefore, identifying accurate prognostic predictions to stratify TC patients is important.

METHODS

Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. We further used qRT‒PCR analysis to validate the expression levels of irlncRNAs in the model. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, immune status, ICI-related molecules, and small-molecule inhibitor efficacy.

RESULTS

We identified 14 DEirlncRNA pairs as the novel predictive signature. In addition, the qRT‒PCR results were consistent with the bioinformatics results obtained from the TCGA dataset. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature could predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, immune status was significantly higher in low-risk groups. Several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further discovered that our new signature was correlated with the clinical response to small-molecule inhibitors, such as sunitinib.

CONCLUSIONS

We have constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC's overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

Targeting 2-oxoglutarate dehydrogenase for cancer treatment

Tricarboxylic acid (TCA) cycle, also called Krebs cycle or citric acid cycle, is an amphoteric pathway, contributing to catabolic degradation and anaplerotic reactions to supply precursors for macromolecule biosynthesis. Oxoglutarate dehydrogenase complex (OGDHc, also called α-ketoglutarate dehydrogenase) a highly regulated enzyme in TCA cycle, converts α-ketoglutarate (αKG) to succinyl-Coenzyme A in accompany with NADH generation for ATP generation through oxidative phosphorylation. The step collaborates with glutaminolysis at an intersectional point to govern αKG levels for energy production, nucleotide and amino acid syntheses, and the resources for macromolecule synthesis in cancer cells with rapid proliferation. Despite being a flavoenzyme susceptible to electron leakage contributing to mitochondrial reactive oxygen species (ROS) production, OGDHc is highly sensitive to peroxides such as HNE (4-hydroxy-2-nonenal) and moreover, its activity mediates the activation of several antioxidant pathways. The characteristics endow OGDHc as a critical redox sensor in mitochondria. Accumulating evidences suggest that dysregulation of OGDHc impairs cellular redox homeostasis and disturbs substrate fluxes, leading to a buildup of oncometabolites along the pathogenesis and development of cancers. In this review, we describe molecular interactions, regulation of OGDHc expression and activity and its relationships with diseases, specifically focusing on cancers. In the end, we discuss the potential of OGDHs as a therapeutic target for cancer treatment.

Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer

Background

Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten womens lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets.

Methods

Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates.

Results

Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression.

Conclusion

These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01965-1.