Autophagy Modulation Attenuates Sorafenib Resistance In HCC Induced In Rats

doi:10.21203/rs.3.rs-2573418/v1

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Autophagy Modulation Attenuates Sorafenib Resistance In HCC Induced In Rats

https://doi.org/10.21203/rs.3.rs-2573418/v1

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Hepatocellular carcinoma (HCC) has risen as the villain of cancer-related death globally, with a usual cruel forecasting. For advanced HCC, sorafenib has been FDA approved as first line treatment. In spite of the bright potential that sorafenib demonstrated in studies, actual clinical results are limited owing to the massive occurrence of drug resistance. The tumor microenvironment (TME) has been linked to therapeutic resistance, indicating that current cellular level approaches may be inadequate to increase treatment efficacy. The involvement of autophagy in cancer is a double-edged knife. On one aspect, autophagy allows malignant cells to withstand strain, such as a hypoxic TME and starvation caused by therapy. on the other side, autophagy plays a vital function in damage prevention, which can decrease carcinogenesis. As a result, regulating autophagy is unquestionably a viable method in the therapy of malignancies. The aim if this study was to investigate the role of autophagy modulation in combination to sorafenib by comparing both induction and inhibition of autophagy to the sorafenib monotherapy of HCC induced in Sprague–Dawley rats. Autophagy, apoptosis and cell cycle were analyzed by using western blot, ELISA, Immunuhistochemistry, flow cytometry and Quantitive-PCR. Routine biochemical testing and pathological examination was carried out. Transmission electron microscope was used to visualize ultracellular structures and autophagic bodies. We found remarkable alleviation of chemotherapeutic resistance and hepatoprotective effects by both regimens. To the best of our knowledge, this study was the first to study the autophagic inhibition simultaneous with autophagic induction in sorafenib treatment in-vivo.

hepatocellular carcinoma

sorafenib

autophagy

drug resistance

hypoxia

simvastatin

hydroxychloroquine

apoptosis

A major cause of death linked to cancer in the world is hepatocellular carcinoma (HCC). Globally, hepatic cancer is the fifth in terms of incidence and second in terms of mortality in men. According to annual statistics, the World Health Organization predicts that more than 1 million people will die of liver cancer by 2030 (1). Considerable progress has been made in recent decades to understand the epidemiology, risk factors, and molecular profiles of HCC. In addition, rational approaches have been developed for prevention, surveillance, early detection, diagnosis, and treatment (2). Until 2006, there were no effective first line treatment drugs for advanced HCC patients, representing 40–70% of the entire HCC population. Various systemic therapies, such as chemotherapy (cisplatin, doxorubicin, epirubicin, etc.), immunotherapy (interferon), hormonal compounds, and many other compounds, gave unfulfilling or negative results (3).

Sorafenib (Nexavar®) is the singular systemic agent used in advanced HCC that is approved by the Food and Drug Administration. Based on the results of Phase III trials of Sorafenib Asia-Pacific and pivotal Sorafenib Hepatocellular carcinoma Assessment Randomized Protocol trials in advanced HCC patients with Child–Pugh class A, it was approved in the European Union and the USA in 2007 (4,5). Sorafenib is a multikinase inhibitor which is administered orally (6–8). Sorafenib decreased the proliferation of HCC cells, tumor growth and angiogenesis, in addition to inducing their apoptosis, in in vitro and animal models of HCC (9).

Despite these impressive results, sorafenib conferred only limited benefits to patients with advanced HCC and failed to completely cure them. It was beneficial to barely about 30% of the patients, and the presence of primary and acquired resistance to sorafenib was detected in HCC cells (4,5). A considerable number of patients develop primary resistance—a result of HCC heterogeneity—but in the majority of the cases, the resistance is acquired (10). This is seen as a consequence of long-term drug exposure. Numerous mechanisms are involved in reducing tumor cell sensitivity to treatment with sorafenib, such as loops of the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) and Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathways, deactivation of pro-apoptotic signals, cancer stem cells, epithelial–mesenchymal transition and hypoxia-inducible response (11,12).

Autophagy (meaning “self-eating”) is the process responsible for bulk degradation of long-lived cytoplasmic proteins and organelles. Macro-autophagy is characterized by the capturing and sequestration of the target cargo far from the lysosome, unlike micro-autophagy and chaperone-mediated autophagy. Macro-autophagy involves the de novo synthesis of double-membraned autophagic vesicles, called autophagosomes, which capture cargo and then translocate it to the lysosome (13). Autophagy is basically a cytoprotective mechanism; but autophagic dysfunction is correlated with a number of human pathologies, such as lung, heart, and liver disease, aging, neurodegeneration, myopathies and diabetes (14). Recently, it has been reported that autophagy defects are linked to tumorigenesis. It has also been shown that autophagy is involved in both the promotion and suppression of tumorigenesis (15).

The objective of our study was to investigate the effect of coupling sorafenib with autophagy modulators on the treatment efficiency of HCC. We evaluated the efficacy of a treatment strategy involving sorafenib, in combination with simvastatin (SV, an autophagy inducer) or hydroxychloroquine (CQ, an autophagy inhibitor), in an HCC rat model.

2.1 Sorafenib combination with autophagic modulators improved hepatic function and suppressed HCC progression more effectively

2.1.1 H&E histopathological examination, nodule size and necroinflammatory scoring

Microscopic images of H&E-stained hepatic sections from the N group showed normal arrangement of hepatic cords around the central vein with normal portal areas and sinusoids (Fig. 1a). H&E-stained hepatic sections from the HCC group showed loss of normal hepatic architecture due to arrangement of hepatocytes into solid nodules and the central vein surrounded by thick necroinflammatory zones containing fibrous tissue infiltrated with leukocytes and hemosiderin-laden macrophages. Some nodules developed well-differentiated HCC. Cells of HCC are polygonal with distinct cell membranes, an eosinophilic granular cytoplasm, enlarged rounded vesicular nuclei with coarse chromatin, thickened nuclear membrane and prominent nucleoli without mitotic figures and cytoplasmic eosinophilic inclusions. Cholangiocarcinoma appears in some sections (Fig. 1a).

Microscopic images of H&E-stained hepatic sections from the SV, CQ, and SF groups showed partial to marked improvements in hepatic architecture. The SF group showed perivascular mild fibrosis around central veins with the presence of a few necrotic hepatocytes and leukocytes (Fig. 1b). In addition, the CQ group showed decreased degrees of inflammation, necrosis and congestion in centrilobular areas with the presence of micro-vesicular steatosis and ballooning degeneration in hepatocytes around centrilobular areas (Fig. 1b).

