Xenotropic and polytropic retrovirus receptor 1 (XPR1) is the only known transporter associated with Pi efflux in mammals, and its impact on tumor progression is gradually being revealed. However, the role of XPR1 in hepatocellular carcinoma (HCC) is unknown. A bioinformatics screen for the phosphate exporter XPR1 was performed in HCC patients. The expression of XPR1 in clinical specimens was analyzed using quantitative real-time PCR, Western blot analysis, and immunohistochemical assays. Knockdown of the phosphate exporter XPR1 was performed by shRNA transfection to investigate the cellular phenotype and phosphate-related cytotoxicity of the Huh7 and HLF cell lines. In vivo tests were conducted to investigate the tumorigenicity of HCC cells xenografted into immunocompromised mice after silencing XPR1. Compared with that in paracancerous tissue, XPR1 expression in HCC tissues was markedly upregulated. High XPR1 expression significantly correlated with poor patient survival. Silencing of XPR1 leads to decreased proliferation, migration, invasion, and colony formation in HCC cells. Mechanistically, knockdown of XPR1 causes an increase in intracellular phosphate levels; mitochondrial dysfunction characterized by reduced mitochondrial membrane potential and adenosine triphosphate levels; increased reactive oxygen species levels; abnormal mitochondrial morphology; and downregulation of key mitochondrial fusion, fission, and inner membrane genes. This ultimately results in mitochondria-dependent apoptosis. These findings reveal the prognostic value of XPR1 in HCC progression and, more importantly, suggest that XPR1 might be a potential therapeutic target.

BACKGROUND: The unfolded protein response (UPR) is associated with immune cells that regulate the biological behavior of tumors. This article aims to combine UPR-associated genes with immune cells to find a prognostic marker and to verify its connection to the UPR.
METHODS: Univariate cox analysis was used to screen prognostically relevant UPRs and further screened for key UPRs among them by machine learning. ssGSEA was used to calculate immune cell abundance. Univariate cox analysis was used to screen for prognostically relevant immune cells. Multivariate cox analysis was used to calculate UPR_score and Tumor Immune Microenvironment score (TIME_score). WGCNA was used to screen UPR-Immune-related (UI-related) genes. Consensus clustering analysis was used to classify patients into molecular subtype. Based on the UI-related genes, we classified colon adenocarcinoma (COAD) samples by cluster analysis. Single-cell analysis was used to analyze the role of UI-related genes. We detected the function of TIMP1 by cell counting and transwell. Immunoblotting was used to detect whether TIMP1 was regulated by key UPR genes.
RESULTS: Combined UPR-related genes and immune cells can determine the prognosis of COAD patients. Cluster analysis showed that UI-related genes were associated with clinical features of COAD. Single-cell analysis revealed that UI-related genes may act through stromal cells. We defined three key UI-related genes by machine learning algorithms. Finally, we found that TIMP1, regulated by key genes of UPR, promoted colon cancer proliferation and metastasis.
CONCLUSIONS: We found that TIMP1 was a prognostic marker and experimentally confirmed that TIMP1 was regulated by key genes of UPR.

A macroscopic perspective is indispensable for understanding the intricate relationship between deubiquitinases and tumorigenesis. Proteomics has been proposed as a viable approach for elucidating the complex role of deubiquitylation in cellular progression. Instead of studying the function of a single ubiquitinase, research on a deubiquitinase family with similar catalytic core(s) may provide a new perspective for the pathological understanding of cancer. The Ubiquitin C-terminal hydrolase L (UCHL) family consists of four members: UCHL1, UCHL3, UCHL5, and BRAC1 associated protein-1 (BAP1), and they have been implicated in tumorigenesis and metastasis. Some members are considered hallmarks of intracranial lesions, colon cancer, chromatin remodeling, and histone stability. The present study uncovered an unknown correlation between the UCHL family and renal cancer. We discovered that UCHLs exhibit diverse regulatory effects in renal cancer, establishing connections between the renal cancer and truncated gene mutations, mitochondrial energetic metastasis, immune cell infiltration, and chromosomal stability of UCHLs family. Notably, we found that the increase of UCHL5 expression in renal cancer cells decreases the antigen processing and presentation of RCC tumor-infiltrating B cells. Further research identified that the expression of UCHL5 in RCC tumors is correlated with transport proteins, which led us to find that the abundance of UCHL5 in the blood of late-stage renal cell cancer patients is upregulated from 18 ng/L to 500 ng/L. Therefore, we propose that the abundance of UCHL5 in patients\' blood can be a possible indicator of poor prognosis for renal cell cancer.

UNASSIGNED: Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.
UNASSIGNED: We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.
UNASSIGNED: In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.
UNASSIGNED: The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

In recent years, C2ORF40 has been identified as a tumor suppressor gene with multiple functions, including roles in cell proliferation, migration, and senescence. To explore the role of the C2ORF40 gene in different tumors, we used multiple databases for analysis. Compared to adjacent normal tissues, C2ORF40 is downregulated in a variety of malignant tumors, including tumors such as breast cancer, colorectal cancer, bladder cancer, hepatocellular carcinoma and prostate cancer. Notably, low expression of the gene is significantly associated with poor overall survival and relapse-free survival rates. In specific cancers including colon cancer and prostate cancer, the expression of C2ORF40 is correlated with the infiltration of CAFs. C2ORF40 is also involved in biological processes such as cell apoptosis and regulation of protein stability. In conclusion, C2ORF40 can hold promise as a prognostic marker for pan-cancer analysis.

