The high mobility group box (HMGB) protein family consists of four members: HMGB1, 2, 3, and 4. They share similar amino acid sequences and identical functional regions, especially HMGB1, 2, and 3. The homology in structure may lead to similarity in function. In fact, though their targets may be different, they all possess the fundamental function of binding and distorting target DNAs. However, further research confirmed they are distributed differently in tissues and involved in various distinct physiological and pathological cellular processes, including cell proliferation, division, migration, and differentiation. Recently, the roles of HMGB family members in carcinogenesis has been widely investigated; however, systematic discussion on their functions and clinical values in malignant tumors is limited. In this review, we mainly review and summarize recent advances in knowledge of HMGB family members in terms of structure, distribution, biochemical cascades, and specific mechanisms regarding tumor progression. Importantly, the diagnostic, prognostic, and therapeutic value of these proteins in cancers is discussed. Finally, we envisage the orientation and challenges of this field in further studies.

Background: Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC. Methods: Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature. Results: Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively. Conclusion: These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Background: The emergence of robotic surgical systems compensated for the technological shortcomings of laparoscopic approaches. However, whether robotic gastrectomy (RG) has better perioperative outcomes and survival than laparoscopic gastrectomy (LG) for gastric cancer (GC) is still unclear but increasingly drawing attention. Materials and methods: In this systematic review and meta-analysis, we searched PubMed, EMBASE, Web of Science, and Cochrane Library as of 20 January 2024, and referenced a list of eligible articles for all published studies comparing RG and LG for patients with GC. Data on study characteristics, individual characteristics, and outcome parameters were extracted. The quality of studies was assessed using the Revised Cochrane risk-of-bias 2 tool and the risk of bias in nonrandomized studies of interventions tool. The main outcome measures were overall survival (OS) and disease-free survival (DFS). Results: The authors identified 3641 articles, of which 72 studies (30 081 patients) were included in the meta-analysis. Compared with LG, RG was associated with higher OS [hazard ratio (HR)=0.89, 95% CI=0.83–0.96], lower rate of overall postoperative complications [odds ratio (OR)=0.77, 95% CI=0.71–0.84], longer operating time [mean difference (MD)=35.53, 95% CI=29.23–41.83], less estimated blood loss (MD=−37.45, 95% CI=−46.24 to −28.67), a higher number of retrieved lymph nodes (MD=1.88, 95% CI=0.77–3.00), faster postoperative recovery, and lower rate of conversion (OR=0.44, 95% CI=0.36–0.55). Mortality and DFS were not significantly different between the two groups. The subgroup of meta-analysis results also showed the advantages of robotic surgery over laparoscopic surgery in intracorporeal reconstruction, total gastrectomy, Ⅰ/Ⅱ stage, and BMI≥25, especially for patients with stage Ⅰ/Ⅱ, there are better OS and DFS. Conclusion: Our findings point to robotic surgery having great benefits compared with laparoscopic surgery in GC. Our study may help inform decision-making in applying robotic surgical systems to clinical treatment.

Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC. mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein–protein interaction (PPI) network, and miRNA–gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC. Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC. In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.