Moreover, the SV group showed centrilobular thick necroinflammatory areas, containing necrotic cells, leukocytes, hemosiderin-laden macrophages and congested blood vessels, and surrounded by multifocal micro-or macro-vesicular steatosis to ballooning degeneration (Fig. 1b). On the other hand, hepatic sections from treated group SF + SV and SF + CQ showed marked improvement in hepatic architecture with mildly congested blood vessels and very few leukocytic cells infiltrating (Fig. 1b).

The combination treatment SF + CQ and SF + SV groups significantly reduced the necroinflammatory score by 6–8 folds compared to that of the HCC group (Fig. 1d & 1e). Also, the necroinflammatory score in these groups was non-significantly lower than that of the SF group alone. Consistently, the mean tumor nodule size was significantly lower in SF + CQ and SF + SV groups compared to that of HCC or SF groups (Fig. 1f).

2.1.2 Masson’s trichrome staining and fibrosis area fraction

Normal control group (N) showed the normal minimal distribution of the bluish stained collagen fibers around the central veins. HCC group appeared with widespread extension of the bluish stained collagen fibers from the central vein vicinity to invade in between the hepatocytes (Fig. 1b). SF group showed apparently less distribution of collagen bundles than the HCC group. CQ and SV groups also had less prominent collagen than the HCC group. SF + CQ and SF + SV groups exhibited the least collagen content. The fibrotic area fraction assessed in Masson’s trichrome stained liver sections was significantly lower in SF + CQ group compared with HCC group (Fig. 1g).

2.1.3 Liver gross examination, Liver index and Overall survival:

The gross examination of livers isolated from HCC group showed a slightly rough nodular surface with noticeable faint discoloration compared to reddish and smooth surfaces of control livers. On the other hand, all the treatment groups showed a decrease in the nodular surface and darker colors specially in SF + CQ group, rendering them smoother and reddish when compared to HCC group (Fig. 1c). Moreover, the liver index was significantly lower in the SF + CQ group compared with either SF group (p < 0.05) or HCC group (p < 0.001) (Fig. 1i). Also, the overall survival over the course of 3-weeks treatment was the highest in SF + CQ (100%), similar to N group, (Fig. 1h).

2.1.4 TEM histopathological micrographs:

HCC group hepatic cells showed disintegrated cellular content, abnormal mega-mitochondria, pyknotic nucleus, with abnormal chromatin distribution, very high content of vacuoles, breakdown of rough endoplasmic reticulum and infiltrated fat droplets, (Fig. 2a). In addition, SF group hepatic cells showed more integrated cellular content, nuclear, and mitochondrial structures and lower content of vacuoles in comparison to HCC group, (Fig. 2a).

TEM images showed ameliorative effects of the combination treatment in SF + CQ and SF + SV groups when compared to HCC or SF groups. SF + CQ and SF + SV groups showed ameliorative architecture of hepatic cells and more integrated organelles indicating regeneration, (Fig. 2a).

CQ and SV groups showed progressing degeneration of hepatic cells and less integrated organelles or abnormal mitochondria in addition to numerous vacuoles, (Fig. 2a).

2.1.5 Serum AFP and routine hepatic function tests:

Serum AFP was lowered significantly (P˂0.001) by SF monotherapy and SF combinations too, but there was no statistical significance between SF and SF + CQ & SF + SV, (Fig. 2b). Additionally, the synthetic function of liver evidenced by albumin plasma concentration was less affected in case of SF + CQ compared to SF group (Fig. 2c). Similarly, the detoxification function evidenced by plasma bilirubin was significantly less impaired too compared to SF group, indicating more successful treatment of hepatobiliary disease (Fig. 2d). Serum ALT, GGT, and ALP activities were significantly lower in SF + CQ compared to SF group. Also, Serum ALT and ALP activities were significantly lower in SF + SV compared to SF group (Figs. 2e, 2f, 2g).

2.2 Success of autophagic modulation by hydroxychloroquine and simvastatin

2.2.1 Westernblotting of LC3

In LC3 blots, the upper band is LC3-I (visible molecular weight: 16 kDa), whereas the bottom band is LC3-II (visible molecular weight is 14 kDa), as shown in (Fig. 3a).

The blot showed expected rise in LC3II/I in the HCC group above that of N group (p < 0.001). Similarly, LC3-II was elevated expectedly in SF when compared to HCC (p < 0.001), in SV (p < 0.001) and in CQ (p < 0.001). Additionally, SF + SV had significant increase in LC3-II when compared to SF (p < 0.001). The exception was SF + CQ, which showed a relative weak LC3II band than what was anticipated, (Figs. 3a, 3b). We then validated the protein expression with gene expression via mRNA quantification of LC3B, (Fig. 3f). Later, P62 expression confirmed success of autophagy inhibition and induction as shown in (Fig. 3c).

2.2.2 Immunohistochemistry of P62 and its relative gene expression using qPCR.

There was no significant variation in P62 gene expression between most of the study groups (Fig. 3g); indicating that the variation in P62 levels relies mainly on the rate of autophagic degradation. Even though, during autophagy, the degradation of autophagic proteins, particularly those acting as cargo receptors, is accelerated, and lysosomes are used in the development of autolysosomes. As a result, the transcriptional activation of lysosomal and autophagic genes could be adequate to compensate for the depletion of the corresponding proteins during autophagy (16). That would be a reasonable explanation for the significant change between N versus HCC group (p < 0.05), and that between SF group and SF + SV, the autophagic induction combination group, (p < 0.05) as shown in (Fig. 3g).

The protein expression of P62 showed elevated levels in SF + CQ when compared to SF (p < 0.001), confirming autophagy inhibition by hydroxychloroquine, which was otherwise not confirmed by western blot analysis of LC3-II. The expression of P62 protein came in accordance with LC3-II/LC3-I -(Fig. 3e)- emphasizing the success of autophagic modulation in SF + CQ & SF + SV when compared to SF, (p < 0.001) in both cases.

2.2.3 TEM micrographs of ultra-thin liver sections of autophagy related cellular structures

Photos from TEM showed very few autophagic bodies in N group, (Fig. 3d), with higher presence of lysosomes and autolysosomes in HCC group. Autolysosomes (AL) typically have only one limiting membrane; frequently they contain electron dense cytoplasmic material and/or organelles at various stages of degradation (17). AL were significantly more in liver sections from SF group. On the other hand, photos from CQ group were characterized by heavy accumulation of lysosomes. SV group showed presence of AL in a manner parallel to that of SF. Combinations groups showed accumulation of lysosomes with the presence of some AL in case of SF + CQ, and accumulation of AL and autophagosomes in case of SF + S.