With the development of gene testing technology, we have found many different genes, and lncRNA is one of them. LncRNAs refer to a non-protein coding RNA molecule with a length of more than 200bp, which is one of the focuses of research on human malignant diseases such as LUAD. LncRNAs act as an oncogene or inhibitor to regulate the occurrence and progression of tumors. The differential expression of LncRNAs promotes or inhibits the progression of lung adenocarcinoma by affecting cell proliferation, metastasis, invasion, and apoptosis, thus affecting the prognosis and survival rate of patients. Therefore, LncRNAs can be used as a potential target for diagnosis and treatment of cancer. The early diagnosis of the disease was made through the detection of tumor markers. Because lung adenocarcinoma is not easy to diagnose in the early stage and tumor markers are easy to ignore, LncRNAs play an important role in the diagnosis and treatment of lung adenocarcinoma. The main purpose of this article is to summarize the known effects of LncRNAs on lung adenocarcinoma, the effect of differential expression of LncRNAs on the progression of lung adenocarcinoma, and related signal transduction pathways. And to provide a new idea for the future research of lung adenocarcinoma-related LncRNAs.

Cholangiocarcinoma (CCA) is widely noted for its high degree of malignancy, rapid progression, and limited therapeutic options. This study was carried out on transcriptome data of 417 CCA samples from different anatomical locations. The effects of lipid metabolism related genes and immune related genes as CCA classifiers were compared. Key genes were derived from MVI subtypes and better molecular subtypes. Pathways such as epithelial mesenchymal transition (EMT) and cell cycle were significantly activated in MVI-positive group. CCA patients were classified into three (four) subtypes based on lipid metabolism (immune) related genes, with better prognosis observed in lipid metabolism-C1, immune-C2, and immune-C4. IPTW analysis found that the prognosis of lipid metabolism-C1 was significantly better than that of lipid metabolism-C2 + C3 before and after correction. KRT16 was finally selected as the key gene. And knockdown of KRT16 inhibited proliferation, migration and invasion of CCA cells.

Aim: Our goal was to explore the prognostic value of the neutrophil-to-hemoglobin ratio (NHR) in HBV-related decompensated cirrhosis (HBV-DC) patients. Methods: 172 HBV-DC patients were enrolled. Multivariate analyses were used to identify risk factors influencing 30-day mortality. Results: The 30-day mortality was 12.8% (22/172). nonsurvivors exhibited a higher NHR than survivors. On multivariate analysis, NHR and model for end-stage liver disease (MELD) score were the only independent predictors of mortality. Notably, the predictive capabilities of NHR were found to be comparable to those of the MELD score. Conclusion: High NHR was associated with poor prognosis in HBV-DC patients, and NHR can serve as an effective and readily available indicator for the prediction of mortality in these patients.
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Metabolic reprogramming is widely recognized as a hallmark of malignant tumors, and the targeting of metabolism has emerged as an appealing approach for cancer treatment. Mitochondria, as pivotal organelles, play a crucial role in the metabolic regulation of tumor cells, and their morphological and functional alterations are intricately linked to the biological characteristics of tumors. As a key regulatory subunit of mitochondria, mitochondrial inner membrane protein (IMMT), plays a vital role in degenerative diseases, but its role in tumor is almost unknown. The objective of this research was to investigate the roles that IMMT play in the development and progression of breast cancer (BC), as well as to elucidate the underlying biological mechanisms that drive these effects. In this study, it was confirmed that the expression of IMMT in BC tissues was significantly higher than that in normal tissues. The analysis of The Cancer Genome Atlas (TCGA) database revealed that IMMT can serve as an independent prognostic factor for BC patients. Additionally, verification in clinical specimens of BC demonstrated a positive association between high IMMT expression and larger tumor size (> 2 cm), Ki-67 expression (> 15%), and HER-2 status. Furthermore, in vitro experiments have substantiated that the suppression of IMMT expression resulted in a reduction in cell proliferation and alterations in mitochondrial cristae, concomitant with the liberation of cytochrome c, but it did not elicit mitochondrial apoptosis. Through Gene Set Enrichment Analysis (GSEA) analysis, we have predicted the associated metabolic genes and discovered that IMMT potentially modulates the advancement of BC through its interaction with 16 metabolic-related genes, and the changes in glycolysis related pathways have been validated in BC cell lines after IMMT inhibition. Consequently, this investigation furnishes compelling evidence supporting the classification of IMMT as prognostic marker in BC, and underscoring its prospective utility as a novel target for metabolic therapy.

Gliomas are highly malignant and invasive tumors lacking clear boundaries. Previous bioinformatics and experimental analyses have indicated that F-box and leucine-rich repeat protein 6 (FBXL6), a protein crucial for the cell cycle and tumorigenesis, is highly expressed in certain types of tumors. The high expression level of FBXL6 is reported to promote tumor growth and adversely affect patient survival. However, the molecular mechanism, prognostic value and drug sensitivity of FBXL6 in glioma remain unclear. To address this, the present study analyzed FBXL6 expression in gliomas, utilizing data from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Analysis of FBXL6 mRNA expression levels, combined with patient factors such as age, sex and tumor grade using Kaplan-Meier plots and nomograms, demonstrated a strong correlation between FBXL6 expression and glioma progression. Co-expression networks provided further insights into the biological function of FBXL6. Additionally, using CIBERSORT and TISDB tools, the correlation between FBXL6 expression correlation tumor-infiltrating immune cells and immune genes was demonstrated to be statistically significant. These findings were validated by examining FBXL6 mRNA and protein levels in glioma tissues using various techniques, including western blot, reverse transcription-quantitative PCR and immunohistochemistry. These assays demonstrated the role of FBXL6 in glioma progression. Furthermore, drug sensitivity analysis demonstrated a strong correlation between FBXL6 expression and various drugs, which indicated that FBXL6 may potentially act as a future promising therapeutic target in glioma treatment. Therefore, the present study identified FBXL6 as a diagnostic and prognostic marker in patients with gliomas and highlighted its potential role in glioma progression.