Hyperthermia, the raising of tumor temperature (≥39°C), holds great promise as an adjuvant treatment for cancer therapy. This review focuses on 2 key aspects of hyperthermia: its molecular and cellular effects and its impact on the immune system. Hyperthermia has profound effects on critical biological processes. Increased temperatures inhibit DNA repair enzymes, making cancer cells more sensitive to chemotherapy and radiation. Elevated temperatures also induce cell cycle arrest and trigger apoptotic pathways. Furthermore, hyperthermia modifies the expression of heat shock proteins, which play vital roles in cancer therapy, including enhancing immune responses. Hyperthermic treatments also have a significant impact on the body’s immune response against tumors, potentially improving the efficacy of immune checkpoint inhibitors. Mild systemic hyperthermia (39°C-41°C) mimics fever, activating immune cells and raising metabolic rates. Intense heat above 50°C can release tumor antigens, enhancing immune reactions. Using photothermal nanoparticles for targeted heating and drug delivery can also modulate the immune response. Hyperthermia emerges as a cost-effective and well-tolerated adjuvant therapy when integrated with immunotherapy. This comprehensive review serves as a valuable resource for the selection of patient-specific treatments and the guidance of future experimental studies.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with clinical features of high metastatic potential, susceptibility to relapse, and poor prognosis. TNBC lacks the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It is characterized by genomic and transcriptional heterogeneity and a tumor microenvironment (TME) with the presence of high levels of stromal tumor-infiltrating lymphocytes (TILs), immunogenicity, and an important immunosuppressive landscape. Recent evidence suggests that metabolic changes in the TME play a key role in molding tumor development by impacting the stromal and immune cell fractions, TME composition, and activation. Hence, a complex inter-talk between metabolic and TME signaling in TNBC exists, highlighting the possibility of uncovering and investigating novel therapeutic targets. A better understanding of the interaction between the TME and tumor cells, and the underlying molecular mechanisms of cell–cell communication signaling, may uncover additional targets for better therapeutic strategies in TNBC treatment. In this review, we aim to discuss the mechanisms in tumor metabolic reprogramming, linking these changes to potential targetable molecular mechanisms to generate new, physical science-inspired clinical translational insights for the cure of TNBC.

Introduction: PhD is the highest awarded degree offered by universities in different disciplines. Owners of a PhD can teach at universities, start independent research and receive a higher salary while further building a scientific career. We examined whether the publication output before the PhD degree has a correlation with subsequent research activities. Methods: We downloaded publication and citation data from the Hungarian Scientific Bibliography for Hungarian researchers who obtained PhD between the ages of 24 and 45. The researchers were grouped into eleven scientific sections. We examined the number of Q1 publications published in the previous 5 years, the H-index, the total number of citations for the last complete year, and the biological age of the researcher. Each parameter was computed for the year at which the PhD was obtained and ten years later. Pre-PhD publications (and citations for these) were excluded when assessing post-PhD track records. Spearman rank correlation and Kruskal-Wallis test were computed. Results: We analyzed all together 4,790 researchers. We obtained a positive correlation between the number of Q1 publications before and after PhD (corr. coeff. = 0.21–0.54, p<0.01 in all sections), between the H-index before and after PhD (corr. coeff. = 0.32–0.56, p<0.01 in all sections), and between the citations received before and after PhD (corr. coeff. = 0.34–0.51, p<0.01 in all sections). All three metrics measured ten years after the PhD were negatively correlated with the age of the researcher at the time of obtaining the PhD (number of publications corr. coeff. = -0.09–0.22, p<0.05; H-index corr. coeff. = -0.09–0.29, p<0.08; number of citations corr. coeff. = -0.14–0.30, p<0.01). Among all disciplines, Philosophy and History and Engineering sciences show the strongest correlation between pre- and post-PhD output. When running multiple regression analysis for all three metrics as dependent variables and the number of articles, the H-index, the number of citations in the year of the PhD, the calendar year of PhD, and the gender of the researcher as independent variables, the number of articles and the H-index in the year of PhD reached the strongest positive correlations while gender had a negative correlation. Conclusions: We independently evaluated pre- and post-PhD publication performance. In connection with age, the discipline-specific reference values of scientometric parameters at the time of obtaining the PhD can help to select candidates for postdoctoral grants and positions.

Background: A deeper understanding of the molecular and clinical characteristics of HER2-low and ultralow breast cancer (BC) subtypes is essential for advancing therapeutic strategies. Methods: Three independent GEO datasets with microarray and IHC/FISH data from 510 BC patients were analyzed to establish reliable HER2 expression cutoff values (>3034 for HER2-positivity and <1780 for HER2-ultralow), defining HER2-positive (HER2+), HER2-low, and HER2-ultralow cohorts. Combined with hormone receptor status, six distinct BC subgroups were identified. Prognosis was evaluated using univariate and multivariate survival analysis in a dataset of 7830 BC patients, alongside correlative analysis of 17 immune-related gene signatures across subgroups. A PubMed literature review compared our findings with existing studies. Results: In hormone receptor-positive (HR+) patients, HER2-low tumors were associated with better prognosis than HER2-ultralow and HER2+ subgroups (P = .0048 for relapse-free survival (RFS) and P = .0015 for distant-metastasis-free survival (DMFS)). No prognostic significance was observed in HR-negative (HR−) patients. Immune gene activation was consistently higher in HR− tumors, with HER2-low (HR+ and HR−) and HR-/HER2+ patients showing significant immune signature overlap. While HR+/HER2-ultralow and HR+/HER2+ patients had modest immune activation, HR-/HER2-ultralow patients exhibited the strongest association with immune signaling, including IFN signaling, T cell-activating cytokines, and cytotoxic effector molecules. Conclusions: These findings, supported by a comprehensive literature review, indicate that patients with HER2-low and HER2-ultralow BC exhibit distinct immune patterns, which supports their classification as unique BC subgroups.

Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 (p < 10⁻⁵ for each gene). Mass spectrometry further validated the differential protein abundance of these genes (IGFBP3, p = 7.53 × 10⁻¹⁸; PLIN2, p = 3.9 × 10⁻³⁹; PLOD2, p = 6.51 × 10⁻³⁶; PFKP, p = 1.01 × 10⁻⁴⁷; VEGFA, p = 1.40 × 10⁻²²; CCND1, p = 1.04 × 10⁻²⁴). We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.