Notably, the high induction of autophagy in case of SF + SV allowed the visualization of various stages of autophagy as phagophore elongation, autophagosome formation and autophagosome lysosomal fusion into AL, illustrated and simplified below the TEM photos from SF + SV. A giant AL was also visualized in photos from the same group.

2.3 Autophagic modulators combination with Sorafenib sensitized HCC to sorafenib

2.3.1 Measuring Serum Galectin-1 level by ELISA

Outstanding work has been done in the field of finding a proper prognostic tool for sorafenib resistance. Galectin-1 (Gal-1) levels are elevated in sorafenib-resistant HCC models in vitro and in vivo, which may enhance cancer spread and tumor invasion. In particular, they discovered that elevated serum galectin-1 values are correlated with poor treatment effectiveness and shorter overall survival in advanced HCC managed by sorafenib. These data validated utilizing Gal-1 as a new HCC predictive and prognostic biomarker for sorafenib resistance (18).

We found a significant increase in Gal-1 serum levels in HCC group than N group (p < 0.001), and a slight non-significant increase with SF when compared to HCC. But in the combination therapy, SF + CQ and SF + SV Gal-1 was decreased when compared to HCC (p < 0.05) and (p < 0.001), respectively. Interestingly, a significantly lower level of Gal-1 was seen in SF + CQ & SF + SV in comparison to SF (p < 0.001) as shown in (Fig. 4a).

2.3.2 Relative gene expression of HIF-1α and VEGFa by qPCR

To further elucidate transcriptional changes associated with sorafenib treatment, we measured the mRNA relative expression of Hypoxia Inducible factor-1 (HIF-1α), and found Significant increase in HIF-1α in HCC group to about 4-fold that of N group (p < 0.001), and further increased significantly in SF group (p < 0.001) indicating the occurrence of sorafenib resistance. The occurrence of resistance was also confirmed by results of VEGFa gene expression which was significantly lowered by SF + CQ & SF + SV too, as in case of HIF-1α, in contrast to SF, illustrated in (Figs. 4b, 4c).

2.3.3 Immunohistochemistry of Nuclear factor kabba B (NF-κB)

Sorafenib resistance was detected as well by the high protein expression of NF-κB in Sorafenib monotherapy and the decrease with combining with simvastatin or hydroxychloroquine, (Figs. 4d, 4f)

2.4 Apoptosis induction and cell cycle arrest

2.4.1 Hydroxychloroquine and Simvastatin synergically increased Caspase-3 Immunuhistochemistry and its apoptotic index with Sorafenib

Apoptosis induction was tested by assessment of hepatic active caspase-3 (Cas-3) protein expression level; as shown in (Figs. 5a, 5b, 5c) autophagic modulators in combination with sorafenib elevated active cas-3 expression, and hence, higher apoptotic index. We further confirmed these results by qPCR and cell cycle analysis.

2.4.2 Hydroxychloroquine and Simvastatin synergically increased apoptosis on the transcriptional level

To further assess the apoptotic pathway modulated, we measured the effect on the gene modulation of three genes by qPCR, which are Caspase-3 (Fig. 5d), Caspase-8 (extrinsic pathway biomarker) (Fig. 5e) and anti-apoptotic Bcl2 (B-cell lymphoma 2) (Fig. 5f).

We found that tumor cells on the gene level was downregulating the expression of caspases and upregulating the Bcl2. Probably the caspases downregulation was a negative feedback loop inhibition in response to early apoptosis that cells normally execute in the carcinogenesis early DNA alterations. This was assisted by mild elevation in hepatic active caspase-3 staining in HCC in comparison to N group.

2.4.3 Sorafenib arrested cell cycle at G0/1 in its monotherapy and in G2/M when combined with Hydroxychloroquine and simvastatin.

Cell cycle analysis results as shown on (Fig. 6), showed significant elevation in apoptotic or necrotic cells represented by SubG1 phase (fragmented DNA that appears on the graph before G1 phase) in SF, CQ & SV (p < 0.001), when compared to HCC.

Interestingly, that increase was higher in SF + CQ & SF + SV than SF of sorafenib (p < 0.001), (Fig. 6a).

We also noticed that SF caused G0/1 cell cycle arrest which is accumulation of more percentage of cells in the resting state rather than replication phases, (Fig. 6b).

On the contrary, the combination regimens, SF + CQ & SF + SV, both caused cell cycle arrest at G2/M phase, marked by decrease of the cells at the active mitosis stage and their stop at the DNA synthesis phase (S) with inability to complete the division for replication, (Figs. 6c, 6d).

Representative figures from each group results are shown on (Fig. 6e).

Although sorafenib has provided a ray of hope after years of looking for therapeutic drugs to manage HCC, the ultimate results are anything but satisfactory. Besides its adverse effects, the genetic variability of HCC confers sorafenib resistance on several patients, leading to the discovery of prognostic biomarkers for primary resistance to sorafenib. The development of acquired resistance after treatment has also piqued the interest of researchers. Several processes are implicated in this phenomenon, including interaction between the PI3K/Akt and JAK-STAT pathways, induction of hypoxia-inducible pathways, and epithelial–mesenchymal transition (7,12).

Autophagy is one of the pathways that is both activated by sorafenib and implicated in its acquired resistance (19). Autophagy suppression by pharmacological inhibitors (chloroquine, 3-MA, or bafilomycin A1) or gene silencing (Beclin1 or Atg5) improves sorafenib efficacy against HCC cells, demonstrating that autophagy induced by sorafenib plays a protective role (20,21). Also, different autophagy responses in different HCC models showed various sensitivities to sorafenib (22).

Animal models are important tools in cancer research. A variety of animal models have been established to better understand the pathophysiology of HCC and the impact of prospective therapy. Several morphological, histogenic, and biochemical aspects of human HCC are shared by chemically induced hepato-carcinogenesis in animals, which begins with an irreversible alteration of DNA structure (23). Common liver carcinogens include diethylnitrosamine (DEN), carbon tetrachloride, aflatoxin, dimethylnitrosamine, and thioacetamide (TAA). The DEN-induced HCC is the most commonly used one (24). A recent study found that autophagy, by increasing HIF-1 degradation, suppresses tumor growth and sorafenib resistance in the DEN-induced HCC mouse model (25).

Tumor cell resistance to sorafenib may also be linked to the transition from autophagic cell death to autophagy-related HCC cell survival. Furthermore, preventing autophagy induction is another method of developing sorafenib resistance. It has been shown that resistant cells had reduced levels of autophagy markers such as LC3, Atg5, Vps34, or Beclin1. For all of these reasons, restoration of autophagy might be one of the most important strategies for avoiding cellular resistance to the antineoplastic drug (26,27).

