Yunhong Tian

Background: The TRIM32 has been implicated in tumorigenesis across various cancers; however, its functional significance in head and neck squamous cell carcinoma (HNSCC) requires systematic investigation. This study sought to explore the expression and biological function of TRIM32 in HNSCC tissues to identify new targets or biomarkers for HNSCC diagnosis and treatment. Methods: HNSCC samples were extracted for TRIM32 expression profiling, with subsequent integration of clinical samples for validation. Gene Set Enrichment Analysis (GSEA) was performed using the c2.cp.kegg.v7.4.symbols.gmt gene set. Immune cell infiltration was evaluated using the ESTIMATE algorithm. Following TRIM32 knockdown via small interfering RNA (siRNA) in HNSCC cell lines (HSC-3, FADU), proliferation and invasion capacities were assessed using Cell Counting Kit-8 (CCK-8) and Transwell assays, respectively. Western blotting was conducted to analyse protein expression within the TRIM32-p53-LAMP1/2-LC3B pathway. Results: Integrated bioinformatics analysis and clinical sample validation revealed significantly elevated TRIM32 expression in HNSCC, correlating with poor patient prognosis. GSEA demonstrated significant enrichment of autophagy and p53 signalling pathways within the TRIM32 high-expression group. In vitro experiments confirmed that TRIM32 silencing suppressed proliferation and invasion capacities in HSC-3 and FADU cell lines. Western blotting further delineated that TRIM32 regulates autophagic flux through the TRIM32-p53-LAMP1/2-LC3B axis. The ESTIMATE algorithm indicated a significant association between TRIM32 expression and immune cell infiltration, suggesting a potential role in remodelling the tumour immune microenvironment. Conclusions: TRIM32 expression is significantly elevated in HNSCC, indicating its potential as an adverse prognostic marker. Experimental evidence demonstrates that TRIM32 facilitates cellular proliferation and migration, significantly influences lysosomal function and autophagy processes within HNSCC cells, and is verified to negatively regulate tumour protein 53 (TP53). These mechanisms contribute to the aggressive behaviour of HNSCC.

Hepatocellular carcinoma (HCC) is highly heterogeneous with unpredictable outcomes. Mitochondrial dysfunction drives HCC progression, but its prognostic implications remain unclear. This study aimed to develop a mitochondrial-related prognostic model for HCC. We analyzed transcriptomic data from 370 HCC patients from The Cancer Genome Atlas (TCGA) using consensus clustering of mitochondrial pathway genes. A mitochondrial-associated scoring model (MASM) was developed via least absolute shrinkage and selection operator (LASSO) and Cox regression, validated across independent cohorts [GSE116174 and the International Cancer Genome Consortium Liver Cancer-RIKEN, Japan (ICGC-LIRI)]. Functional roles of MRPS15, a key gene in the model, were investigated through in vitro experiments and subcutaneous tumor formation experiment in nude mice. Consensus clustering identified three mitochondrial subtypes with distinct metabolic profiles: subtype A exhibited elevated oxidative phosphorylation (OXPHOS) and preserved metabolic homeostasis; subtype B showed adaptive metabolic reprogramming; subtype C displayed selective activation of biosynthetic pathways despite widespread mitochondrial dysfunction. The MASM prognostic signature, comprising eight mitochondrial genes, achieved predictive accuracy with area under the curve (AUC) values of 0.805, 0.751, and 0.742 for 1-, 3-, and 5-year survival, respectively, and showed favorable trends compared with clinical staging. MRPS15 was validated as a critical regulator of HCC cell proliferation, invasion, migration, and apoptosis, primarily through sustaining OXPHOS and redox homeostasis. This study establishes a mitochondrial-based classification and eight-gene MASM model for HCC, which stratifies patients into three subtypes with distinct metabolic, genomic, and immune profiles. The model outperforms traditional staging in survival prediction. MRPS15 is validated as a key oncogenic driver sustaining OXPHOS. These findings provide a framework for personalized therapy and warrant prospective clinical validation.

