Identification of a Solute Carrier Family-Based Signature for Predicting Overall Survival in Osteosarcoma

Combination of transcriptome and Mendelian inheritance reveals novel prognostic biomarker of CTLA-4-related lncRNAs and protective role of nitrogen metabolism pathway in lung adenocarcinoma development

OBJECTIVE

Since in the cancer setting, tumor cells may use cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) to evade the immune system. This study aimed to identify CTLA-4-related long non-coding RNAs (lncRNAs) and assess their roles in lung adenocarcinoma (LUAD) development.

METHODS

Clinical and genomic data were obtained from The Cancer Genome Atlas (TCGA), MSigDB and Gene Weaver. CTLA-4-related lncRNA-based gene signatures (CTLA4LncSigs) were identified using Cox regression, establishing a risk score model and an independent prognostic model. Enrichment analysis (GO/KEGG) was performed. Mendelian randomization (MR) analysis investigated the nitrogen metabolism and lung cancer relationship, with Bayesian weighted MR (BWMR) addressing uncertainties. Correlations with tumor microenvironment and drug sensitivity were explored.

RESULTS

Nineteen CTLA4LncSigs significantly influenced LUAD prognosis. The risk score demonstrated independence as a prognostic factor. Functional analysis revealed lncRNAs' impact on nitrogen metabolism. MR and BWMR confirmed the protective role of the nitrogen metabolism pathway in lung cancer.

CONCLUSION

Our study identifies CTLA-4-related lncRNAs associated with LUAD prognosis and uncovers a previously undiscovered protective role of the nitrogen metabolism pathway in combating LUAD development, providing new insights into potential therapeutic targets and prognostic biomarkers for this aggressive cancer subtype.

Comprehensive analyses of solute carrier family members identify SLC12A2 as a novel therapy target for colorectal cancer

As the largest transporter family impacting on tumor genesis and development, the prognostic value of solute carrier (SLC) members has not been elucidated in colorectal cancer (CRC). We aimed to identify a prognostic signature from the SLC members and comprehensively analyze their roles in CRC. Firstly, we downloaded transcriptome data and clinical information of CRC samples from GEO (GSE39582) and TCGA as training and testing dataset, respectively. We extracted the expression matrix of SLC genes and established a prognostic model by univariate and multivariate Cox regression. Afterwards, the low-risk and high-risk group were identified. Then, the differences of prognosis traits, transcriptome features, clinical characteristics, immune infiltration and drug sensitivity between the two groups were explored. Furthermore, molecular subtyping was also implemented by non-negative matrix factorization (NMF). Finally, we studied the expression of the screened SLC genes in CRC tumor tissues and normal tissues as well as investigated the role of SLC12A2 by loss of function and gain of function. As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2). Both in the training and testing sets, CRC patients in the high-risk group had the poorer prognosis and were in the more advanced pathological stage. What's more, the high-risk group were enriched with CRC progression signatures and immune infiltration. Two groups showed different drug sensitivity. On the other hand, two distinct subclasses (C1 and C2) were identified based on the 6 SLC genes. CRC patients in the high-risk group and C1 subtype had a worse prognosis. Furthermore, we found and validated that SLC12A2 was steadily upregulated in CRC. A loss-of-function study showed that knockdown of SLC12A2 expression restrained proliferation and stemness of CRC cells while a gain-of-function study showed the contrary results. Hence, we provided a 6-SLC gene signature for prognosis prediction of CRC patients. At the same time, we identified that SLC12A2 could promote tumor progression in CRC, which may serve as a potential therapeutic target.

Identification of key pyroptosis-related genes and microimmune environment among peripheral arterial beds in atherosclerotic arteries

Atherosclerosis is a chronic inflammatory disease characterized with innate and adaptive immunity but also involves pyroptosis. Few studies have explored the role of pyroptosis in advanced atherosclerotic plaques from different vascular beds. Here we try to identify the different underlying function of pyroptosis in the progression of atherosclerosis between carotid arteries and femoral. arteries. We extracted gene expression levels from 55 advanced carotid or femoral atherosclerotic plaques. The pyroptosis score of each sample was calculated by single-sample-gene-set enrichment analysis (ssGSEA). We then divided the samples into two clusters: high pyroptosis scores cluster (PyroptosisScoreH cluster) and low pyroptosis scores cluster (PyroptosisScoreL cluster), and assessed functional enrichment and immune cell infiltration in the two clusters. Key pyroptosis related genes were identified by the intersection between results of Cytoscape and LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Finally, all key pyroptosis related genes were validated in vitro. We found all but one of the 29 carotid plaque samples belonged to the PyroptosisScoreH cluster and the majority (19 out of 26) of femoral plaques were part of the PyroptosisScoreL cluster. Atheromatous plaque samples in the PyroptosisScoreL cluster had higher proportions of gamma delta T cells, M2 macrophages, myeloid dendritic cells (DCs), and cytotoxic lymphocytes (CTLs), but lower proportions of endothelial cells (ECs). Immune full-activation pathways (e.g., NOD-like receptor signaling pathway and NF-kappa B signaling pathway) were highly enriched in the PyroptosisScoreH cluster. The key pyroptosis related genes GSDMD, CASP1, NLRC4, AIM2, and IL18 were upregulated in advanced carotid atherosclerotic plaques. We concluded that compared to advanced femoral atheromatous plaques, advanced carotid atheromatous plaques were of higher grade of pyroptosis. GSDMD, CASP1, NLRC4, AIM2, and IL18 were the key pyroptosis related genes, which might provide a new sight in the prevention of fatal strokes in advanced carotid atherosclerosis.