Several studies, covered in the review by Sun et al. (19), have showed a synergistic antitumor effect arising from the modulation of autophagy during sorafenib treatment, with autophagic inhibitors, such as chloroquine/hydroxychloroquine (21,28) and vorinostat (29), Akt inhibitors such as pemetrexed (30), GDC0068 (27) and arsenic trioxide (31) as well as direct autophagy inducers such as metformin (20). However, excess stimulation may result in programmed cell death rather than survival (32). When sorafenib was coupled with pemetrexed, a folate anti-metabolite that promotes autophagy, the therapy enhanced cell death in vitro while suppressing tumor development in vivo(30). Moreover, Manov et al. showed a peculiar antagonistic effect of combining sorafenib with doxorubicin which, on its own, can stimulate autophagy (33). Thus, autophagy can either facilitate cell survival or drive cell death (34), and further research is required to elucidate this.

Here, we compared the results of treating HCC using sorafenib coupled with an autophagy inducer or an autophagy inhibitor.

Oxygen deprivation is frequent in solid tumors, including HCC, and promotes VEGF synthesis and angiogenesis via HIF-1α stimulation (35). Thereby, the antiangiogenic effects of sorafenib originate from the inhibition of the HIF-1α/VEGF pathway (36). Unfortunately, there is a compelling link between acquired sorafenib resistance and the hypoxic microenvironment, since the antiangiogenic activity of long-term sorafenib treatment causes tumor starvation and subsequent intratumoral hypoxia, favoring the selection of resistant cell clones well adapted to oxygen and nutrient shortage. This scenario reduces the efficacy of sorafenib (37).

Liang et al. found that regular administration of sorafenib in HCC mice models increased HIF-1α and NF-κB transcriptional activity and protein expression. Accordingly, this might induce sorafenib resistance as a cytoprotective adaptive response. They also noted that HCC tissues from sorafenib-resistant patients exhibited higher intratumoral hypoxia and HIF-1α expression than sorafenib-sensitive or untreated HCC tissues (37). Several studies have showed a compensatory response to sorafenib treatment by an increase in HIF-1α after an initial lowering (18,25). Hypoxia in the stroma, along with oxidative stress, can activate autophagy, which serves as an escape mechanism for cancer cells throughout antiangiogenic therapy (21,30).

In addition, HIF-1α is a key regulator of VEGF expression. Under hypoxic conditions, accumulating HIF-1α upregulates the production of a number of angiogenic factors, including VEGF, and stabilizes the VEGF mRNA, eventually initiating tumor angiogenesis (38). Furthermore, there is also evidence of a bi-directional relationship between HIF-1α and NF-κB, wherein NF-κB may stimulate HIF-1α and HIF-1α can control NF-κB. The occurrence of hypoxia and inflammation is a hallmark of cancer. Hypoxia increases inflammation by regulating gene expression via oxygen sensitive transcriptional regulators such as HIF-1α and NF-κB (39). The induction of NF-κB is a common characteristic of cancer cell responses to chemotherapy. Increased NF-κB activity, for example, has been linked to a poor response to neoadjuvant chemotherapy and radiation in patients with esophageal cancer (40). NF-κB may be a crucial indicator of drug resistance, since its induction by chemotherapy lowers apoptosis (41). Multiple in vitro investigations have shown that inhibiting NF-κB makes cancer cells more susceptible to chemotherapy-induced apoptosis (42,43).

Several investigations have revealed that tumor cells subjected to hypoxia express greater amounts of Gal-1 (44). Gal-1 can trigger epithelial–mesenchymal transition: a critical phase in the development of cancer in human cells, and a major pathway of sorafenib resistance (45), involving the α_vβ₃-integrin/FAK/PI3K/AKT signaling pathway (46). Through stimulation of the H-Ras/Raf/extracellular signal-regulated kinase pathway, disruption of Gal-1 expression is linked to chemotherapy resistance. Multiple studies have reported the involvement of Gal-1 in metastatic potential, and the impact of Gal-1 knockdown on treatment sensitivity in HCC (47,48), as well as other forms of cancer (49–51).

The results of our investigation on acquired sorafenib resistance agree with our previous work, wherein certain biomarkers, such as HIF-1α, VEGFa, NF-κB and Gal-1, were increased rather than lowered in the sorafenib monotherapy group. These biomarkers were even lower in the co-treatment groups (sorafenib with the autophagy modulators hydroxychloroquine and simvastatin), implying that modulating autophagy prevented the development of resistance.

The induction of autophagy in HCC cells manifested in the increased expression of LC3II (normalized to actin bands) and the higher LC3II/I ratio (p < 0.001) compared with the control group N. The expression of p62 increased (p < 0.0001) as a result of transcriptional activation to compensate for its depletion during the highly induced autophagy (16). This was seen in the significant increase of p62 mRNA in HCC group compared to N group (p < 0.05). Additionally, the autophagic structures were clearly visualized in the ultrathin sections from the HCC group using TEM. Previous studies have shown that the autophagic induction by sorafenib is accompanied with a significant increase in LC3II and LC3II/I ratio (p < 0.0001), and a decrease in p62 protein expression (p < 0.001) with no significant change in p62 mRNA level, when compared with the results for HCC. This confirmed that the decrease in P62 was due to autophagic degradation and not due to transcriptional changes.

The induction of autophagy by simvastatin was indicated by the high LC3II and LC3II/I band intensities in the SV and SF + SV groups when compared with the HCC group (p < 0.001) and the SF group (p < 0.001). The blocking of autophagy by hydroxychloroquine was indicated by an increase in LC3II and LC3II/I band intensities in the CQ group, but not in the SF + CQ group, when compared with the HCC group. This was a bit confusing considering that hepatic p62 proteins had already accumulated in that group compared with the HCC and SF groups. Usually, p62 accumulates due to the blockage of the last step of autophagy, inhibiting the autophagic degradation of p62.

One reasonable explanation, which is also a limitation of our study, is that the whole liver tissue was analyzed, without isolating the HCC tumor nodules. The results show the overall state in the dissected liver section, and are not limited to the tumor cells. Thus, one probability could be that the liver section used for this analysis had a larger percentage of normal cells, which were undergoing basal or minimal autophagy, and thus minimal cellular uptake of hydroxychloroquine. This possibility is supported by a non-significant difference (p = 0.47) between the SF + CQ and HCC groups in terms of relative LC3II expression levels. Also, the best hepatic indices among the study groups were that of the SF + CQ group.

One study used bafilomycin (BafA1), hydroxychloroquine and a combination of both. BafA1 increased LC3II levels more pronouncedly than hydroxychloroquine. Moreover, the treatment with both compounds increased LC3II to levels similar to those observed in cells exposed to BafA1 alone (52). This implies that the ability of hydroxychloroquine to accumulate LC3II is relatively weak, and the addition of hydroxychloroquine to a system where LC3II is supposed to accumulate excessively, does not add much value.