Background: The systemic inflammation score (SIS), based on serum albumin (Alb) and lymphocyte-to-monocyte ratio (LMR), is a novel prognostic tool for some tumours. Studies indicate that the SIS can be used as a postoperative prognostic marker. However, its predictive value in elderly oesophageal squamous cell carcinoma (ESCC) patients treated with radiotherapy is unclear. Methods: In total, 166 elderly ESCC patients who received radiotherapy with or without chemotherapy were included. Based on different combinations of Alb and LMR levels, the SIS was divided into 3 groups, SIS = 0 (n = 79), SIS = 1 (n = 71) and SIS = 2 (n = 16). The Kaplan—Meier method was used for survival analysis. Univariate and multivariate analyses were performed to assess prognosis. Time-dependent receiver operating characteristic (t-ROC) curves were used to compare the prognostic accuracy of the SIS with that of Alb, LMR, neutrophil-to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII). Results: Decreased Alb and LMR were both associated with shorter OS, whereas a lower SIS was significantly associated with better outcomes. The OS of SIS = 0, SIS = 1 and SIS = 2 was 28.0 ± 2.9, 16.0 ± 2.8 and 10.0 ± 7.0 months, respectively (p = 0.000). Similar results were also observed for PFS. Multivariate analysis of the model with SIS revealed that the SIS was a significant independent biomarker for predicting OS and PFS. The nomogram showed that the C-index was improved to 0.677 when the SIS factor was incorporated. Furthermore, the 3-year OS rates for patients in the SIS-high group (SIS = 1 and SIS = 2) undergoing concurrent radiotherapy with a single agent (CCRT-1) and concurrent radiotherapy with two agents (CCRT-2) were 42% and 15%, respectively (p = 0.039). The t-ROC curve showed that the SIS was more sensitive than other prognostic factors for predicting overall survival. Conclusion: The SIS may be a useful prognostic marker in elderly patients with ESCC receiving radiotherapy alone or chemoradiotherapy. The SIS showed a better predictive ability for OS than the continuous variable Alb and could stratify patient prognosis in different therapeutic regimens. CCRT-1 may be the best treatment for SIS-high patients.

Background: Inhibitors of programmed cell death 1 (PD-1)/programmed cell death ligand 1(PD-L1) checkpoint have been approved for metastatic triple negative breast cancer (mTNBC) in patients positive for PD-L1 expression. Negative results from the recent phase III trials (IMPassion131 and IMPassion132) have raises questions on the efficacy of PD-1/PD-L1 checkpoint inhibitors and the predictive value of PD-L1 expression. Here we attempt to systematically analyze the biomarker value of PD-L1 expression for predicting the response of PD-1/PD-L1 checkpoint inhibitors in mTNBC.Materials and methods: PubMed database was searched until Dec 2021 for studies evaluating PD-1/PD-L1 checkpoint inhibitors plus/minus chemotherapy in mTNBC. Outcome of interest included objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Review Manager (RevMan) version 5.4. was used for data-analysis.Results: In total, 20 clinical trials comprising 3962 mTNBC patients (ICT: 2665 (67%); CT: 1297 (33%) were included in this study. Overall ORR was 22% (95%CI, 14-30%) and significant improvement was observed for PD-L1+ patients (ORR 1.78 [95%CI, 1.45-2.19], p2 = 0%; 2-year OS: (ORR 2.47 [95%CI, 1.30-4.69], p=0.006; I^{2 =} 63%). Subgroup analysis indicated that PD-L1 expression can successfully predict tumor response and 2-year OS benefit in mTNBC patients regardless of the type of investigating agent, line of treatment administration, and to some extent the type of treatment. Biomarker ability of PD-L1 expression to predict 1-year PFS was slightly better with pembrolizumab (p=0.09) than atezolizumab (p=0.18), and significantly better when treatment was administered in the first-line setting (OR 1.38 [95%CI, 1.02-1.87], p=0.04) and chemotherapy was added (OR 1.38 [95%CI, 1.02-1.86], p=0.03). Immune-related toxicity of any grade and grade≥3 was 39% (95%CI, 26%-52%) and 10% (95%CI, 8%-13%), respectively.Conclusions: PD-L1 expression can predict objective response rate and 2-year OS in mTNBC patients receiving PD-1/PD-L1 checkpoint inhibitors. One-year PFS is also predicted in selected patients. PD-L1 expression can be a useful biomarker of efficacy of PD-1/PD-L1 checkpoint inhibitors in mTNBC.

