Influence of Immunoexpression of Mismatch Repair Complex Proteins on Disease-Free Survival in Non-Surgically Treated Oropharyngeal Squamous Cell Carcinomas

OBJECTIVE

To evaluate the influence of MMR complex protein immunoexpression on disease-free survival in oropharyngeal SCC treated non-surgically.

MATERIALS AND METHODS

85 cases of oropharyngeal SCC diagnosed and treated at the Ceará Cancer Institute were surveyed, from which clinical-pathological data and paraffin blocks of incisional biopsies were retrieved for immunohistochemical reaction for MSH2, MSH6, PMS2, MLH1 and p16. Disease-free survival was calculated and Kruskal-Wallis and Friedman/Dunn tests, chi-square and Fisher's exact, Log-Rank Mantel Cox and Cox regression were performed.

RESULTS

In p16- tumors, loss of MSH2 expression was associated with shorter disease-free survival (p = 0.035) and mean MSH6 expression was significantly higher than MSH2 (p = 0.001). Loss of MSH2 expression in p16 + tumors was associated with longer disease-free survival compared to p16- tumors. Imbalance in the MSH6/MSH2 ratio in p16 + tumors was associated with longer survival compared to p16- tumors. MLH1/PMS2 imbalance was significantly higher in p16 + with recurrence (p = 0.003). Low MSH2 immunoexpression increased the risk of relapse by 9.10 times (CI95% 1.99 to 83.06).

CONCLUSION

Microsatellite instability in oropharyngeal SCC is demonstrated by the association between loss of protein expression and its heterodimer imbalance with disease-free survival. It was demonstrated that the imbalance of the MMR complex can consequently lead to resistance to treatment and a decrease in disease-free survival in p16 + oropharyngeal SCC tumors.

Molecular genetic evidence supporting diverse histogenic origins of germ cell tumors

Germ cell tumors (GCTs) originate during the histogenesis of primordial germ cells to mature gametes. Previous studies identified five histogenic mechanisms in ovarian mature teratomas (type I: failure of meiosis I; type II: failure of meiosis II; type III: duplication of the genome of a mature gamete; type IV: no meiosis; and type V: fusion of two different ova), but those of other GCTs remain elusive. In this study, we analyzed 84 GCTs of various pathologic types to identify the histogenesis using single-nucleotide polymorphism array by analyzing copy-neutral loss of heterozygosity (CN-LOH) and copy number alterations (CNAs). We detected types I and II in ovarian teratomas, type III in ovarian teratomas and yolk sac tumors (YSTs), and type IV in all GCT types. The GCTs with multiple-type histogenesis (I-IV) (ovarian mature/immature teratomas and YST) show meiotic CN-LOH with scant CNAs. Type IV-only GCTs are either with mitotic CN-LOH and abundant CNAs (seminoma, dysgerminoma, testicular mixed GCTs) or with scant CNAs and no CN-LOH (pediatric testicular and mediastinal teratomas). The development sequences of CN-LOH and CNA are different between the multiple type (I-IV) GCTs and type IV-only GCTs. We analyzed two different histologic areas in eight GCTs (one mature teratoma with a mucin-secreting adenoma, two immature teratomas, and five mixed GCTs). We found that GCTs (mature teratoma, immature teratoma, and mixed GCT) showed different genomic alterations between histologic areas, suggesting that genomic differences within a GCT could accompany histologic differentiation. Of note, we found evidence for collision tumors in a mixed GCT. Our data indicate that GCTs may have various histogenesis and intratumoral genomic differences, which might provide important information for the identification of GCTs, especially for those with different histologic areas. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

Background

Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.

Methods

A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).

Results

Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).

Conclusion

MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

Meiotic gene activation in somatic and germ cell tumours

Background

Germ cell tumours are uniquely associated with the gametogenic tissues of males and females. A feature of these cancers is that they can express genes that are normally tightly restricted to meiotic cells. This aberrant gene expression has been used as an indicator that these cancer cells are attempting a programmed germ line event, meiotic entry. However, work in non‐germ cell cancers has also indicated that meiotic genes can become aberrantly activated in a wide range of cancer types and indeed provide functions that serve as oncogenic drivers. Here, we review the activation of meiotic factors in cancers and explore commonalities between meiotic gene activation in germ cell and non‐germ cell cancers.

Objectives

The objectives of this review are to highlight key questions relating to meiotic gene activation in germ cell tumours and to offer possible interpretations as to the biological relevance in this unique cancer type.

Materials and Methods

PubMed and the GEPIA database were searched for papers in English and for cancer gene expression data, respectively.

Results

We provide a brief overview of meiotic progression, with a focus on the unique mechanisms of reductional chromosome segregation in meiosis I. We then offer detailed insight into the role of meiotic chromosome regulators in non‐germ cell cancers and extend this to provide an overview of how this might relate to germ cell tumours.

Conclusions

We propose that meiotic gene activation in germ cell tumours might not indicate an unscheduled attempt to enter a full meiotic programme. Rather, it might simply reflect either aberrant activation of a subset of meiotic genes, with little or no biological relevance, or aberrant activation of a subset of meiotic genes as positive tumour evolutionary/oncogenic drivers. These postulates provide the provocation for further studies in this emerging field.

Elucidation of the developmental mechanism of ovarian mature cystic teratomas using B allele-frequency plots of single nucleotide polymorphism array data

Ovarian mature cystic teratomas (MCTs) originate from post-meiotic germ cells. Conventional methods such as karyotyping or short tandem repeat-polymorphism analysis may be used to better classify MCTs, although such data would be insufficient. The aim of this study was to elucidate the origin of ovarian MCTs using B allele-frequency (BAF) plots of single nucleotide polymorphism array data. MCTs can be classified in terms of the zygosity of the centromeres and distal chromosome regions. We evaluated the zygosity of all chromosomes from 38 MCT specimens using BAF plot data. BAF plots were used to determine the homozygous and heterozygous regions over the whole genome. Theoretically, MCTs originated from the fusion of two ova (previously referred to as type V MCTs) should have a mixed pattern of centromeric zygosity, that is, a combination of heterozygous and homozygous regions in the centromeric regions. However, no MCTs in this study met this criterion. We identified 13 type I MCTs, 14 type II MCTs, and 11 type III MCTs. In addition, BAF plots facilitated the construction of recombination maps at the whole-genome level for type I and II MCTs. No crossover, especially in the short arms, contributed to the failure of meiosis I, resulting in type I MCTs. Crossover in all arms might assure the normal progress of meiosis in human oocytes. In conclusion, our findings indicate that BAF plots can elucidate the developmental mechanism of MCTs, and further serve as useful analytical tools for analyzing human oocyte meiosis, and related aberrations.