It is worth mentioning that hydroxychloroquine showed beneficial effects on cancer regression in clinical studies, generally, (NCT03344172, NCT00969306, NCT01006369 & NCT01273805) (53), and in HCC-treated with sorafenib, particularly (NCT03037437), either alone or in combination with other medications. Those effects may not necessarily be connected with a blockade in autophagy (54,55).

Apoptosis is strongly linked with cell cycle progress. This relationship plays an important function in neoplasia. Furthermore, cell cycle regulation is linked to cell death and plays a significant role in cell turnover and carcinogenesis (56). Extrinsic and intrinsic pathways are frequently involved in cellular apoptosis. The extrinsic route is regulated by death receptors in the cell membrane, which then activates a downstream cascade of caspases. The intrinsic route, on the other hand, is strongly linked to the Bcl-2 family of proteins, all of which govern apoptosis by modulating the permeability of the mitochondrial outer membrane (57,58), where the Bcl-2 protein is usually found to be overexpressed in the majority of cancers (59).

For more than three decades, the establishment of medicines and regimens supporting the efficient eradication of cancer cells by apoptosis has been a mainstay and aim of clinical oncology. On the level of apoptosis, HCC cells showed apoptotic signs in terms of caspase-3 immunostaining and its apoptotic index, as well as the presence of higher fraction of cells in the subG₁ phase, where apoptotic and necrotic cells are found in cell cycle analysis. However, these findings in the HCC group were statistically insignifcant when compared with the N group. Interestingly, when we compared HCC to N on the transcriptional level, we found elevated levels of hepatic Bcl-2 mRNA (p < 0.001) and lowered levels of caspase-8 (p < 0.01), while hepatic caspase-3 mRNA showed a non-significant difference. Thus, we deduced that the late apoptotic signs seen in the HCC group were because of cells that had successfully executed programmed cell death in response to DNA misfolding due to DEN-induced tumorigenesis and prolonged treatment by TAA. Whereas the gene expression results were the tumor genetic modification to downregulate the apoptotic pathway both intrinsically and extrinsically in the early pathway stages (the gene transcription level) to affect the downstream proteins subsequently.

When compared to untreated HCC group, all the treatment groups showed a significant increase in all of the apoptotic signs, early and late in the pathway, these results are in agreement with the numerous studies which indicate that sorafenib (5,60–62), simvastatin (63–65) and hydroxychloroquine (66–68) induce apoptosis in cancer cells. Reasonably, the combination therapies showed a marked much higher elevation than that achieved by sorafenib alone (p < 0.0001) indicating increased cytotoxicity, and hence its efficacy as an anticancer drug. We did not have a significant variation between the two sorafenib combinations in resistance biomarkers quantified (HIF-1α, VEGFa, NF-κB and Gal-1), nor in apoptosis biomarkers, except for caspase-3 mRNA which showed significant increase in the favor of SF + SV group.

Our study demonstrated that autophagy modulators inhibited the sorafenib-resistant HCC cell proliferation and mitosis by inducing G2/M cell cycle arrest, leading to programmed cell death. All individual treatments increased accumulation of cells in G0/G1, which means more cells achieved a resting state (quiescence). Also, the combination therapies caused the cells to accumulate in S phase and they were unable to enter the mitotic stage, seen as a decrease in cell count in G2/M.

Regarding hepatic hemostasis, the histopathological score of liver sections obtained from the combination treatments of sorafenib was way more improved than that of sorafenib alone, as well as the biochemical results of liver function tests and fibrosis staining. There was a slight superiority of the hydroxychloroquine combination to the simvastatin one, and this can be explained in the light of the results obtained from the groups of monotherapy controls, where the hydroxychloroquine alone group showed better hepatoprotective signs compared to simvastatin alone group, this came in line with the studies that elucidated better liver indices in case of chloroquine treatment in TAA induced HCC rat models (69), and hydroxychloroquine in diabetic rats (70) and in liver steatosis (71). However, the improvement was not statistically significant compared to that of simvastatin combination except for alkaline phosphatase (P < 0.05).

In a nutshell, the current study showed that autophagy modulators in combination with sorafenib cause regression of chemotherapeutic resistance marked by lower HIF-1α and VEGFa on the gene transcription level, suppressed NF-κB and Gal-1 protein expression, G2/M phase arrest, programmed cell death activation, and ameliorative liver function when compared to sorafenib alone. Our findings emphasize the significance of HIF-1α, VEGFa, NF-κB and Gal-1 signaling pathways in the implementation of novel treatment techniques against drug-resistant cancers in general, and sorafenib resistance in particular. Our findings, are in line with that of other researchers (72,73) suggesting that not only autophagic inhibitors managed to show promising influence on sorafenib resistance, but also clinically safe autophagic inducers like simvastatin showed good results, indicating that both regimens are crucial candidates for clinical trials of HCC.

4.1. Establishment of the HCC model

All animal experiments were carried out following the ethical guidelines of the National Institutes of Health (NIH) concerning the care and use of laboratory animals (NIH publication No. 85–23, revised 1985). The experiments were approved by the Research Ethics Committee of the Faculty of Pharmacy, Mansoura University, Egypt (#2022-71). Male Sprague–Dawley rats weighing 220 ± 20 g (8–10 weeks age) were obtained from the “Medical Experimental Research Center,” Mansoura, Egypt. The rats were kept on acclimation under standard laboratory conditions of controlled room temperature (25°C ± 2°C) on a 12 h light/dark cycle for 2 weeks. They were allowed free access to water and food. HCC was established in rats by a single 200 mg/kg body weight intraperitoneal (i.p.) injection of diethylnitrosamine (DEN; Sigma–Aldrich; St. Louise, MO, USA); two weeks later, an i.p. injection of 200 mg/kg body weight thioacetamide (TAA; Sigma–Aldrich) was administered twice per week for 16 weeks (24). At the end of this period, the HCC model's establishment was validated by a liver histopathological examination and an assessment of serum alpha feto protein (AFP) levels.