Background: This study sought to examine the expression and mutation status of fibroblast growth factor receptor 3 (FGFR3) in non-small cell lung cancer (NSCLC) tissues and explore the prognostic potential of FGFR3 in NSCLC. Methods: Immunohistochemistry (IHC) was used to evaluate the FGFR3 protein expression of 116 NSCLC tissues. Sanger sequencing was used to examine the mutation status of exons 7, 10, and 15 in FGFR3. A Kaplan‑Meier survival analysis was conducted to evaluate the association between the expression level of FGFR3 and the overall survival (OS) and disease-free survival (DFS) of NSCLC patients. Univariate and multivariate Cox analyses were conducted to examine the association between the risk score and clinical features. Results: FGFR3 was immunoreactive in 26 of the 86 NSCLC cases. Further, FGFR3 was positively expressed in 84.6% of the lung adenocarcinoma (AC) cases and 15.4% of the lung squamous cell carcinoma (SCC) cases. FGFR3 mutations were detected in 2 NSCLC patients (2/72, 2.8%), who both harbored the T450M mutation, a novel mutation in exon 10 of FGFR3. In NSCLC, a high expression of FGFR3 was positively correlated with gender, smoking, histology type, T stage, and the epidermal growth factor receptor (EGFR) mutation (PConclusions: This study showed that FGFR3 was highly expressed in NSCLC tissues, and the frequency rate for the FGFR3 mutation at T450 M in NSCLC tissues was low. The survival analysis suggested that FGFR3 may be a useful prognostic biomarker in NSCLC.

Background and aim: Pyroptosis is an inflammatory form of programmed cell death implicated in inflammation and disease. Moreover, inducing pyroptosis has been appreciated as anti-cancer therapy for its ability to unleash anti-cancer immune responses.Methods: Utilizing the data available in The Cancer Genome Atlas (TCGA), pyroptosis-related genes’ (PRGs) expression, genomic aberrations, and clinical significance were systematically analyzed in pan-cancer. A GSVA score was obtained to rate pyroptosis level and divide the cancers into pyroptosis-low and pyroptosis-high groups. Immunohistochemistry (IHC) was used to evaluate the differential expression of major PRGs (GSDMC, GSDMD, GSDME, NLRP3, NLRC4, IL1B) in selected tumor types (COAD, HNSC, KIRC, LIHC, LUAD, LUSC). Selection of tumors for immunohistochemistry (IHC) was based on their expression pattern in TCGA cancers, clinical relevance, tumor epidemiology, and sample availability.Results: Differential expression of PRGs was evident in various cancers and associated with prognosis which was driven by genomic variations and epigenetic abnormalities, such as single nucleotide variations (SNVs), copy number variation (CNV) and DNA methylation level. For example, methylation of PRGs in lower grade glioma (LGG), uveal melanoma (UVM) and kidney renal clear cell carcinoma (KIRC) were predictive of improved survival as upregulation of PRGs was risky in these cancers. Pyroptosis level significantly differentiated tumor from normal samples in 15 types of cancers, exhibited a progressive trend with cancer stage, observed variation among cancer subtypes, and showed a significant association with cancer prognosis. Higher pyroptosis level was associated with worst prognosis in majority of the cancers in terms of OS (KIRC, LGG, and UVM), PFS (GBM, KIRC, LGG, PRAD, THCA, and THYM) and DSS (KIRC and LGG) as estimated by Kaplan-Meier survival curves. Moreover, Pyroptosis level was strongly indicative of a hot tumor immune microenvironment with high presence of CD8+ T cell and other T cell subtypes. Several oncogenic pathways, such as P53 pathway, DNA repair, KRAS signaling, epithelial-mesenchymal transition (EMT), IL6 JAK STAT3 signaling, IL2 STAT5 signaling, PI3K AKT MTOR signaling and angiogenesis, were enriched in pyroptosis-hi subgroups across cancers.Conclusions: Genetic alterations in PRGs greatly influence the pyroptosis level and cancer prognosis. A relatively hot tumor immune microenvironment was associated with pyroptosis irrespective of the cancer prognosis. Overall, our study reveals the critical role of pyroptosis in cancer and highlights pyroptosis-based therapeutic vulnerabilities.

