Heat shock protein (HSP) synthesis is switched on in a remarkably wide range of tumor cells, in both experimental animal systems and in human cancer, in which these proteins accumulate in high levels. In each case, elevated HSP concentrations bode ill for the patient, and are associated with a poor outlook in terms of survival in most cancer types. The significance of elevated HSPs is underpinned by their essential roles in mediating tumor cell intrinsic traits such as unscheduled cell division, escape from programmed cell death and senescence, de novo angiogenesis, and increased invasion and metastasis. An increased HSP expression thus seems essential for tumorigenesis. Perhaps of equal significance is the pronounced interplay between cancer cells and the tumor milieu, with essential roles for intracellular HSPs in the properties of the stromal cells, and their roles in programming malignant cells and in the release of HSPs from cancer cells to influence the behavior of the adjacent tumor and infiltrating the normal cells. These findings of a triple role for elevated HSP expression in tumorigenesis strongly support the targeting of HSPs in cancer, especially given the role of such stress proteins in resistance to conventional therapies.

BackgroundHeat Shock Proteins (HSPs), a family of genes with key roles in proteostasis, have been extensively associated with cancer behaviour. However, the HSP family is quite large and many of its members have not been investigated in breast cancer (BRCA), particularly in relation with the current molecular BRCA classification. In this work, we performed a comprehensive transcriptomic study of the HSP gene family in BRCA patients from both The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts discriminating the BRCA intrinsic molecular subtypes.MethodsWe examined gene expression levels of 1097 BRCA tissue samples retrieved from TCGA and 1981 samples of METABRIC, focusing mainly on the HSP family (95 genes). Data were stratified according to the PAM50 gene expression (Luminal A, Luminal B, HER2, Basal, and Normal-like). Transcriptomic analyses include several statistical approaches: differential gene expression, hierarchical clustering and survival analysis.ResultsOf the 20,531 analysed genes we found that in BRCA almost 30% presented deregulated expression (19% upregulated and 10% downregulated), while of the HSP family 25% appeared deregulated (14% upregulated and 11% downregulated) (|fold change| > 2 comparing BRCA with normal breast tissues). The study revealed the existence of shared HSP genes deregulated in all subtypes of BRCA while other HSPs were deregulated in specific subtypes. Many members of the Chaperonin subfamily were found upregulated while three members (BBS10, BBS12 and CCTB6) were found downregulated. HSPC subfamily had moderate increments of transcripts levels. Various genes of the HSP70 subfamily were upregulated; meanwhile, HSPA12A and HSPA12B appeared strongly downregulated. The strongest downregulation was observed in several HSPB members except for HSPB1. DNAJ members showed heterogeneous expression pattern. We found that 23 HSP genes correlated with overall survival and three HSP-based transcriptional profiles with impact on disease outcome were recognized.ConclusionsWe identified shared and specific HSP genes deregulated in BRCA subtypes. This study allowed the recognition of HSP genes not previously associated with BRCA and/or any cancer type, and the identification of three clinically relevant clusters based on HSPs expression patterns with influence on overall survival.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4621-1) contains supplementary material, which is available to authorized users.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

Heat shock factor 1 (HSF1) is the primary component for initiation of the powerful heat shock response (HSR) in eukaryotes. The HSR is an evolutionarily conserved mechanism for responding to proteotoxic stress and involves the rapid expression of heat shock protein (HSP) molecular chaperones that promote cell viability by facilitating proteostasis. HSF1 activity is amplified in many tumor contexts in a manner that resembles a chronic state of stress, characterized by high levels of HSP gene expression as well as HSF1-mediated non-HSP gene regulation. HSF1 and its gene targets are essential for tumorigenesis across several experimental tumor models, and facilitate metastatic and resistant properties within cancer cells. Recent studies have suggested the significant potential of HSF1 as a therapeutic target and have motivated research efforts to understand the mechanisms of HSF1 regulation and develop methods for pharmacological intervention. We review what is currently known regarding the contribution of HSF1 activity to cancer pathology, its regulation and expression across human cancers, and strategies to target HSF1 for cancer therapy.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p= .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.

Epidemiological studies describe estrogens as protectors in the development of colon cancer in postmenopausal women treated with hormone replacement therapy. However, the role of progesterone in colon cancer has been minimally studied and the results are controversial. For the above, the objective of this work was to determine the hormonal regulation exerted by natural ovarian steroids on proliferation and apoptosis in an experimental model of colon cancer in ovariectomized rats treated with 17-beta estradiol and progesterone. Sprague–Dawley rats were exposed to the carcinogen 1,2-dimethylhydrazine to induce colon tumors. Thirty days later, the rats were ovariectomized and treated with estradiol (60 μg/kg), progesterone (10 mg/kg), estradiol plus progesterone (60 μg/kg and 10 mg/kg) or vehicle. We observed no significant differences in colon cancer incidence and tumor multiplicity between the groups. Nevertheless, we observed a decrease in PCNA expression and a greater number of apoptotic index, higher expression of caspase 3, cleaved PARP and cleaved caspase 8 in tumors, confirming the activation of the extrinsic pathway of apoptosis by the combined treatment. In addition, we observed a higher expression of estrogen receptor beta in these tumors. We conclude that the action of both hormones, estradiol and progesterone, is necessary to reduce proliferation and increase apoptosis in colon tumors, probably through estrogen receptor beta activation.

The oral squamous cell carcinoma (OSCC), which has a high morbidity rate, affects patients worldwide. Changes in SPINK7 in precancerous lesions could promote oncogenesis. Our aim was to evaluate SPINK7 as a potential molecular biomarker which predicts OSCC stages, compared to: HER2, TP53, RB1, NFKB and CYP4B1. This study used oral biopsies from three patient groups: dysplasia (n = 33), less invasive (n = 28) and highly invasive OSCC (n = 18). The control group consisted of clinically suspicious cases later to be confirmed as normal mucosa (n = 20). Gene levels of SPINK7, P53, RB, NFKB and CYP4B1 were quantified by qPCR. SPINK7 levels were correlated with a cohort of 330 patients from the TCGA. Also, SPINK7, HER2, TP53, and RB1, were evaluated by immunohistofluorescence. One-way Kruskal–Wallis test and Dunn's post-hoc with a p < 0.05 significance was used to analyze data. In OSCC, the SPINK7 expression had down regulated while P53, RB, NFKB and CYP4B1 had up regulated (p < 0.001). SPINK7 had also diminished in TCGA patients (p = 2.10e-6). In less invasive OSCC, SPINK7 and HER2 proteins had decreased while TP53 and RB1 had increased with respect to the other groups (p < 0.05). The changes of SPINK7 accompanied by HER2, P53 and RB1 can be used to classify the molecular stage of OSCC lesions allowing a diagnosis at molecular and histopathological levels.

We argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable criterion levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and determining sample sizes much more directly than significance testing does; but none of the statistical tools should replace significance testing as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, or implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.