4.2. Experimental design

Rats were divided into seven groups as follows: Control group (N) which received no treatment (n = 20); HCC group in which rats had HCC but received no further treatment (n = 16); sorafenib group (SF) which received 10 mg/kg sorafenib tosylate (LC laboratories; Boston, MA, USA) by oral gavage daily for 3 weeks (n = 16) (74,75); hydroxychloroquine group (CQ) which received 60 mg/kg CQ (plaquenil®; Sanofi Pharmaceutical Corporation, Paris, France) by oral gavage daily for 3 weeks (n = 16) (52,76,77); simvastatin group (SV) which received 10 mg/kg SV (granted by EVA Pharma Company for Pharmaceuticals; Cairo, Egypt) by oral gavage daily for 3 weeks (n = 16) (78,79); sorafenib + hydroxychloroquine group (SF + CQ) which received a combination of 10 mg/kg sorafenib and 60 mg/kg CQ by oral gavage daily for 3 weeks (n = 16); and sorafenib + simvastatin group (SF + SV) which received a combination of 10 mg/kg sorafenib and 10 mg/kg SV by oral gavage daily for 3 weeks (n = 16).

4.3. Sample collection

At the end of the experiment, rats were starved for 12 h and allowed free access to water. Blood samples were drawn through retro-orbital puncture and centrifuged for serum separation. Instantly after sacrificing the rats, the liver was isolated and divided into four sections. The first section was put into RNA-later (Qiagen-Germany) and flash frozen in liquid nitrogen to measure gene expression by qPCR. The second section was cut into pieces, flash frozen in liquid nitrogen and then stored at − 80°C for western blotting and cell cycle analysis. The third section was preserved in a primary fixative solution of 2.5% glutaraldehyde + 2% paraformaldehyde containing 0.1 M sodium phosphate buffer pH 7.4 at 4°C for 24 hours for transmission electron microscopy (TEM) analysis. The fourth section was fixed in neutral formalin, embedded in paraffin blocks and used for histopathological examination. The liver index was calculated by the following formula: Liver index = [Liver weight (g) / Body weight (g)] × 100 (80).

4.4. Biochemical analysis

Sera were used for kinetic colorimetric measurement of alanine aminotransferase (Spectrum Diagnostics, Egypt), gamma-glutamyl transferase (Human diagnostics, Germany), alkaline phosphatase (Human diagnostics, Germany) activities, and also, for endpoint colorimetric assays to measure the levels of albumin (Biodiagnostic, Egypt) and total bilirubin (Diamond Diagnostics, Egypt).

4.5. Histopathological examination of hepatic tissues

After being fixed in formalin, liver tissues were embedded in paraffin blocks, dissected at a 4 µm-thickness to be stained with hematoxylin and eosin (H&E) and Masson's trichrome for histopathological examination under a light microscope. H&E-stained hepatic sections were used to evaluate necroinflammatory scores guided by Ishak modified Histology activity index (81). Necroinflammatory changes were calculated in H&E-stained liver sections as the sum of four categories: periportal or periseptal interface hepatitis (0–3), necrosis (0–3), ballooning degeneration (0–4), steatosis (0–3) and fibrosis (0–3). As for the measurement of tumor nodule size, the mean diameter of each nodule (in millimeters) was calculated by measuring two diameters perpendicular to each other.

Liver sections were stained with Masson's trichrome for semi-quantification of collagen fiber deposition in liver tissues (82) using the Image J software, and the fibrosis percentage was used to assess the fibrosis degree. The percentages for central vein thickening, the portal area expansion, and parenchymal fibrous areas from the ten fields each were expressed as the mean ± standard error. The total fibrotic area for each sample was calculated as the mean percentage of the three zones.

4.6. Immunohistochemical analysis

For immunohistochemical analysis of p62, nuclear factor kappa B (NF-κB) and caspase-3, primary antibodies against p62 (ABclonal, USA, dilution 1:200, catalog# A11250), NF-κB (Bioss, USA, dilution 1:400, catalog# bs-20159R), and active caspase-3 (Servicebio, China, dilution 1:1000, catalog# GB11532) were used in accordance with the standard protocols. Succinctly, 5 µm thick hepatocyte sections were deparaffinized with xylene, and then rehydrated with a graded ethanol concentration (100-95-75%). The Heat Induced Epitope Retrieval approach was used to retrieve epitopes utilizing Marque triology and a pressure cooker. Heating in citrate buffer pH 6 improved antigen recovery after five changes of distilled water and phosphate buffered saline (PBS). Hydrogen peroxide (3%) was employed to inhibit endogenous peroxidase activity. Following that, the tissue was incubated for 1 hour with the matching antibodies; then with Ultra Vision horseradish peroxidase (HRP) polymer for 15 minutes. A previously prepared 3,3’Diaminobenzidine (DAB) substrate solution was used to cover each slide. The solution was composed of DAB chromogen solution with DAB buffer solution in a 1:1 ratio. The last three steps were washing with distilled water, counter-staining with hematoxylin, and dehydration with xylene and ascending grades of ethanol. The slides finally were visualized under a light microscope. Images were analyzed by Image J software to calculate stain intensity by the area fraction method.

4.7. Calculation of apoptotic index

Apoptotic index was measured as the number of apoptotic cells per 10 high power fields (83).

4.8. Autophagy assessment

There is presently no "gold standard" for assessing autophagic activity that can be used in all experimental settings. Moreover, evaluating autophagic flux in vivo or in organisms is currently one of the poorest explored topics, and optimum approaches in contrast to cell culture techniques might not exist. This research used four fundamental techniques for detecting macro-autophagy. We proposed three alternative ways for analyzing autophagy that are extensively utilized. To assess autophagic flux, the following approaches were used: immunoblotting of microtubule-associated protein 1 light chain 3 (LC3) turnover, followed by degradation of autophagic substrate p62, confirmation by gene expression analysis of LC3B and p62 via qPCR, and eventually, visualization of various autophagic bodies using TEM (84).

4.9. Transmission electron microscopy

Tissues were washed three times with 0.1 M sodium phosphate buffer + 0.1 M sucrose. Secondary fixations were achieved in 2% sodium phosphate buffered osmium tetroxide, pH 7.4, at room temperature in a rotator for 90 minutes. Tissues were washed three more times with 0.1 M sodium phosphate buffer. Samples were dehydrated by treating twice with an increasing ethyl alcohol gradient series (30%, 50%, 80%, 90%, 96%, and 100%) and were washed with acetone for 15 min twice. They were treated with an acetone-Epon mixture with increasing Epon concentrations (acetone:Epon 2:1, then 1:1, then 1:2) for 30 min each, and finally stored in Epon pure solution overnight at 4°C. In new fresh Epon solution, samples were incubated for 24 hours at 70°C for polymerization.

Ultrathin sections were obtained from blocks by ultramicrotome (PowerTome by RMC Boeckeler, USA) set to 50–100 nm thickness. Sections were rinsed on 300 mesh copper grids and post-contrasted for 10 min with 8% uranylacetate, and for 5 min with 1% lead citrate (85). After drying for ~ 15 min, ultrathin sections were observed at 160 kV using a JEOL JEM-2100 (Tokyo, Japan) at the EM Unit, Mansoura University, Egypt. Images were acquired with a Gatan-211.1404.0 system (CA, USA).