Background: In the era of immunotherapy, predictive or prognostic biomarkers for head and neck squamous cell carcinoma (HNSCC) are urgently needed. Metabolic reprogramming in the tumor microenvironment (TME) is a non-negligible reason for the low therapeutic response to immune checkpoint inhibitor (ICI) therapy. We aimed to construct a metabolism-related gene prognostic index (MRGPI) for HNSCC bridging metabolic characteristics and antitumor immune cycling and identified the immunophenotype, genetic alternations, potential targeted inhibitors, and the benefit of immunotherapy in MRGPI-defined subgroups of HNSCC.Methods: Based on The Cancer Genome Atlas (TCGA) HNSCC dataset (n = 502), metabolism-related hub genes were identified by the weighted gene co-expression network analysis (WGCNA). Seven genes were identified to construct the MRGPI by using the Cox regression method and validated with an HNSCC dataset (n = 270) from the Gene Expression Omnibus (GEO) database. Afterward, the prognostic value, metabolic activities, genetic alternations, gene set enrichment analysis (GSEA), immunophenotype, Connectivity map (cMAP), and benefit of immunotherapy in MRGPI-defined subgroups were analyzed.Results: The MRGPI was constructed based on HPRT1, AGPAT4, AMY2B, ACADL, CKM, PLA2G2D, and ADA. Patients in the low-MRGPI group had better overall survival than those in the high-MRGPI group, consistent with the results in the GEO cohort (cutoff value = 1.01). Patients with a low MRGPI score display lower metabolic activities and an active antitumor immunity status and more benefit from immunotherapy. In contrast, a higher MRGPI score was correlated with higher metabolic activities, more TP53 mutation rate, lower antitumor immunity ability, an immunosuppressive TME, and less benefit from immunotherapy.Conclusion: The MRGPI is a promising indicator to distinguish the prognosis, the metabolic, molecular, and immune phenotype, and the benefit from immunotherapy in HNSCC.

The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification and the personalized management of patients with LGG, there is an urgent need to develop an accurate risk prediction model. The TCGA-LGG dataset, downloaded from The Cancer Genome Atlas (TCGA) portal, was used as a training cohort, and the Chinese Glioma Genome Atlas (CGGA) dataset and Rembrandt dataset were used as validation cohorts. The levels of various cancer hallmarks were quantified, which identified glycolysis as the primary overall survival-related risk factor in LGGs. Furthermore, using various bioinformatic and statistical methods, we developed a strong glycolysis-related gene signature to predict prognosis. Gene set enrichment analysis showed that in our model, high-risk glioma correlated with the chemoradiotherapy resistance and poor survival. Moreover, based on established risk model and other clinical features, a decision tree and a nomogram were built, which could serve as useful tools in the diagnosis and treatment of LGGs. This study indicates that the glycolysis-related gene signature could distinguish high-risk and low‐risk patients precisely, and thus can be used as an independent clinical feature.

Background: The discovery of reliable cancer biomarkers could tune a diagnosis and improve the way patients are treated. However, many cancers lack robust biomarkers. C1QTNF6 has been preliminarily elucidated for its role in some tumors. However, no pan-cancer analysis has been performed to comprehensively explore the value of C1QTNF6.Methods: Data from the TCGA database, GTEx database stored in the USUC Xena were used for analyzing the profiles of C1QTNF6 expression in normal and tumor tissues in pan-cancer. Subsequently, the gene alteration rates of C1QTNF6 were acquired on the online web cBioportal. With the aid of the TCGA data, the association between C1QTNF6 mRNA expression and copy number alterations (CNA) and methylation was determined. Survival analyses of C1QTNF6 were carried out. Moreover, the tumor biological and immunological characteristics of C1QTNF6 were clarified in the forms of the correlation between C1QTNF6 expression and hallmark Pathway scores in MsigDB database, immune cell infiltration, immune-related genes. We conducted a GSEA of C1QTNF6 to illustrate its potential biological functions. In addition, GDSC2 data with 198 drugs were adopted to explore drug sensitivity with the change of C1QTNF6 expression.Result:C1QTNF6 was overexpressed in many types of cancer, Survival analysis showed that C1QTNF6 independently served as a prognostic indicator for poor survival in many tumors. Besides, we also identified a positive correlation between C1QTNF6 and cancer hallmark pathway score, tumor microenvironment related pathways score (TMEp score), and immune characteristic. In terms of drug sensitivity analysis, we found higher expression level of C1QTNF6 predicts a high IC50 value for most of 198 drugs which predicts drug resistance.Conclusions: Our study provides a new biological marker for pan-cancer, which is beneficial to the diagnosis and treatment of cancer, which bring a new therapeutic target for tumors.