A ferroptosis-related LncRNAs signature for predicting prognoses and screening potential therapeutic drugs in patients with lung adenocarcinoma: A retrospective study

BACKGROUND

Lung adenocarcinoma (LUAD) has a high mortality rate. Ferroptosis is linked to tumor initiation and progression.

AIMS

This study aims to develop prognostic models of ferroptosis-related lncRNAs, evaluate the correlation between differentially expressed genes and tumor microenvironment, and identify prospective drugs for managing LUAD.

METHODS AND RESULTS

In this study, transcriptomic and clinical data were downloaded from the TCGA database, and ferroptosis-related genes were obtained from the FerrDb database. Through correlation analysis, Cox analysis, and the LASSO algorithm for constructing a prognostic model, we found that ferroptosis-related lncRNA-based gene signatures (FLncSig) had a strong prognostic predicting ability in the LUAD patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments reconfirmed that ferroptosis is related to receptor-ligand activity, enzyme inhibitor activity, and the IL-17 signaling pathway. Next, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) algorithms, and pRRophetic were used to predict immunotherapy response and chemotherapy sensitivity. The IMvigor210 cohort was also used to validate the prognostic model. In the tumor microenvironment, Type_II_IFN_Response and HLA were found to be a group of low-risk pathways, while MHC_class_I was a group of high-risk pathways. Patients in the high-risk subgroup had lower TIDE scores. Exclusion, MDSC, CAF, and TAMM2 were significantly and positively correlated with risk scores. In addition, we found 15 potential therapeutic drugs for LUAD. Finally, differential analysis of stemness index based on mRNA expression (mRNAsi) indicated that mRNAsi was correlated with gender, primary tumor (T), distant metastasis (M), and the tumor, node, and metastasis (TNM) stage in LUAD patients.

CONCLUSIONS

In conclusion, the prognostic model based on FLncSig can alleviate the difficulty in predicting the prognosis and immunotherapy of LUAD patients. The identified FLncSig and the screened drugs exhibit potential for clinical application and provide references for the treatment of LUAD.

Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma

BACKGROUND

Osteosarcoma (OS) is the most common primary malignancy of bone tumors. More and more ARHGAP family genes have been confirmed are to the occurrence, development, and invasion of tumors. However, its significance in osteosarcoma remains unclear. In this study, we aimed to identify the relationship between ARHGAP family genes and prognosis in patients with OS.

METHODS

OS samples were retrieved from the TCGA and GEO databases. We then performed LASSO regression analysis and multivariate COX regression analysis to select ARHGAP family genes to construct a risk prognosis model. We then validated this prognostic model. We utilized ESTIMATE and CIBERSORT algorithms to calculate the stroma and immune scores of samples, as well as the proportions of tumor infiltrating immune cells (TICs). Finally, we conducted in vivo and in vitro experiments to investigate the effect of ARHGAP28 on osteosarcoma.

RESULTS

We selected five genes to construct a risk prognosis model. Patients were divided into high- and low-risk groups and the survival time of the high-risk group was lower than that of the low-risk group. The high-risk group in the prognosis model constructed had relatively poor immune function. GSEA and ssGSEA showed that the low-risk group had abundant immune pathway infiltration. The overexpression of ARHGAP28 can inhibit the proliferation, migration, and invasion of osteosarcoma cells and tumor growth in mice, and IHC showed that overexpression of ARHGAP28 could inhibit the proliferation of tumor cells.

CONCLUSION

We constructed a risk prognostic model based on five ARHGAP family genes, which can predict the overall survival of patients with osteosarcoma, to better assist us in clinical decision-making and individualized treatment.

Transcriptional profiling of canine osteosarcoma identifies prognostic gene expression signatures with translational value for humans

Canine osteosarcoma is increasingly recognized as an informative model for human osteosarcoma. Here we show in one of the largest clinically annotated canine osteosarcoma transcriptional datasets that two previously reported, as well as de novo gene signatures devised through single sample Gene Set Enrichment Analysis (ssGSEA), have prognostic utility in both human and canine patients. Shared molecular pathway alterations are seen in immune cell signaling and activation including TH1 and TH2 signaling, interferon signaling, and inflammatory responses. Virtual cell sorting to estimate immune cell populations within canine and human tumors showed similar trends, predominantly for macrophages and CD8+ T cells. Immunohistochemical staining verified the increased presence of immune cells in tumors exhibiting immune gene enrichment. Collectively these findings further validate naturally occurring osteosarcoma of the pet dog as a translationally relevant patient model for humans and improve our understanding of the immunologic and genomic landscape of the disease in both species.

Rethinking our approach to cancer metabolism to deliver patient benefit

Altered cellular metabolism is a major mechanism by which tumours support nutrient consumption associated with increased cellular proliferation. Selective dependency on specific metabolic pathways provides a therapeutic vulnerability that can be targeted in cancer therapy. Anti-metabolites have been used clinically since the 1940s and several agents targeting nucleotide metabolism are now well established as standard of care treatment in a range of indications. However, despite great progress in our understanding of the metabolic requirements of cancer and non-cancer cells within the tumour microenvironment, there has been limited clinical success for novel agents targeting pathways outside of nucleotide metabolism. We believe that there is significant therapeutic potential in targeting metabolic processes within cancer that is yet to be fully realised. However, current approaches to identify novel targets, test novel therapies and select patient populations most likely to benefit are sub-optimal. We highlight recent advances in technologies and understanding that will support the identification and validation of novel targets, re-evaluation of existing targets and design of optimal clinical positioning strategies to deliver patient benefit.