4.10. Cell cycle analysis by flow cytometry

Fresh liver tissue specimens were transported in isotonic saline and prepared according to the Tribukait method (86). Briefly, cell material for DNA analysis was placed in saline, pressed through a nylon grid (40–50 mesh counts/cm), then treated with isotonic Tris EDTA buffer (Tris 0.1 M, NaCl 0.07 M and EDTA 0.005 M, pH 7.5) containing RNase (1 mg/ml). The cell suspension was centrifuged at 1800 rpm for 10 min, and the supernatant was aspirated. Suspensions of single cell nuclei were obtained by pepsin treatment (0.5% pepsin solution, pH 2.0) for 10 min in a 37°C water bath.

The cell suspension (1 mL) was processed in a Falcon 12 × 75 mm polystyrene tube. The fluorochrome stain was prepared by adding 25 µg/mL of propidium iodide, 500 mg sodium citrate, and 0.5 ml Triton X-100 to 500 ml distilled water. Cells were suspended in 1 ml of the staining solution at 4°C for 30 min. Sample and staining solution were kept on ice, then filtered through a 30 µm nylon mesh immediately before analysis to eliminate nuclear clumps in another 5 mL tube (87,88). For determination of subG1 phase, the staining cell was kept in dark for at least 24 hours; then the samples were analyzed on an accuri™ C6 BD flow cytometer (Biosciences, San Jose, CA, USA) using accuri™ C6 software for analysis and acquisition of data.

4.11. ELISA assessment of serum galectin-1 and AFP

Serum galectin-1 (Gal-1) protein expression was assessed using a commercially available ELISA Kit (Bioassay, England, # Catalog No E2580Ra) according to the manufacturer's instructions. Serum AFP protein expression was assessed using a commercially available ELISA Kit (precheck, USA, # Catalog No. 37312C) according to the manufacturer's instructions.

4.12. qPCR

RNA was extracted from (50 mg) tissue using the GeneJET RNA purification kit (Thermo Fisher Scientific, USA) following the instructions of the manufacturer for total RNA extraction and subsequent gene expression analysis of Bcl2, caspase-3, caspase-8, vascular endothelial growth factor (VEGF), p62 and hypoxia-inducible factor-1-alpha (HIF-1α). Quantifying p62 degradation is another reliable method of autophagy assessment. p62 can bind LC3, serving as a selective substrate for autophagy. p62 degradation was found to be proportional to the rate of autophagy induction, and vice versa [49]. One significant limitation of this method is that the gene transcription level of p62 can alter based on a variety of influences [50]. To overcome this limitation, we also verified p62 protein expression using immunohistochemistry.

Genomic DNA was removed from the RNA samples by DNase 1, RNase-free kit (Thermo Fisher Scientific, USA). The purity and concentration of the extracted RNA were quantified on a Nanodrop 2000 UV–Vis spectrophotometer (Thermo Fisher Scientific, USA). RNA samples were reverse transcribed to complementary DNA (cDNA) according to the instructions of the High capacity cDNA reverse transcription kit (Thermo Fisher Scientific, USA) using the PCR Thermal cycler TCA0096 (Thermo Fisher Scientific, USA). Design of specific PCR primers was carried out using Refseq-RNA PubMed primer blast, primer3 and Netprimer websites in accordance with the gene sequence from PubMed (Entrez Gene). The designed primers wereblasted using the NCBI/BLAST tool to ensure their specificity to the target gene (Table 1). RT-PCR reactions were carried out with the aid of SensiFast™ SYBR®-No ROX kit (Bioline, USA) on the realtime PCR system StepOne Plus™, (Applied Biosystems, Thermo Fisher Scientific, USA.) according to manufacturer's instructions. The housekeeping gene used was rat glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Ct values for gene samples were normalized against GAPDH as internal control and relative gene expression was calculated following the ΔΔCt method.

Table 1

Sequences of qPCR primers. LC3B: microtubule-associated protein 1 light chain 3-B ; VEGFa: Vascular Endothelial Growth Factor a; HIF1-α : Hypoxia inducible factor-1α.
Gene of interest			Accession number	Product size	Reference
Caspase-3	Forward	5′-GGAGCAGTTTTGTGTGTGTGA-3′	NM_012922.2	191	(69,91)
Caspase-3	Reverse	5′-TGTCTCAATACCGCAGTCCA-3′	NM_012922.2	191	(69,91)
Caspase-8	Forward	5′-CCTTTCTCCTCCCTCTGACCTC-3′	NM_022277.1	193	(69)
Caspase-8	Reverse	5′-GTAACCTGTCGCCGAGTCCC-3′	NM_022277.1	193	(69)
Bcl-2	Forward	5′-AGGATAACGGAGGCTGGGATG-3′	NM_016993.1	179	(69)
Bcl-2	Reverse	5′-TATTTGTTTGGGGCAGGTCT-3′	NM_016993.1	179	(69)
LC3b	Forward	GCTCCATGCAGGTAGCAGGAA	NM_022867.2	102	(92)
LC3b	Reverse	AGCTCTGAAGGCAACAGCAACA	NM_022867.2	102	(92)
P62	Forward	5`-GGAGCAGTTTTGTGTGTGTGA − 3	NM_175843.4	100	-
P62	Reverse	5’- GGGGGCACAGTGAATGATAAG − 3’	NM_175843.4	100	-
VEGFa	Forward	5’ TTTTGCTTCCTATTCCCCTCT – 3’	NM_001110333.2	150	-
VEGFa	Reverse	5’ – TCTCTCTCTCTCTCTCTTCCTTGA – 3’	NM_001110333.2	150	-
GAPDH	Forward	5’ TCCCATTCTTCCACCTTTGA − 3’	NM_017008.4	109	-
GAPDH	Reverse	5’ – CCACCACCCTGTTGCTGTAG – 3’	NM_017008.4	109	-
HIF1α	Forward	5’ – AGCAACTAGGAACCCGAACC – 3’	NM_024359.2	112	-
HIF1α	Reverse	5’ – AGAGAAAGGGGCAAGTCCAG – 3’	NM_024359.2	112	-

Table 2

Cell cycle analysis results (mean % of cells ± S.E.M).
	N	HCC	SF	CQ	SV	SF + CQ	SF + SV
SubG1	1.65 ± 0.21	2.05 ± 0.49	15.71 ± 0.27	23.08 ± 0.83	28.35 ± 1.20	40.88 ± 0.85	40.38 ± 0.95
G0/1	95.63 ± 0.69	70.30 ± 0.35	76.85 ± 0.45	67.55 ± 0.92	68.20 ± 1.05	48 ± 0.82	50.65 ± 1.66
S	5.78 ± 0.11	20.28 ± 0.37	6.62 ± 0.05	6.80 ± 0.90	4.73 ± 0.28	12.13 ± 0.83	10.43 ± 0.91
G2M	5.20 ± 0.23	17.10 ± 0.36	6.82 ± 0.30	6.05 ± 0.23	5.25 ± 0.85	0.50 ± 0.15	2 ± 0.58
Percentage of cells of Normal control (N), hepatocellular carcinoma (HCC), and HCC treated with sorafenib (SF), sorafenib + simvastatin (SF + SV), sorafenib + hydroxychloroquine (SF + CQ), hydroxychloroquine (CQ), or simvastatin (SV) groups in each phase.

4.13. Western blotting of LC3I/II

Liver tissues were first pre-washed in PBS and homogenized in 5 mL pre-cooled lysis buffer (pH 7.4: 10 mM Tris-Base, 25 mM EDTA, 25 mM EGTA, 50 mM NaCl, 1% NP-40, 1% Triton X-100), supplemented with 1:1000 protease/phosphatase inhibitors (Sigma, MO, USA). Initially, determination of the total proteins was carried out colorimetrically using the Pierce™ 660 nm assay kit (Thermo Scientific, USA). For each sample, an equal amount (30 µg) of protein was mixed, boiled with sodium dodecyl sulfate (SDS)-loading buffer at 96°C for 5 min, separated by an electrophoresis unit (Cleaver Scientific Ltd, UK), and then transferred using semi-dry Electroblotter (Bio-Rad, CA, USA). The proteins on PVDF membranes were blocked with bovine serum albumin (5% in Tris-buffered saline-Tween-20) at RT, washed, and incubated with Rabbit anti-rat antibodies against LC3-II/I (1:1000, #4108, Cell Signaling Technology, MA, USA), and β-actin (1:2500, # A5060, Sigma, St. Louis, MO, USA) for 18–20 h at 4°C. The membranes were washed again, incubated at room temperature with an HRP-conjugated goat anti-rabbit immunuglobulin as a secondary antibody (1:1000, Dako, Glostrup, Denmark, # Catalog No P0448) for 1 hour (89). The Western Lightning Plus ECL Chemiluminescence Reagents (Perkin Elmer, Waltham, MA) were mixed 1:1 and applied to the blots for 1 min, and the band signals were then captured using the Chemi-Doc imager (Bio-Rad, Hercules, CA, USA), with their intensities analyzed and normalized to corresponding β-actin bands, as an internal control protein (90).

4.14. Statistical analysis

All results were expressed as mean ± S.E. Experimental results were examined using one-way analysis of variance followed by Tukey's post-hoc test. Necroinflammatory and immunohistochemical scoring results were analyzed using the non-parametric Kruskal-Wallis test followed by Dunn’s post-hoc test. Statistical tests were performed by GraphPad Prism 2019 v8.0.2.263 software (IBM Corp., Armonk, NY, USA). Statistical significance was considered at p < 0.05.

HCC, hepatocellular carcinoma

PI3K, phosphoinositide 3-kinase

JAK-STAT, Janus kinase – signal transducer and activator of transcription

SF, Sorafenib group

SV, simvastatin group

CQ, hydroxychloroquine group

SF+CQ sorafenib with hydroxychloroquine group

SF+SV, sorafenib with simvastatin group

NIH, National Institutes of Health

DEN, diethlynitrosamine

TAA, thioacetamide

AFP, alpha feto protein

qPCR, quantitative polymerase chain reaction

TEM, transmission electron microscopy

H&E, hematoxylin and eosin

NF-κB, nuclear factor kappa B

HIF1, hypoxia-inducible factor 1

VEGF, vascular endothelial growth factor

BafA1. Bafilomycin

Ethics approval and consent to participate:

This research was performed in accordance with the Declaration of Helsinki, and was approved by ethics committee of faculty of pharmacy, Mansoura university on 23/4/2022 (approval code #2022-71).

Consent for publication:

Not applicable

Availability of data and materials

Not applicable

Competing interests:

No potential conflicts of interest were disclosed by all the authors.

Funding: This work was self-funded by the authors and was helped by the institution resources.

Authors contributions

YE: Conceptualization, Methodology, Resources, Investigation, Software, Data curation, Formal analysis, Writing- Original draft, Visualization. MN: Conceptualization, Methodology, Validation, Writing- Reviewing and Editing. AE: Supervision, Administration.

Acknowledgements

The authors would like to thank Dr. Mahmoud Abd-Elfatah, for helping in TEM imaging and interpretation, and Eva pharma for supplementing simvastatin.

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Autophagy Modulation Attenuates Sorafenib Resistance In HCC Induced In Rats

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Abstract

Figures

1. Introduction

2. Results

2.1 Sorafenib combination with autophagic modulators improved hepatic function and suppressed HCC progression more effectively

2.1.1 H&E histopathological examination, nodule size and necroinflammatory scoring

2.1.2 Masson’s trichrome staining and fibrosis area fraction

2.1.3 Liver gross examination, Liver index and Overall survival:

2.1.4 TEM histopathological micrographs:

2.1.5 Serum AFP and routine hepatic function tests:

2.2 Success of autophagic modulation by hydroxychloroquine and simvastatin

2.2.1 Westernblotting of LC3

2.2.2 Immunohistochemistry of P62 and its relative gene expression using qPCR.

2.2.3 TEM micrographs of ultra-thin liver sections of autophagy related cellular structures

2.3 Autophagic modulators combination with Sorafenib sensitized HCC to sorafenib

2.3.1 Measuring Serum Galectin-1 level by ELISA

2.3.2 Relative gene expression of HIF-1α and VEGFa by qPCR

2.3.3 Immunohistochemistry of Nuclear factor kabba B (NF-κB)

2.4 Apoptosis induction and cell cycle arrest

2.4.1 Hydroxychloroquine and Simvastatin synergically increased Caspase-3 Immunuhistochemistry and its apoptotic index with Sorafenib

2.4.2 Hydroxychloroquine and Simvastatin synergically increased apoptosis on the transcriptional level

3. Discussion

4. Methods

4.1. Establishment of the HCC model

4.2. Experimental design

4.3. Sample collection

4.4. Biochemical analysis

4.5. Histopathological examination of hepatic tissues

4.6. Immunohistochemical analysis

4.7. Calculation of apoptotic index

4.8. Autophagy assessment

4.9. Transmission electron microscopy

4.10. Cell cycle analysis by flow cytometry

Abbreviations

Declarations

References

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